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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
• The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

• Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
• Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
• Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
• Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
• Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
• Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
• Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
• Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
• Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
• Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
• Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

• In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
• In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
• In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
• In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
• In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

• Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
• Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
• Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
• Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
• Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

• Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
• Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
• Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
• Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

• To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
• To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
• To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
• To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
• To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

• Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
• Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
• Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
• Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
• Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
• Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

• Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
• Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
• Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
• Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
• Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
• Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

• Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
• Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
• Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
• Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
• Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
• Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

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Researcher, Academic Writer, Web developer

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What is a good example of a conceptual framework?

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18 April 2023

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• The importance of a conceptual framework

The main purpose of a conceptual framework is to improve the quality of a research study. A conceptual framework achieves this by identifying important information about the topic and providing a clear roadmap for researchers to study it.

Through the process of developing this information, researchers will be able to improve the quality of their studies in a few key ways.

Clarify research goals and objectives

A conceptual framework helps researchers create a clear research goal. Research projects often become vague and lose their focus, which makes them less useful. However, a well-designed conceptual framework helps researchers maintain focus. It reinforces the project’s scope, ensuring it stays on track and produces meaningful results.

Provide a theoretical basis for the study

Forming a hypothesis requires knowledge of the key variables and their relationship to each other. Researchers need to identify these variables early on to create a conceptual framework. This ensures researchers have developed a strong understanding of the topic before finalizing the study design. It also helps them select the most appropriate research and analysis methods.

Guide the research design

As they develop their conceptual framework, researchers often uncover information that can help them further refine their work.

Here are some examples:

Confounding variables they hadn’t previously considered

Sources of bias they will have to take into account when designing the project

Whether or not the information they were going to study has already been covered—this allows them to pivot to a more meaningful goal that brings new and relevant information to their field

• Steps to develop a conceptual framework

There are four major steps researchers will follow to develop a conceptual framework. Each step will be described in detail in the sections that follow. You’ll also find examples of how each might be applied in a range of fields.

Step 1: Choose the research question

The first step in creating a conceptual framework is choosing a research question . The goal of this step is to create a question that’s specific and focused.

By developing a clear question, researchers can more easily identify the variables they will need to account for and keep their research focused. Without it, the next steps will be more difficult and less effective.

Here are some examples of good research questions in a few common fields:

Natural sciences: How does exposure to ultraviolet radiation affect the growth rate of a particular type of algae?

Health sciences: What is the effectiveness of cognitive-behavioral therapy for treating depression in adolescents?

Business: What factors contribute to the success of small businesses in a particular industry?

Education: How does implementing technology in the classroom impact student learning outcomes?

Step 2: Select the independent and dependent variables

Once the research question has been chosen, it’s time to identify the dependent and independent variables .

The independent variable is the variable researchers think will affect the dependent variable . Without this information, researchers cannot develop a meaningful hypothesis or design a way to test it.

The dependent and independent variables for our example questions above are:

Natural sciences

Independent variable: exposure to ultraviolet radiation

Dependent variable: the growth rate of a particular type of algae

Health sciences

Independent variable: cognitive-behavioral therapy

Dependent variable: depression in adolescents

Independent variables: factors contributing to the business’s success

Dependent variable: sales, return on investment (ROI), or another concrete metric

Independent variable: implementation of technology in the classroom

Dependent variable: student learning outcomes, such as test scores, GPAs, or exam results

Step 3: Visualize the cause-and-effect relationship

This step is where researchers actually develop their hypothesis. They will predict how the independent variable will impact the dependent variable based on their knowledge of the field and their intuition.

With a hypothesis formed, researchers can more accurately determine what data to collect and how to analyze it. They will then visualize their hypothesis by creating a diagram. This visualization will serve as a framework to help guide their research.

The diagrams for our examples might be used as follows:

Natural sciences : how exposure to radiation affects the biological processes in the algae that contribute to its growth rate

Health sciences : how different aspects of cognitive behavioral therapy can affect how patients experience symptoms of depression

Business : how factors such as market demand, managerial expertise, and financial resources influence a business’s success

Education : how different types of technology interact with different aspects of the learning process and alter student learning outcomes

Step 4: Identify other influencing variables

The independent and dependent variables are only part of the equation. Moderating, mediating, and control variables are also important parts of a well-designed study. These variables can impact the relationship between the two main variables and must be accounted for.

A moderating variable is one that can change how the independent variable affects the dependent variable. A mediating variable explains the relationship between the two. Control variables are kept the same to eliminate their impact on the results. Examples of each are given below:

Moderating variable: water temperature (might impact how algae respond to radiation exposure)

Mediating variable: chlorophyll production (might explain how radiation exposure affects algae growth rate)

Control variable: nutrient levels in the water

Moderating variable: the severity of depression symptoms at baseline might impact how effective the therapy is for different adolescents

Mediating variable: social support might explain how cognitive-behavioral therapy leads to improvements in depression

Control variable: other forms of treatment received before or during the study

Moderating variable: the size of the business (might impact how different factors contribute to market share, sales, ROI, and other key success metrics)

Mediating variable: customer satisfaction (might explain how different factors impact business success)

Control variable: industry competition

Moderating variable: student age (might impact how effective technology is for different students)

Mediating variable: teacher training (might explain how technology leads to improvements in learning outcomes)

Control variable: student learning style

• Conceptual versus theoretical frameworks

Although they sound similar, conceptual and theoretical frameworks have different goals and are used in different contexts. Understanding which to use will help researchers craft better studies.

Conceptual frameworks describe a broad overview of the subject and outline key concepts, variables, and the relationships between them. They provide structure to studies that are more exploratory in nature, where the relationships between the variables are still being established. They are particularly helpful in studies that are complex or interdisciplinary because they help researchers better organize the factors involved in the study.

Theoretical frameworks, on the other hand, are used when the research question is more clearly defined and there’s an existing body of work to draw upon. They define the relationships between the variables and help researchers predict outcomes. They are particularly helpful when researchers want to refine the existing body of knowledge rather than establish it.

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What is a Conceptual Framework?

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format.

Updated on August 28, 2023

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research.  Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results. 

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them. 

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc. 

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same. 

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research. 

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

• Selecting the appropriate research questions
• Defining the process variables (dependent, independent, and others)
• Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables. 

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research. 

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

• Provides the data for molecular docking studies to identify suitable target proteins
• Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration. 

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research. 

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Introduction

Understanding conceptual frameworks

Selecting and developing your framework, variables in a conceptual framework.

• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Conceptual framework: Definition and theory

Theoretical and conceptual frameworks ultimately go hand in hand, but while there is significant overlap with theoretical perspectives and theoretical frameworks, understanding the essential differences is important when designing your research project.

Let's explore the idea of a conceptual framework, provide a few common examples, and discuss how to choose a framework for your study. Keep in mind that a conceptual framework will differ from a theoretical framework and that we will explore these differences in the next section.

In this section, we'll delve into the intricacies of conceptual frameworks and their role in qualitative research . They are essentially the scaffolding on which you hang your research questions and analysis . They define the concepts that you'll study and articulate the relationships among them.

Developing conceptual frameworks in research

At the most basic level, a conceptual framework is a visual or written product that explains, either graphically or in narrative form, the main things to be studied, the key factors, variables, or constructs, and any presumed relationships among them. It acts as a road map guiding the course of your research, directing what will be studied, and helping to organize and analyze the data.

The purpose of a conceptual framework

A conceptual framework serves multiple functions in a research project. It helps in clarifying the research problem and purpose, assists in refining the research questions, and guides the data collection and analysis process. It's the tool that ties all aspects of the study together, offering a coherent perspective for the researcher and readers to understand the research more holistically.

Relation between theoretical perspectives and conceptual frameworks

Theoretical perspectives offer overarching philosophies and assumptions that guide the research process, while conceptual frameworks are the specific devices that are derived from these perspectives to operationalize the study. If a theoretical perspective is the broad philosophical underpinning, a conceptual framework is a pragmatic approach that puts that philosophy into practice in the context of the study.

For instance, if you're working from a feminist theoretical perspective, your conceptual framework might involve specific constructs like gender roles, power dynamics , and societal norms, as well as the relationships between these constructs. The conceptual framework would be the lens through which you examine and interpret your data, guided by your theoretical perspective.

Critical theory

Critical theory is a theoretical perspective that seeks to confront social, historical, and ideological forces and structures that produce and constrain social problems. The corresponding conceptual framework might focus on constructs like power relations, historical context, and societal structures. For instance, a study on income inequality might have a conceptual framework involving constructs of socioeconomic status, institutional policies, and the distribution of resources.

Feminist theory

Feminist theory emphasizes the societal roles of gender and power relationships. A conceptual framework derived from this theory might involve constructs like gender roles, power dynamics, and societal norms. In a study about gender representation in media, a feminist conceptual framework could involve constructs such as stereotyping, representation, and societal expectations of gender.

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Choosing and developing your conceptual framework is a pivotal process in your research design. This framework will help guide your study, inform your methodology , and shape your analysis .

Factors to consider when choosing a framework

Your conceptual framework should be derived from and align with your chosen theoretical perspective , but there are other considerations as well. It should resonate with your research question , problem, or purpose and be applicable to the specific context or population you are studying. You should also consider the feasibility of operationalizing the constructs in your framework.

When selecting a conceptual framework, consider the following questions:

1. How does this framework relate to my research topic? 2. Can I use this framework to effectively address my research question(s)? 3. Does this framework resonate with the population and context I'm studying? 4. Can the constructs in this framework be feasibly operationalized in my study?

Steps in developing a conceptual framework

Developing your conceptual framework involves a few key steps:

1. Identify key constructs: Based on your theoretical perspective and research question(s) , what are the main constructs or variables that you need to explore in your study? 2. Clarify relationships among constructs: How do these constructs relate to each other? Are there presumed causal relationships, correlations, or other types of associations? 3. Define each construct: Clearly define what each construct means in the context of your study. This might also involve operationalizing each construct or defining the indicators you will use to measure or identify each construct. 4. Create a visual representation : It is often extremely helpful to create a visual representation of your conceptual framework to illustrate the constructs and their relationships. Map out the relationships among constructs to develop a holistic understanding of what you want to study.

Remember, your conceptual framework is not set in stone. You can start creating your conceptual framework based on your literature review and your own critical reflections. As you proceed with your study, you might need to refine or adapt your conceptual framework based on what you're learning from your data. Developing a robust framework is an iterative process that requires critical thinking, creativity, and flexibility.

A strong conceptual framework includes variables that refer to the constructs or characteristics that are being studied. They are the building blocks of your research study. It might be helpful to think about how the variables in your conceptual framework could be categorized as independent and dependent variables, which respectively influence and are influenced within the research study.

Independent variables and dependent variables

An independent variable is the characteristic or condition that is manipulated or selected by the researcher to determine its effect on the dependent variable. For example, in a study exploring the impact of classroom size on student engagement, classroom size would be the independent variable.

The dependent variable is the main outcome that the researcher is interested in studying or explaining. In the example given above, student engagement would be the dependent variable, as it's the outcome being observed for any changes in response to the independent variable (classroom size). In essence, defining these variables can help you identify the cause-and-effect relationships in your study. While it might be difficult to know beforehand exactly which variables will be important and how they relate to one another, this is a helpful thought exercise to flesh out potential relationships among variables you may want to study.

Relationships among variables

Within a conceptual framework, the dependent and independent variables are listed in addition to their proposed relationships to each other. The ways in which these variables influence one another form the crux of the propositions or assumptions that guide your research.

In a conceptual framework based on the theoretical perspective of constructivism, for instance, the independent variable might be a teaching method (as constructivists would argue that methods of instruction can shape learning), and the dependent variable could be the depth of student understanding. The proposed relationship between these variables might be that student-centered teaching methods lead to a deeper understanding, which would guide the data collection and analysis such that this proposition could be explored.

However, it is important to note that the terminology of independent and dependent variables is more typical of quantitative research , in which independent and dependent variables are operationalized in hypotheses that will be tested based on pre-established theory. In qualitative research , the relationships between variables are more fluid and open-ended because the focus is often more on understanding the phenomenon as a whole and building a contextualized understanding of the research problem. This can involve including new or unexpected variables and interrelationships that emerge during the study, thus extending previous theory or understanding that didn’t initially predict these relationships.

Thus, in your conceptual framework, rather than solely focusing on identifying independent and dependent variables, consider how various factors interact and influence one another within the context of your study. Your conceptual framework should provide a holistic picture of the complexity of the phenomenon you are studying.

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How to Use a Conceptual Framework for Better Research

A conceptual framework in research is not just a tool but a vital roadmap that guides the entire research process. It integrates various theories, assumptions, and beliefs to provide a structured approach to research. By defining a conceptual framework, researchers can focus their inquiries and clarify their hypotheses, leading to more effective and meaningful research outcomes.

What is a Conceptual Framework?

A conceptual framework is essentially an analytical tool that combines concepts and sets them within an appropriate theoretical structure. It serves as a lens through which researchers view the complexities of the real world. The importance of a conceptual framework lies in its ability to serve as a guide, helping researchers to not only visualize but also systematically approach their study.

Key Components and to be Analyzed During Research

• Theories: These are the underlying principles that guide the hypotheses and assumptions of the research.
• Assumptions: These are the accepted truths that are not tested within the scope of the research but are essential for framing the study.
• Beliefs: These often reflect the subjective viewpoints that may influence the interpretation of data.
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Together, these components help to define the conceptual framework that directs the research towards its ultimate goal. This structured approach not only improves clarity but also enhances the validity and reliability of the research outcomes. By using a conceptual framework, researchers can avoid common pitfalls and focus on essential variables and relationships.

For practical examples and to see how different frameworks can be applied in various research scenarios, you can Explore Conceptual Framework Examples .

Different Types of Conceptual Frameworks Used in Research

Understanding the various types of conceptual frameworks is crucial for researchers aiming to align their studies with the most effective structure. Conceptual frameworks in research vary primarily between theoretical and operational frameworks, each serving distinct purposes and suiting different research methodologies.

Theoretical vs Operational Frameworks

Theoretical frameworks are built upon existing theories and literature, providing a broad and abstract understanding of the research topic. They help in forming the basis of the study by linking the research to already established scholarly works. On the other hand, operational frameworks are more practical, focusing on how the study’s theories will be tested through specific procedures and variables.

• Theoretical frameworks are ideal for exploratory studies and can help in understanding complex phenomena.
• Operational frameworks suit studies requiring precise measurement and data analysis.

Choosing the Right Framework

Selecting the appropriate conceptual framework is pivotal for the success of a research project. It involves matching the research questions with the framework that best addresses the methodological needs of the study. For instance, a theoretical framework might be chosen for studies that aim to generate new theories, while an operational framework would be better suited for testing specific hypotheses.

Benefits of choosing the right framework include enhanced clarity, better alignment with research goals, and improved validity of research outcomes. Tools like Table Chart Maker can be instrumental in visually comparing the strengths and weaknesses of different frameworks, aiding in this crucial decision-making process.

Real-World Examples of Conceptual Frameworks in Research

Understanding the practical application of conceptual frameworks in research can significantly enhance the clarity and effectiveness of your studies. Here, we explore several real-world case studies that demonstrate the pivotal role of conceptual frameworks in achieving robust research conclusions.

• Healthcare Research: In a study examining the impact of lifestyle choices on chronic diseases, researchers used a conceptual framework to link dietary habits, exercise, and genetic predispositions. This framework helped in identifying key variables and their interrelations, leading to more targeted interventions.
• Educational Development: Educational theorists often employ conceptual frameworks to explore the dynamics between teaching methods and student learning outcomes. One notable study mapped out the influences of digital tools on learning engagement, providing insights that shaped educational policies.
• Environmental Policy: Conceptual frameworks have been crucial in environmental research, particularly in studies on climate change adaptation. By framing the relationships between human activity, ecological changes, and policy responses, researchers have been able to propose more effective sustainability strategies.

Adapting conceptual frameworks based on evolving research data is also critical. As new information becomes available, it’s essential to revisit and adjust the framework to maintain its relevance and accuracy, ensuring that the research remains aligned with real-world conditions.

For those looking to visualize and better comprehend their research frameworks, Graphic Organizers for Conceptual Frameworks can be an invaluable tool. These organizers help in structuring and presenting research findings clearly, enhancing both the process and the presentation of your research.

Step-by-Step Guide to Creating Your Own Conceptual Framework

Creating a conceptual framework is a critical step in structuring your research to ensure clarity and focus. This guide will walk you through the process of building a robust framework, from identifying key concepts to refining your approach as your research evolves.

Building Blocks of a Conceptual Framework

• Identify and Define Main Concepts and Variables: Start by clearly identifying the main concepts, variables, and their relationships that will form the basis of your research. This could include defining key terms and establishing the scope of your study.
• Develop a Hypothesis or Primary Research Question: Formulate a central hypothesis or question that guides the direction of your research. This will serve as the foundation upon which your conceptual framework is built.
• Link Theories and Concepts Logically: Connect your identified concepts and variables with existing theories to create a coherent structure. This logical linking helps in forming a strong theoretical base for your research.

Visualizing and Refining Your Framework

Using visual tools can significantly enhance the clarity and effectiveness of your conceptual framework. Decision Tree Templates for Conceptual Frameworks can be particularly useful in mapping out the relationships between variables and hypotheses.

Map Your Framework: Utilize tools like Creately’s visual canvas to diagram your framework. This visual representation helps in identifying gaps or overlaps in your framework and provides a clear overview of your research structure.

Analyze and Refine: As your research progresses, continuously evaluate and refine your framework. Adjustments may be necessary as new data comes to light or as initial assumptions are challenged.

By following these steps, you can ensure that your conceptual framework is not only well-defined but also adaptable to the changing dynamics of your research.

Practical Tips for Utilizing Conceptual Frameworks in Research

Effectively utilizing a conceptual framework in research not only streamlines the process but also enhances the clarity and coherence of your findings. Here are some practical tips to maximize the use of conceptual frameworks in your research endeavors.

• Setting Clear Research Goals: Begin by defining precise objectives that are aligned with your research questions. This clarity will guide your entire research process, ensuring that every step you take is purposeful and directly contributes to your overall study aims. \
• Maintaining Focus and Coherence: Throughout the research, consistently refer back to your conceptual framework to maintain focus. This will help in keeping your research aligned with the initial goals and prevent deviations that could dilute the effectiveness of your findings.
• Data Analysis and Interpretation: Use your conceptual framework as a lens through which to view and interpret data. This approach ensures that the data analysis is not only systematic but also meaningful in the context of your research objectives. For more insights, explore Research Data Analysis Methods .
• Presenting Research Findings: When it comes time to present your findings, structure your presentation around the conceptual framework . This will help your audience understand the logical flow of your research and how each part contributes to the whole.
• Avoiding Common Pitfalls: Be vigilant about common errors such as overcomplicating the framework or misaligning the research methods with the framework’s structure. Keeping it simple and aligned ensures that the framework effectively supports your research.

By adhering to these tips and utilizing tools like 7 Essential Visual Tools for Social Work Assessment , researchers can ensure that their conceptual frameworks are not only robust but also practically applicable in their studies.

How Creately Enhances the Creation and Use of Conceptual Frameworks

Creating a robust conceptual framework is pivotal for effective research, and Creately’s suite of visual tools offers unparalleled support in this endeavor. By leveraging Creately’s features, researchers can visualize, organize, and analyze their research frameworks more efficiently.

• Visual Mapping of Research Plans: Creately’s infinite visual canvas allows researchers to map out their entire research plan visually. This helps in understanding the complex relationships between different research variables and theories, enhancing the clarity and effectiveness of the research process.
• Brainstorming with Mind Maps: Using Mind Mapping Software , researchers can generate and organize ideas dynamically. Creately’s intelligent formatting helps in brainstorming sessions, making it easier to explore multiple topics or delve deeply into specific concepts.
• Centralized Data Management: Creately enables the importation of data from multiple sources, which can be integrated into the visual research framework. This centralization aids in maintaining a cohesive and comprehensive overview of all research elements, ensuring that no critical information is overlooked.
• Communication and Collaboration: The platform supports real-time collaboration, allowing teams to work together seamlessly, regardless of their physical location. This feature is crucial for research teams spread across different geographies, facilitating effective communication and iterative feedback throughout the research process.

Moreover, the ability t Explore Conceptual Framework Examples directly within Creately inspires researchers by providing practical templates and examples that can be customized to suit specific research needs. This not only saves time but also enhances the quality of the conceptual framework developed.

In conclusion, Creately’s tools for creating and managing conceptual frameworks are indispensable for researchers aiming to achieve clear, structured, and impactful research outcomes.

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Chiraag George is a communication specialist here at Creately. He is a marketing junkie that is fascinated by how brands occupy consumer mind space. A lover of all things tech, he writes a lot about the intersection of technology, branding and culture at large.

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Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

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The Significance of Conceptual Framework in Research

Craft a strong conceptual framework in research with our comprehensive guide. Learn the essential steps to create an effective framework!

Research is a systematic process of inquiry that involves gathering and analyzing information to answer questions and solve problems. Conducting research is an essential activity in various fields, including business, education, healthcare, and social sciences. In research, a conceptual framework is a critical element that guides the study and helps to organize and interpret the collected data. In this article, we will discuss the definition of a conceptual framework, its purpose and importance in research, and the steps involved in developing a conceptual framework.

What is Conceptual Framework

A conceptual framework is a structure that provides a theoretical or conceptual foundation for research, allowing researchers to examine and analyze complex phenomena. It is a tool that researchers use to guide the research process by defining the key concepts, ideas, and theories that underpin their study. The conceptual framework can help to identify the research questions, the variables that will be studied, and the relationships between them. It can also provide a way to visualize the research problem, clarify the research methodology, and explain the research findings.

Purpose and Importance of a Conceptual Framework in Research

The purpose of a conceptual framework in research.

The purpose of a conceptual framework in research is to provide a clear and concise understanding of the key concepts, variables, relationships, and assumptions that underlie a research study. Specifically, a conceptual framework serves several purposes:

Helps to clarify research questions: A well-developed conceptual framework helps to define the research problem and the specific research questions that the study seeks to answer.

Provides a theoretical basis for the study: The conceptual framework provides a theoretical foundation for the study, drawing on existing theories and concepts to guide the research process.

Guides data collection and analysis: The conceptual framework helps to identify the relevant variables and relationships that need to be studied, and guides the collection and analysis of data.

Ensures research validity and reliability: The conceptual framework helps to ensure that the study is focused, relevant, and valid, and that the data collected is reliable.

Helps to make conclusions and recommendations: The conceptual framework provides a basis for making conclusions and recommendations based on the collected data, contributing to the existing body of knowledge in the field.

The Importance of a Conceptual Framework in Research

Provide a basis for research design: The conceptual framework provides a blueprint for the research study, outlining the key concepts, variables, and relationships between them. This helps researchers to design a study that is logical, structured, and focused.

Guide data collection and analysis: The conceptual framework helps to identify the variables and relationships that will be examined in the study. This helps researchers to collect and analyze data that is relevant to the research question and hypothesis.

Ensure validity and reliability: A well-developed conceptual framework helps to ensure that the research is valid and reliable. It ensures that the research is measuring what it intends to measure and that the results are consistent over time.

Facilitate communication: The conceptual framework provides a common language and understanding for researchers, facilitating communication and collaboration among team members.

Identify gaps in existing knowledge: The conceptual framework helps to identify gaps in existing knowledge and to develop new insights and theories.

A well-developed conceptual framework is crucial to the success of a research study. It provides a clear and logical structure for the study, helps to ensure validity and reliability, and facilitates communication and collaboration among researchers.

Steps to Developing a Conceptual Framework

Developing a conceptual framework involves several steps. These steps are outlined below:

1. Choose a research question

The first step in developing a conceptual framework is to identify the research question. This question should be clear, specific, and relevant to the study. It should be formulated based on a review of the existing literature and the identification of gaps in knowledge or areas where further research is needed. Read our Research Question article to learn more about it. 

2. Identify the main variables

The next step is to identify the main variables that will be studied. These variables should be measurable, observable, and relevant to the research question. The independent variable is the variable that is manipulated or controlled in the study, while the dependent variable is the variable that is measured or observed. The independent variable is usually the cause, while the dependent variable is the effect. Read our Research Variables content to understand it better.

3. Visualize the cause-and-effect relationship

The next step is to visualize the cause-and-effect relationship between the independent and dependent variables. This can be done by creating a diagram or a flowchart that illustrates the relationship between the variables. The diagram or flowchart should clearly show the direction of the relationship, whether it is positive or negative, and the strength of the relationship.

4. Identify other influencing variables

The researcher should also identify other variables that may influence the relationship between the main variables. These variables can be included in the conceptual framework, they are known as confounding variables and should be identified and controlled in the study.

5. Include moderating and mediating variables

Moderating and mediating variables should be included in the conceptual framework if they are relevant to the study. Moderating variables affect the strength or direction of the relationship between the main variables while mediating variables explain the relationship between the main variables.

6. Consider control variables

Control variables are variables that are held constant in the study to ensure that the results are valid and reliable. These variables should be included in the conceptual framework to ensure that the study is well-controlled.

7. Revise and refine the conceptual framework

Once the conceptual framework has been developed, the researcher should revise and refine it to ensure that it is clear, concise, and comprehensive. The conceptual framework should be reviewed to ensure that it accurately represents the research question and the variables involved in the study.

Moderating Variables

Moderating variables are variables that can modify or change the strength or direction of the relationship between the independent and dependent variables. These variables can be included in the conceptual framework to help explain the results of the study. For example, in a study on the effects of exercise on weight loss, age, and gender may be moderating variables that can affect the strength of the relationship between exercise and weight loss.

Mediating Variables

Mediating variables are variables that help to explain the relationship between the independent and dependent variables. These variables may be included in the conceptual framework to help identify the mechanisms through which the independent variable affects the dependent variable. For example, in a study on the effects of exercise on weight loss, metabolism, and calorie intake may be mediating variables that help to explain how exercise affects weight loss.

Moderator vs Mediator

It is essential to understand the difference between a moderator and a mediator in research. Here is a table that highlights the differences between moderators and mediators in a theoretical framework:

Control Variables

Control variables are factors that are held constant or unchanged in a study or experiment. In a conceptual framework, control variables refer to the variables that are kept constant or held fixed during the study to ensure that the effect of other independent variables on the dependent variable is not confounded or influenced by any other factor.  For example, in a study on the effects of exercise on weight loss, the type of exercise, duration of exercise, and frequency of exercise may be control variables that are held constant to ensure that the results are not affected by these factors.

The Final Analysis

A conceptual framework is a critical element in research that provides a theoretical basis for the study and guides the research process. Developing a conceptual framework involves several steps, including choosing a research question, selecting independent and dependent variables, visualizing cause-and-effect relationships, identifying other influencing variables, including moderating and mediating variables, and controlling variables. It also provides a basis for making conclusions and recommendations based on the collected data. Researchers should pay close attention to developing a robust conceptual framework to ensure that their research is of high quality and contributes to existing knowledge.

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

• Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

23 Comments

Thank you for giving a valuable lesson

good thanks!

VERY INSIGHTFUL

thanks for given very interested understand about both theoritical and conceptual framework

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

good, fantastic

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Thanks for shedding light on these two t opics. Much clearer in my head now.

Simple and clear!

The differences between the two topics was well explained, thank you very much!

Thank you great insight

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Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

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I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

I’m well inform about these two concepts in research. Thanks

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very clear and useful. information important at start of research!!

Wow, great information, clear and concise review of the differences between theoretical and conceptual frameworks. Thank you! keep up the good work.

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• What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Table of contents

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualise your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

• The expected cause, ‘hours of study’, is the independent variable (the predictor, or explanatory variable)
• The expected effect, ‘exam score’, is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (‘hours of study’).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualising your expected cause-and-effect relationship.

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the ‘effect’ component of the cause-and-effect relationship.

Let’s add the moderator ‘IQ’. Here, a student’s IQ level can change the effect that the variable ‘hours of study’ has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our ‘IQ’ moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the ‘IQ’ moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

• A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
• A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly,  control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Swaen, B. & George, T. (2024, March 18). What Is a Conceptual Framework? | Tips & Examples. Scribbr. Retrieved 2 September 2024, from https://www.scribbr.co.uk/research-methods/conceptual-frameworks/

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Conceptual Research: Definition, Framework, Example and Advantages

Conceptual Research: Definition

Conceptual research is defined as a methodology wherein research is conducted by observing and analyzing already present information on a given topic. Conceptual research doesn’t involve conducting any practical experiments. It is related to abstract concepts or ideas. Philosophers have long used conceptual research to develop new theories or interpret existing theories in a different light.

For example, Copernicus used conceptual research to come up with the concepts of stellar constellations based on his observations of the universe. Down the line, Galileo simplified Copernicus’s research by making his own conceptual observations which gave rise to more experimental research and confirmed the predictions made at that time.

The most famous example of conceptual research is Sir Issac Newton. He observed his surroundings to conceptualize and develop theories about gravitation and motion.

Einstein is widely known and appreciated for his work on conceptual research. Although his theories were based on conceptual observations, Einstein also proposed experiments to come up with theories to test the conceptual research.

Nowadays, conceptual research is used to answer business questions and solve real-world problems. Researchers use analytical research tools called conceptual frameworks to make conceptual distinctions and organize ideas required for research purposes.

Conceptual Research Framework

Conceptual research framework constitutes of a researcher’s combination of previous research and associated work and explains the occurring phenomenon. It systematically explains the actions needed in the course of the research study based on the knowledge obtained from other ongoing research and other researchers’ points of view on the subject matter.

Here is a stepwise guide on how to create the conceptual research framework:

01. Choose the topic for research

Before you start working on collecting any research material, you should have decided on your topic for research. It is important that the topic is selected beforehand and should be within your field of specialization.

02. Collect relevant literature

Once you have narrowed down a topic, it is time to collect relevant information about it. This is an important step, and much of your research is dependent on this particular step, as conceptual research is mostly based on information obtained from previous research. Here collecting relevant literature and information is the key to successfully completing research.

The material that you should preferably use is scientific journals , research papers published by well-known scientists , and similar material. There is a lot of information available on the internet and in public libraries as well. All the information that you find on the internet may not be relevant or true. So before you use the information, make sure you verify it.  

03. Identify specific variables

Identify the specific variables that are related to the research study you want to conduct. These variables can give your research a new scope and can also help you identify how these can be related to your research design . For example, consider hypothetically you want to conduct research about the occurrence of cancer in married women. Here the two variables that you will be concentrating on are married women and cancer.

While collecting relevant literature, you understand that the spread of cancer is more aggressive in married women who are beyond 40 years of age. Here there is a third variable which is age, and this is a relevant variable that can affect the end result of your research.  

04. Generate the framework

In this step, you start building the required framework using the mix of variables from the scientific articles and other relevant materials. The research problem statement in your research becomes the research framework. Your attempt to start answering the question becomes the basis of your research study. The study is carried out to reduce the knowledge gap and make available more relevant and correct information.

Example of Conceptual Research Framework

Thesis statement/ Purpose of research: Chronic exposure to sunlight can lead to precancerous (actinic keratosis), cancerous (basal cell carcinoma, squamous cell carcinoma, and melanoma), and even skin lesions (caused by loss of skin’s immune function) in women over 40 years of age.

The study claims that constant exposure to sunlight can cause the precancerous condition and can eventually lead to cancer and other skin abnormalities. Those affected by these experience symptoms like fatigue, fine or coarse wrinkles, discoloration of the skin, freckles, and a burning sensation in the more exposed areas.

Note that in this study, there are two variables associated- cancer and women over 40 years in the African subcontinent. But one is a dependent variable (women over 40 years, in the African subcontinent), and the other is an independent variable (cancer). Cumulative exposure to the sun till the age of 18 years can lead to symptoms similar to skin cancer. If this is not taken care of, there are chances that cancer can spread entirely.

Assuming that the other factors are constant during the research period, it will be possible to correlate the two variables and thus confirm that, indeed, chronic exposure to sunlight causes cancer in women over the age of 40 in the African subcontinent. Further, correlational research can verify this association further.

Advantages of Conceptual Research

1. Conceptual research mainly focuses on the concept of the research or the theory that explains a phenomenon. What causes the phenomenon, what are its building blocks, and so on? It’s research based on pen and paper.

2. This type of research heavily relies on previously conducted studies; no form of experiment is conducted, which saves time, effort, and resources. More relevant information can be generated by conducting conceptual research.

3. Conceptual research is considered the most convenient form of research. In this type of research, if the conceptual framework is ready, only relevant information and literature need to be sorted.

QuestionPro for Conceptual Research

QuestionPro offers readily available conceptual frameworks. These frameworks can be used to research consumer trust, customer satisfaction (CSAT) , product evaluations, etc. You can select from a wide range of templates question types, and examples curated by expert researchers.

We also help you decide which conceptual framework might be best suited for your specific situation.

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Defining The Conceptual Framework

Making a conceptual framework, conceptual framework for dmft students, conceptual framework guide, example frameworks, additional framework resources.

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What is it?

• The researcher’s understanding/hypothesis/exploration of either an existing framework/model or how existing concepts come together to inform a particular problem. Shows the reader how different elements come together to facilitate research and a clear understanding of results.
• Informs the research questions/methodology (problem statement drives framework drives RQs drives methodology)
• A tool (linked concepts) to help facilitate the understanding of the relationship among concepts or variables in relation to the real-world. Each concept is linked to frame the project in question.
• Falls inside of a larger theoretical framework (theoretical framework = explains the why and how of a particular phenomenon within a particular body of literature).
• Can be a graphic or a narrative – but should always be explained and cited
• Can be made up of theories and concepts

What does it do?

• Explains or predicts the way key concepts/variables will come together to inform the problem/phenomenon
• Gives the study direction/parameters
• Helps the researcher organize ideas and clarify concepts
• Introduces your research and how it will advance your field of practice. A conceptual framework should include concepts applicable to the field of study. These can be in the field or neighboring fields – as long as important details are captured and the framework is relevant to the problem. (alignment)

What should be in it?

• Variables, concepts, theories, and/or parts of other existing frameworks

How to make a conceptual framework

• With a topic in mind, go to the body of literature and start identifying the key concepts used by other studies. Figure out what’s been done by other researchers, and what needs to be done (either find a specific call to action outlined in the literature or make sure your proposed problem has yet to be studied in your specific setting). Use what you find needs to be done to either support a pre-identified problem or craft a general problem for study. Only rely on scholarly sources for this part of your research.
• Begin to pull out variables, concepts, theories, and existing frameworks explained in the relevant literature.
• If you’re building a framework, start thinking about how some of those variables, concepts, theories, and facets of existing frameworks come together to shape your problem. The problem could be a situational condition that requires a scholar-practitioner approach, the result of a practical need, or an opportunity to further an applicational study, project, or research. Remember, if the answer to your specific problem exists, you don’t need to conduct the study.
• The actionable research you’d like to conduct will help shape what you include in your framework. Sketch the flow of your Applied Doctoral Project from start to finish and decide which variables are truly the best fit for your research.
• Create a graphic representation of your framework (this part is optional, but often helps readers understand the flow of your research) Even if you do a graphic, first write out how the variables could influence your Applied Doctoral Project and introduce your methodology. Remember to use APA formatting in separating the sections of your framework to create a clear understanding of the framework for your reader.
• As you move through your study, you may need to revise your framework.
• Note for qualitative/quantitative research: If doing qualitative, make sure your framework doesn’t include arrow lines, which could imply causal or correlational linkages.
• Conceptural and Theoretical Framework for DMFT Students This document is specific to DMFT students working on a conceptual or theoretical framework for their applied project.
• Conceptual Framework Guide Use this guide to determine the guiding framework for your applied dissertation research.

Let’s say I’ve just taken a job as manager of a failing restaurant. Throughout the first week, I notice the few customers they have are leaving unsatisfied. I need to figure out why and turn the establishment into a thriving restaurant. I get permission from the owner to do a study to figure out exactly what we need to do to raise levels of customer satisfaction. Since I have a specific problem and want to make sure my research produces valid results, I go to the literature to find out what others are finding about customer satisfaction in the food service industry. This particular restaurant is vegan focused – and my search of the literature doesn’t say anything specific about how to increase customer service in a vegan atmosphere, so I know this research needs to be done.

I find out there are different types of satisfaction across other genres of the food service industry, and the one I’m interested in is cumulative customer satisfaction. I then decide based on what I’m seeing in the literature that my definition of customer satisfaction is the way perception, evaluation, and psychological reaction to perception and evaluation of both tangible and intangible elements of the dining experience come together to inform customer expectations. Essentially, customer expectations inform customer satisfaction.

I then find across the literature many variables could be significant in determining customer satisfaction. Because the following keep appearing, they are the ones I choose to include in my framework: price, service, branding (branched out to include physical environment and promotion), and taste. I also learn by reading the literature, satisfaction can vary between genders – so I want to make sure to also collect demographic information in my survey. Gender, age, profession, and number of children are a few demographic variables I understand would be helpful to include based on my extensive literature review.

Note: this is a quantitative study. I’m including all variables in this study, and the variables I am testing are my independent variables. Here I’m working to see how each of the independent variables influences (or not) my dependent variable, customer satisfaction. If you are interested in qualitative study, read on for an example of how to make the same framework qualitative in nature.

Also note: when you create your framework, you’ll need to cite each facet of your framework. Tell the reader where you got everything you’re including. Not only is it in compliance with APA formatting, but also it raises your credibility as a researcher. Once you’ve built the narrative around your framework, you may also want to create a visual for your reader.

See below for one example of how to illustrate your framework:

If you’re interested in a qualitative study, be sure to omit arrows and other notations inferring statistical analysis. The only time it would be inappropriate to include a framework in qualitative study is in a grounded theory study, which is not something you’ll do in an applied doctoral study.

A visual example of a qualitative framework is below:

Some additional helpful resources in constructing a conceptual framework for study:

• Problem Statement, Conceptual Framework, and Research Question. McGaghie, W. C.; Bordage, G.; and J. A. Shea (2001). Problem Statement, Conceptual Framework, and Research Question. Retrieved on January 5, 2015 from http://goo.gl/qLIUFg
• Building a Conceptual Framework: Philosophy, Definitions, and Procedure
• https://www.scribbr.com/dissertation/conceptual-framework/
• https://www.projectguru.in/developing-conceptual-framework-in-a-research-paper/

Conceptual Framework Research

A conceptual framework is a synthetization of interrelated components and variables which help in solving a real-world problem. It is the final lens used for viewing the deductive resolution of an identified issue (Imenda, 2014). The development of a conceptual framework begins with a deductive assumption that a problem exists, and the application of processes, procedures, functional approach, models, or theory may be used for problem resolution (Zackoff et al., 2019). The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013). In applied research the application of theory in problem solving focuses on how theory in conjunction with practice (applied action) and procedures (functional approach) frames vision, thinking, and action towards problem resolution. The inclusion of theory in a conceptual framework is not focused on validation or devaluation of applied theories. A concise way of viewing the conceptual framework is a list of understood fact-based conditions that presents the researcher’s prescribed thinking for solving the identified problem. These conditions provide a methodological rationale of interrelated ideas and approaches for beginning, executing, and defining the outcome of problem resolution efforts (Leshem & Trafford, 2007).

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). Just as with traditional research, a theory does not or cannot be expected to explain all phenomenal conditions, a conceptual framework is not a random identification of disparate ideas meant to incase a problem. Instead it is a means of identifying and constructing for the researcher and reader alike an epistemological mindset and a functional worldview approach to the identified problem.

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Imenda, S. (2014). Is There a Conceptual Difference between Theoretical and Conceptual Frameworks? Sosyal Bilimler Dergisi/Journal of Social Sciences, 38(2), 185.

Leshem, S., & Trafford, V. (2007). Overlooking the conceptual framework. Innovations in Education & Teaching International, 44(1), 93–105. https://doi-org.proxy1.ncu.edu/10.1080/14703290601081407

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

Zackoff, M. W., Real, F. J., Klein, M. D., Abramson, E. L., Li, S.-T. T., & Gusic, M. E. (2019). Enhancing Educational Scholarship Through Conceptual Frameworks: A Challenge and Roadmap for Medical Educators . Academic Pediatrics, 19(2), 135–141. https://doi-org.proxy1.ncu.edu/10.1016/j.acap.2018.08.003

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Research Process Guide

• Step 1 - Identifying and Developing a Topic
• Step 2 - Narrowing Your Topic
• Step 3 - Developing Research Questions
• Step 4 - Conducting a Literature Review
• Step 5 - Choosing a Conceptual or Theoretical Framework
• Step 6 - Determining Research Methodology
• Step 6a - Determining Research Methodology - Quantitative Research Methods
• Step 6b - Determining Research Methodology - Qualitative Design
• Step 7 - Considering Ethical Issues in Research with Human Subjects - Institutional Review Board (IRB)
• Step 8 - Collecting Data
• Step 9 - Analyzing Data
• Step 10 - Interpreting Results
• Step 11 - Writing Up Results

Step 5: Choosing a Conceptual or Theoretical Framework

For all empirical research, you must choose a conceptual or theoretical framework to “frame” or “ground” your study. Theoretical and/or conceptual frameworks are often difficult to understand and challenging to choose which is the right one (s) for your research objective (Hatch, 2002). Truthfully, it is difficult to get a real understanding of what these frameworks are and how you are supposed to find what works for your study. The discussion of your framework is addressed in your Chapter 1, the introduction and then is further explored through in-depth discussion in your Chapter 2 literature review.

“Theory is supposed to help researchers of any persuasion clarify what they are up to and to help them to explain to others what they are up to” (Walcott, 1995, p. 189, as cited in Fallon, 2016). It is important to discuss in the beginning to help researchers “clarify what they are up to” and important at the writing stage to “help explain to others what they are up to” (Fallon, 2016).  

What is the difference between the conceptual and the theoretical framework?

Often, the terms theoretical framework and conceptual framework are used interchangeably, which, in this author’s opinion, makes an already difficult to understand idea even more confusing. According to Imenda (2014) and Mensah et al. (2020), there is a very distinct difference between conceptual and theoretical frameworks, not only how they are defined but also, how and when they are used in empirical research.

Imenda (2014) contends that the framework “is the soul of every research project” (p.185). Essentially, it determines how the researcher formulates the research problem, goes about investigating the problem, and what meaning or significance the research lends to the data collected and analyzed investigating the problem.  

Very generally, you would use a theoretical framework if you were conducting deductive research as you test a theory or theories. “A theoretical framework comprises the theories expressed by experts in the field into which you plan to research, which you draw upon to provide a theoretical coat hanger for your data analysis and interpretation of results” (Kivunja, 2018, p.45 ).  Often this framework is based on established theories like, the Set Theory, evolution, the theory of matter or similar pre-existing generalizations like Newton’s law of motion (Imenda, 2014). A good theoretical framework should be linked to, and possibly emerge from your literature review.

Using a theoretical framework allows you to (Kivunja, 2018):

• Increase the credibility and validity of your research
• Interpret meaning found in data collection
• Evaluate solutions for solving your research problem

According to Mensah et al.(2020) the theoretical framework for your research is not a summary of your own thoughts about your research. Rather, it is a compilation of the thoughts of giants in your field, as they relate to your proposed research, as you understand those theories, and how you will use those theories to understand the data collected.

Additionally, Jabareen (2009) defines a conceptual framework as interlinked concepts that together provide a comprehensive  understanding of a phenomenon. “A conceptual framework is the total, logical orientation and associations of anything and everything that forms the underlying thinking, structures, plans and practices and implementation of your entire research project” (Kivunja, 2018, p. 45). You would largely use a conceptual framework when conducting inductive research, as it helps the researcher answer questions that are core to qualitative research, such as the nature of reality, the way things are and how things really work in a real world (Guba & Lincoln, 1994).

Some consideration of the following questions can help define your conceptual framework (Kinvunja, 2018):

• What do you want to do in your research? And why do you want to do it?
• How do you plan to do it?
• What meaning will you make of the data?
• Which worldview will you situate your study in? (i.e. Positivist? Interpretist? Constructivist?)

Examples of conceptual frameworks include the definitions a sociologist uses to describe a culture and the types of data an economist considers when evaluating a country’s industry. The conceptual framework consists of the ideas that are used to define research and evaluate data. Conceptual frameworks are often laid out at the beginning of a paper or an experiment description for a reader to understand the methods used (Mensah et al., 2020).

Writing it up

After choosing your framework is to articulate the theory or concept that grounds your study by defining it and demonstrating the rationale for this particular set of theories or concepts guiding your inquiry.  Write up your theoretical perspective sections for your research plan following your choice of worldview/ research paradigm. For a quantitative study you are particularly interested in theory using the procedures for a causal analysis. For qualitative research, you should locate qualitative journal articles that use a priori theory (knowledge that is acquired not through experience) that is modified during the process of research (Creswell & Creswell, 2018). Also, you should generate or develop a theory at the end of your study. For a mixed methods study which uses a transformative (critical theoretical lens) identify how the lens specifically shapes the research process.                                   

Creswell, J. W., & Creswell, J. D. (2 018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Fallon, M. (2016). Writing up quantitative research in the social and behavioral sciences. Sense. https://kean.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&AuthType=cookie,ip,url,cpid&custid=keaninf&db=nlebk&AN=1288374&site=ehost-live&scope=site&ebv=EB&ppid=pp_C1

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of Qualitative Research, 2 (163-194), 105.

Hatch, J. A. ( 2002). Doing qualitative research in education settings. SUNY Press.

Imenda, S. (2014). Is there a conceptual difference between theoretical and conceptual frameworks?  Journal of Social Sciences, 38 (2), 185-195.

Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8 (4), 49-62.

Kivunja, C. ( 2018, December 3). Distinguishing between theory, theoretical framework, and conceptual framework. The International Journal of Higher Education, 7 (6), 44-53. https://files.eric.ed.gov/fulltext/EJ1198682.pdf  

Mensah, R. O., Agyemang, F., Acquah, A., Babah, P. A., & Dontoh, J. (2020). Discourses on conceptual and theoretical frameworks in research: Meaning and implications for researchers. Journal of African Interdisciplinary Studies, 4 (5), 53-64.

• Last Updated: Jun 29, 2023 1:35 PM
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• Research article
• Open access
• Published: 30 June 2020

How methodological frameworks are being developed: evidence from a scoping review

• Nicola McMeekin   ORCID: orcid.org/0000-0003-2918-8820 1 ,
• Olivia Wu 1 ,
• Evi Germeni 1 &
• Andrew Briggs 1  

BMC Medical Research Methodology volume  20 , Article number:  173 ( 2020 ) Cite this article

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Although the benefits of using methodological frameworks are increasingly recognised, to date, there is no formal definition of what constitutes a ‘methodological framework’, nor is there any published guidance on how to develop one. For the purposes of this study we have defined a methodological framework as a structured guide to completing a process or procedure. This study’s aims are to: (a) map the existing landscape on the use of methodological frameworks; (b) identify approaches used for the development of methodological frameworks and terminology used; and (c) provide suggestions for developing future methodological frameworks. We took a broad view and did not limit our study to methodological frameworks in research and academia.

A scoping review was conducted, drawing on Arksey and O’Malley’s methods and more recent guidance. We systematically searched two major electronic databases (MEDLINE and Web of Science), as well as grey literature sources and the reference lists and citations of all relevant papers. Study characteristics and approaches used for development of methodological frameworks were extracted from included studies. Descriptive analysis was conducted.

We included a total of 30 studies, representing a wide range of subject areas. The most commonly reported approach for developing a methodological framework was ‘Based on existing methods and guidelines’ (66.7%), followed by ‘Refined and validated’ (33.3%), ‘Experience and expertise’ (30.0%), ‘Literature review’ (26.7%), ‘Data synthesis and amalgamation’ (23.3%), ‘Data extraction’ (10.0%), ‘Iteratively developed’ (6.7%) and ‘Lab work results’ (3.3%). There was no consistent use of terminology; diverse terms for methodological framework were used across and, interchangeably, within studies.

Conclusions

Although no formal guidance exists on how to develop a methodological framework, this scoping review found an overall consensus in approaches used, which can be broadly divided into three phases: (a) identifying data to inform the methodological framework; (b) developing the methodological framework; and (c) validating, testing and refining the methodological framework. Based on these phases, we provide suggestions to facilitate the development of future methodological frameworks.

Peer Review reports

There is no formal definition of a methodological framework amongst the academic community. There is, however, unspoken agreement that a methodological framework provides structured practical guidance or a tool to guide the user through a process, using stages or a step-by-step approach [ 1 , 2 , 3 , 4 , 5 ]. Specific descriptions of a methodological framework include: ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures’ [ 6 ], a ‘set of structured principles’, an approach for ‘structuring how a given task is performed’ [ 7 ], and a ‘sequence of methods’.

The benefits of using methodological frameworks are manifold: they can improve the consistency, robustness and reporting of the activity [ 8 ], enhance the quality of the research, standardise approaches [ 5 ], and maximise trustworthiness of findings [ 2 ].

In 2017, Rivera et al. published the results of a literature review which identified existing methodological frameworks used to measure healthcare research impact and summarised the common themes and metrics used to measure this impact [ 6 ]. The authors found that the identified methodological frameworks had been developed using a variety of approaches, with no guidelines or consensus on the best pathway that should be used to develop a robust methodological framework. The authors concluded that this lack of guidance needs to be addressed to ensure that best practice methods can be used in the future . We sought to address this gap, by 1) systematically scoping the literature on methodological frameworks, charting and summarising approaches employed, and using these summarised approaches to make suggestions for developing future methodological frameworks, 2) identify terminology used in the literature in order to inform future research. Rather than limiting our search to methodological frameworks related to academic research as Rivera et al. did, we opted to be more inclusive so we could understand the rationale and approaches for the development of methodological frameworks in the wider arena.

We carried out a scoping review as a way of mapping the existing landscape on the use of methodological frameworks, identifying approaches used to develop them, and summarising these approaches thematically to inform suggestions for developing methodological frameworks. Scoping reviews have been shown to be particularly useful for when a research area has not yet been widely reviewed, such as areas with emerging evidence [ 9 ], to examine the extent, range and nature of a research area [ 10 ], where there is a lack of consistency in methodology and terminology to clarify key concepts and definitions [ 11 ] and for informing a systematic review [ 12 ]. Our scoping review methodology followed Arksey and O’Malley’s recommendations [ 10 ], as well as more recent guidance by Levac [ 9 ] and Colquhoun et al., [ 11 ]. Our study consisted of the following stages: 1) identifying the research question; 2) identifying relevant studies; 3) study selection; 4) charting the data; and 5) collating, summarising and reporting the results. No publicly-available protocol is available for the research; however, interested readers can contact the corresponding author for further details on methods.

Identifying the research question

There is no formal definition of a methodological framework, nor is there guidance on the approaches to use when developing a methodological framework. In this review the working definition of a methodological framework is a tool to guide the developer through a sequence of steps to complete a procedure. Methodology is defined as the group of methods used in a specified field, and framework is defined as a structure of rules or ideas. The primary research question posed in this review is ‘what approaches are used in developing a methodological framework and is there consistency in those approaches to enable making suggestions for developing methodological frameworks?’ The secondary research question is ‘what terminology is used for naming methodological frameworks?’

Identifying relevant studies

Identifying relevant studies followed an iterative approach, guided by an experienced subject librarian. An initial search was conducted in August 2018 in Web of Science. The results of the initial search helped to inform the scoping review search. There were no standardised MESH terms for methodological frameworks, because of this index terms were also scrutinised.

The main scoping review search took place in September 2018. We searched MEDLINE and Web of Science for published literature and also conducted a search for grey literature. The search terms used were necessarily narrow to avoid an impractically large amount of potential studies. Only titles rather than abstracts were searched to ensure that the search terms were the main focus of the article or paper. Details of search terms used are included in Additional file  1 .

The grey literature search used methods previously published by Godin’s et al. [ 13 ] who used systematic methods for grey literature searching. The search was conducted in Google and results were restricted to the first 10 pages (100 hits). A single search term was used; ‘Methodological framework development’. Drawing on the approach used by Rivera et al. [ 6 ], we also searched Google Images; methodological frameworks are often presented as a diagram and therefore could be easily identified using this approach. Based on Rivera et al’s published methods the first 50 items were screened [ 6 ]. The electronic search was supplemented by a manual search of the reference lists and citations of all the relevant studies.

Study selection

Studies were eligible for inclusion if: (a) they included a methodological framework and reported the approach used for developing that framework; (b) were written in English; and (c) were published in the last decade (2008 onwards). Screening criteria were established a priori. Duplicates were removed, and titles and abstracts of identified papers were screened for potential eligibility by the first author (NM) after downloading the search results into Excel. The full texts of potentially eligible articles were retrieved and read to assess eligibility for final inclusion, also by the first author (NM). Any uncertainty over eligibility for inclusion was discussed by the authors.

Charting the data

The lead author (NM) developed a data charting form on Microsoft Excel and extracted from each individual paper the following information: (a) basic study characteristics (i.e. authors, title, journal, type of study, year of study and country of origin); (b) subject area; (c) approaches taken in developing the methodological framework; and (d)terminology used for methodological frameworks .

Collating, summarising and reporting the results

The extracted data were analysed in line with the aims of the scoping review. Approaches were examined in detail, then synthesised and grouped together into similar methods. The approaches are reported descriptively with frequencies and percentages. These approaches were then categorised into phases and interpreted to make the suggestions. The results were reported in line with the PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation [ 14 ]. The completed PRISMA-ScR is provided in Additional file  2 .

Literature search

The combined search strategies yielded a total of 320 records (266 after removing duplicates). 179 potentially relevant full-text papers were screened and 30 were included in the review [ 1 , 2 , 3 , 4 , 5 , 8 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. The flow chart of study selection is presented in Fig.  1 .

PRISMA flow chart of study selection

Study characteristics

A majority of included papers (26/30) were journal articles, followed by conference proceedings (3/30) and a book chapter (1/30). The studies represented a wide range of subject areas; 20 different subject areas were identified, the most common being ecology (6/30), followed by education (4/30), then manufacturing and regional (3/30), and healthcare, architecture and health economics (2/30). The papers originated from 14 countries; the most common was UK (8/30), followed by Greece, Germany, US and the Netherlands (3/30) and finally Italy (2/30). Basic study characteristics are presented in Additional File  3 .

We found a variety of terms used to describe the methodological frameworks. This use of different terms was seen in both the title and the body of the study. Six studies did not include ‘methodological framework’ in the title (20.0%). Of these one included the words ‘methodological’ and ‘framework’ separately [ 2 ], four included only ‘framework’ in title and one used the term ‘conceptual framework’. Of these six studies two were identified from references [ 4 , 5 ], two from citations [ 37 , 38 ] and one from Google images [ 34 ].

Alternative terms for methodological frameworks were used interchangeably within the studies (Fig.  2 ).

Terminology used in studies

Most studies included a combination of ‘methodological framework’ and ‘framework’ to describe the methodological framework (63.3%). One used a combination of methodological framework and conceptual framework. Three used ‘framework’ only and one used ‘methodological framework’ only. One study used three terms and a further two studies used a combination of four terms.

Keywords used in the studies that related to methodological frameworks are summarised in Table  1 . Half of the studies (15/30) did not have any keywords related to methodological frameworks. Of those that used keywords related to methodological frameworks most used ‘methodology’ (4/30), followed by ‘methodological framework’ (3/30), ‘design methodology’ (2/30), ‘simulation methodology’ (1/30), ‘methods’ (1/30) and ‘guidance’ (1/30). One study contained two relevant keywords [ 5 ]. 4/30 studies had no keywords at all

Approaches used for the development of methodological frameworks

We identified eight different approaches used for developing methodological frameworks (Table 2 ), these are also summarised by study in Additional File  4 .

The most frequently reported approach was ‘Based on existing methods and guidelines’, which comprise previous methodological frameworks or guidance and published methodology. Whilst some studies did not explain how the existing methods formed the foundations of the framework being developed, most did expand this further: adapting the methods [ 19 , 24 ], integrating methods, building on the existing methods [ 4 , 37 ], based on the framework [ 20 , 21 , 22 , 27 , 30 , 33 ], combined well established guidelines which comprised the same stages [ 16 ], and the framework was basic inspiration [ 28 ]. Only one study specified how the frameworks or guidance was identified; Squires and colleagues used a literature review [ 5 ].

Ten studies reported ‘Refined and validated’ as a method. Approaches taken to refining and validating comprised; piloting the framework [ 35 ], trialling identified stages and using the results of the trial to further develop the framework [ 25 ], using a case study or Delphi panel to evaluate and refine the framework [ 5 , 8 , 33 ], using a case study to validate the framework [ 17 , 29 ] and testing the framework [ 20 ]. Two studies did not report details of the case study [ 18 , 24 ].

Nine studies reported using ‘Experience and expertise’ to develop the methodological framework, and reported using experience from different levels: personal [ 15 ], school/university [ 25 ] and country level [ 28 ]. One study restricted ‘experience’ to the authors’ experience [ 15 ], the rest included the experience of experts in the field of the methodological framework. In all but one study the experts were recruited specifically to develop the methodological framework, the remaining study used experience already reported [ 28 ]. Methods used to extract experience and expertise comprise: during meetings [ 18 ], consultations [ 39 ] and collaboration [ 33 ]. Two frameworks did not specifically mention experience but used surveys and interviews [ 34 ] and focus groups for extracting expertise [ 5 ]. Whilst these studies did not explicitly mention experience the methods reported would have extracted experience or views on experience.

Eight studies reported conducting a ‘Literature Review’. Specifically; purposeful sampling [ 2 , 26 ], sources for searches included databases, dissertation [ 23 ], library catalogue, key author, databases websites and citations [ 8 ]. Other studies reported conducting a literature review but did not report specific methods used [ 5 , 8 , 23 , 29 , 33 , 35 ].

Seven studies reported using ‘Data synthesis and amalgamation’. Specific methods included: identifying phases [ 2 ], themes [ 2 , 34 ] and dimensions [ 23 ], analysing and grouping or categorising themes, or thematic analysis [ 2 , 3 , 8 , 23 , 26 ].

‘Data extraction’ was reported in three studies and includes extracting data from interviews and focus groups using transcribing methods [ 5 , 34 ], and extracting key information from published literature [ 2 ].

‘Iteratively developed’ was a method reported in two studies, one framework had no details on this [ 20 ], the other explained that the framework evolved and developed as items were extracted, synthesised and revised [ 8 ].

The least frequently mentioned method was ‘Lab work results’, the study that reported using this method was from the field of explosives, where the results of lab tests were used to inform the framework [ 1 ].

A pattern emerged whilst reviewing the methods and in applying meaning to these results, they were split into three categories. The first category relates to identifying evidence or data to inform and shape the framework. This evidence comes from: existing methods, literature reviews, lab results and experience/expertise. The second category relates to developing the framework using the identified data, comprising: extracting data, and synthesising and amalgamating this data iteratively. The third and final category is refining and validating the framework: trialling the framework with pilot or case studies and or Delphi panels.

The scoping review results were used as a basis for the following outline of suggestions that may be considered for developing a methodological framework on. The three phases underpinned the structure and specific approaches were included within those phases. These are summarised in Figure and explained in greater detail below.

(Uploaded as ‘Fig. 3 Summary of suggestions for developing methodological frameworks.pptx’)

Summary of suggestions for developing methodological frameworks

Phase 1 – identifying evidence to inform the methodological framework

This phase is split into two; the first is identifying previous frameworks or guidance which are used for the foundations of the new methodological framework, the second is identifying new data to help develop the methodological framework. This new data can be identified in numerous ways: purposeful literature searches, qualitative research (focus groups, interviews, surveys), collaboration between interested parties and the experience and expertise of the developers. If qualitative research is included, if possible it should be conducted with experts in the field of the methodological framework and not restricted to author experiences if possible.

Phase 2 – developing the methodological framework

In this phase the frameworks or guidance identified in Phase 1 are adapted, combined with other guidance and built upon to create the foundations of the new methodological framework. Key information in the new data identified in Phase 1 should be extracted using appropriate methods. Appropriate methods include; transcribing qualitative data, entering themes into predesigned tables, and entering quantitative information into piloted data extraction forms. Once the information is extracted it should be analysed, synthesised, and grouped or amalgamated into categories to inform the new framework. This should be an iterative process; after grouping or amalgamation of the new data, it should be brought back to key experts and the study team for refinement. This iterative approach should be followed until consensus is reached on the proposed methodological framework.

Phase 3 – evaluate and refine

In this final stage the proposed methodological framework should be evaluated and refined. Evaluation techniques include using case studies to pilot the methodological framework and Delphi panels. The results from this evaluation should be used to refine the methodological framework if appropriate. Refining will include updating the methodological framework with any changes identified from the evaluation stage and presenting these changes to key experts and the study team for verification.

These suggestions are not intended to be prescriptive, and the developer should adapt them to their specific situation. Finally, the developer should include the term ‘methodological framework’ at least in the title of the study, preferably in the body of the text too and as a keyword if possible.

Summary of evidence

The purpose of this scoping review was to identify approaches taken in developing methodological frameworks and terminology used in describing them. We were able to locate 30 studies that were published in the last decade and reported these approaches. Studies covered 20 subject areas and came from 14 different countries. After synthesis and amalgamation, we identified eight approaches used for developing methodological frameworks. Not all studies with methodological frameworks reported the approaches used to develop them; out of 179 potentially eligible frameworks scrutinised in full, 37 (20.7%) were rejected because the authors did not report approaches, Studies which did report approaches were often not clear about the methods used. However, whilst the approaches used to develop methodological frameworks were not always reported or reported clearly, there were a sufficient number of common approaches to allow the amalgamation and categorisation of the approaches that were reported to form an evidence base on which suggestions for developing methodological frameworks could be made.

In the included studies extracted terms used to describe methodological frameworks highlighted the lack of clarity in terminology, as different terms were used to describe methodological frameworks within the studies. The majority of studies used a combination of ‘methodological framework’ and ‘framework’, which is understandable bearing in mind journal word limits and flow of discussions. Two studies used a combination of four terms highlighting the lack of clarity in terminology. This lack of clarity in terminology suggests that when conducting a literature search for methodological frameworks, it is likely that many methodological frameworks might not be identified. We recommend using ‘methodological framework’ in the title of the study as a minimum.

Many of the included studies did not use any keywords related to methodological frameworks suggesting that the studies were more focussed on the subject of the methodological framework rather than the actual process of developing the methodological framework itself.

As there is no existing guidance for developing methodological frameworks, it is not possible to interpret the results of this scoping review in light of what is already known. However, Rivera et al. [ 6 ] also concluded that methodological frameworks vary in their development, although there appear to be some common approaches. In their review, only one paper (4%) did not report any methods of development [ 40 ], compared to 37 (20.7%) in this review. Rivera et al. reported four key methods: using a literature review, stakeholders’ involvement, methods to incorporate stakeholder views and a pilot phase. The results from this scoping review identified additional methods, including: refined and validated, data synthesis, data synthesis and amalgamation and iteratively developed.

Strengths and limitations

To the best of our knowledge, this is the first study to identify approaches used for the development of methodological frameworks; our work addresses an important gap in the literature by providing suggestions for the development of future methodological frameworks and highlighting issues with terminology which can inform future work. Further strengths are; the methodological frameworks identified and analysed come from many contexts and demonstrate a degree of natural variation, and our research offers a contemporary slice of how methodological frameworks are used.

Certain limitations need to be acknowledged and addressed. As with any review this research is limited by dependency on the quality of included studies and the search strategy, specific limitations are discussed further below [ 41 ].

First, issues with lack of consistency in terminology meant that further examples of methodological frameworks may have been missed in the search if a different term to ‘methodological framework’ had been used in the title. However, a pragmatic balance had to be struck between the sensitivity and specificity of the search; using the search term ‘framework’ only would have resulted in an impractical number of results. This limitation to the search strategy will have potentially resulted in limiting the number of approaches reported and limited the identification of variations in terminology Also, as previously discussed, not all the studies identified included methods, limiting the amount of data that could be extracted and included in the scoping review. Linked to this, not all methods were clearly reported, perhaps because of word count, the aim and focus of the paper, or traditionally how different disciplines report. Moreover, data screening and extraction was conducted by one reviewer, although key decisions on study selection were discussed with the wider team. Last, scoping reviews do not assess the quality of included evidence; therefore, there is a risk that the frameworks included in this review were not of high quality, however, as there is scant evidence in this area, a scoping review was the most suitable method to use [ 12 , 42 ].

The current lack of guidance provides an opportunity to make some initial steps towards addressing this gap in the knowledge. This scoping review summarises the reported approaches used in developing a methodological framework. This work can be viewed as the first step in developing robust guidance for developing a methodological framework. As the terminology, definitions and process are not widely agreed, there is a need for standardisation of these. Whilst terminology and definitions were not consistent, reported approaches for development were. This consistency allowed for suggestions to be made for developing methodological frameworks. Future research to update this scoping review and suggestions should include a systematic review based on the terminology identified, and collaboration with experts, for example using a Delphi panel or focus group, to develop best practise guidance. Furthermore, a standardised procedure to collecting qualitative data in phase one would add consistency and transparency to evidence gathering.

Availability of data and materials

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Acknowledgements

The authors would like to acknowledge Paul Cannon who assisted with the search strategy.

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NM conceived and carried out the scoping review. EG provided expertise in methodology. NM took the lead in writing in the manuscript. OW, EG and AB contributed to the draft manuscript, and all authors read, contributed to and approved the final manuscript.

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Supplementary information

Additional file 1..

OVID Medline search September 2018.

Additional file 2.

Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Additional file 3.

Basic study characteristics.

Additional file 4.

Extracted data from studies.

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McMeekin, N., Wu, O., Germeni, E. et al. How methodological frameworks are being developed: evidence from a scoping review. BMC Med Res Methodol 20 , 173 (2020). https://doi.org/10.1186/s12874-020-01061-4

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Conceptual Framework - Meaning, Importance and How to Write it

Ideally a concept is an abstract idea which forms a basis for learning or argument. In this article, it is a part of the research process where ideas on the subject topic are studied. It is also an analytical tool used for comprehensive understanding of the subject topic for the readers of a research work.

Why do we use conceptual frameworks in research papers?

A conceptual framework is used in a research paper to explain the key concepts or variables and the relationships between them that need to be studied. Simply put, conceptual framework is the way ideas are organised to achieve a research project's purpose and explanation is the most common method employed.

While a conceptual framework means a researcher's perception about the research problem, it is still an arranged and self-explanatory method drafted for the readers.

In this article, we will examine:

• the process of conceptual framework
• the types of conceptual framework
• the components of a good conceptual framework
• the guidelines to writing a good conceptual framework

Process of Creating a Conceptual Framework.

Firstly, it is worthy to note that a conceptual framework is a structure. The researcher uses that structure to best explain the natural progression of the phenomenon to be studied ( Camp, 2001 ). It is linked with the concepts, empirical research and important theories used in promoting and systemising the knowledge gathered by the researcher in the course of the study. The process of conceptual framework includes;

• Choose your topic: You have to decide on what will be your research topic. This should be based on your interest and available resources.
• Do a literature review: Visit relevant and updated researches on similar  topics to learn past used conceptual frameworks.
• Earmark the important variables (dependent and independent variables) in your study then use it to generate your conceptual framework.
• Expand your conceptual framework by identifying other variables (mediator, moderator, control variables) that might influence the relationship between your independent and dependent variables.

NB: Conceptual framework includes one or more formal theories (in part or whole) as well as other concepts and empirical findings from the literature. It is used to show relationships among these ideas and how they relate to the research study.

Having gone through the  process of writing a conceptual framework, the types may interest you. There are various types of conceptual framework employed by researchers depending on their choice and the design of study. They are listed below.

The Types of Conceptual Frameworks are:

• Visual representation
• Mathematical description

Taxonomy :  This is a verbal description which categorises phenomena into classes. Relationships are evident inasmuch as those items within a class are alike; but the relationships among the classes are either weak or nonexistent. This type of conceptual framework doesn’t show relationships between classes.

The scope of the phenomena described may be narrow, but it is often broad. Evidence for the phenomena may be a result of direct experience, or developed from, logical reasoning, or developed empirically. Thus the source may be described as lacking rigor from a scientific viewpoint. Examples of taxonomies would include Barrett's (1968) Taxonomy of Comprehension, the descriptions of the reading process of. Fries (1962) or Lefevre (1964), and the lists of reading objectives often found in curriculum guides and basal reader guidebooks.

Visual Representation : This type of conceptual framework provides a picture of the phenomena, it shows that relationships between classes exist without showing the extent of the relationship. Relationships are shown between classes, whereas in the taxonomy no such relationships are usually  made. The phenomena presented may be as broad as the total reading situation or as narrow as a single grapheme-phoneme correspondence (GPC). The evidence must be at least logical and may have empirical support. It may come from authority opinion or research. From a scientific viewpoint evidence may or may not be rigorous. Examples of visual representations include the work of Gray (1960) and Robinson (1966) illustrating the major aspects of reading; the Goodman (1970) diagram of the reading process; and the work of Smith and Carrigan (1959).

Mathematical Description : This is a type of conceptual framework in which the phenomena can be expressed in some type of mathematical equation, although verbal description and pictorial representation are also possible. The relationships between phenomena are quantified with specific weights given to each; which clearly differentiates this type of conceptual framework from the visual representation which only shows that a relationship exists, but not the degree; and the taxonomy which may not show any relationship between the classes presented. The phenomena represented can probably be described as narrow in respect to reading; but this may change.

In this type, empirical evidence from research is required; but logical explanation may not be required since such frameworks may only represent what is, rather than why. Mathematical descriptions tend to be narrow in scope because only evidence that can be empirically gathered is included. An example of this type of conceptual framework is the work of Holmes (1960, 1965) and Singer (1965).

Note: There is no specific demarcation among the three types of conceptual frameworks. A mathematical description may be visually represented or verbally described. Likewise, a visual representation may be described verbally; and a picture of a taxonomy may be drawn although the relationship among the various classes would not be clearly evident. A taxonomy or visual representation could eventually become a mathematical description if the appropriate empirical evidence was gathered and analysed. It should not be assumed that one type of conceptual framework is inherently superior to another.

More Advanced Types of Conceptual Framework includes:

• Working hypothesis – exploration or exploratory research.
• Pillar questions – exploration or exploratory research.
• Descriptive categories – description or descriptive research.
• Practical ideal type – analysis.
• Models of operations research – decision making.
• Formal hypothesis – explanation and prediction.

However, in qualitative research, conceptual framework can be developed based on the research problem, objective and questions. The main goal of the conceptual framework is to illustrate your research approach in some pictorial or text form to ease reader's understanding. Generally, the type employed is usually picked in resonance with the research topic itself, a conceptual framework should be constructed before collecting data and this is done in chapter two.

Components of a Conceptual Framework.

• Definition of the topic.
• Qualitative characteristics and useful information on the topic.
• The elements of the topic
• Components of the topic.
• Presentation and closure.

Guidelines to Writing a Good Conceptual Framework.

• Select a topic for your research and carry out a literature review.
• Understand what research has already been carried out on the subject matter.
• Look for the specific variables explained in the literature and examine the relationship between them.
• Fill in the gap in knowledge.
• Create your conceptual framework; it can be in the form of a flowchart, mind map or concept map and explain thereafter.

[1] Camp, W. G. (2001). Formulating and Evaluating Theoretical Frameworks for Career and Technical Education Research. Journal of Vocational Educational Research, 26 (1), 27-39.

[2] Robert E (1970) A Schema for the Classification of Conceptual Frameworks Involving Reading . Journal of Reading Behavior, Vol. 3, No. 2, Spring, 16-18

Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution

• Open access
• Published: 02 September 2024
• Volume 3 , article number  135 , ( 2024 )

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• Debbie A. Sanders 1 , 2 &
• Shirley S. Mukhari 1 , 3  

In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment.

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1 Introduction

Over the past few years there have been notable advances in supporting lecturers to enhance their teaching methods, and in improving students’ learning experiences through the adoption of blended learning. Defined as a combination of face-to-face (F2F) and online learning, blended learning offers more flexible learning experiences that are also deemed to be more effective. Also known as "brick-and-click" instruction, hybrid learning, dual-mode instruction, blended pedagogies, or HyFlex, targeted, multimodal or flipped learning [ 5 , 38 ], this approach is becoming increasingly popular. The approach, which combines traditional classroom F2F learning with online components, facilitates the application of asynchronous teaching and learning in educational settings [ 16 ]. In recent years, educational institutions have widely embraced blended learning as the preferred teaching method, expressing appreciation for its flexibility, timeliness, and uninterrupted learning opportunities. As hybrid learning gains popularity, so it has become increasingly important to find new ways of improving the effectiveness thereof [ 17 ]. Recent developments in artificial intelligence (AI) are one way of enhancing the efficacy of blended learning approaches. With the integration of AI into academic environments, individualised learning experiences can be provided, administrative tasks can be automated, and such systems can be adapted to student needs [ 20 , 44 ]. For these reasons, the researchers sought to understand lecturers’ views on the relationship between AI and blended learning, as those perspectives are crucial for developing effective teaching and learning practices in higher education contexts.

AI involves the study and development of computer programs that display ‘intelligent’ behaviour, mindful of the fact that machine intelligence is distinct from the natural intelligence that is inherent in humans and animals. Other definitions of AI examine efforts to enable computers to possess intelligence [ 19 ]. Ultimately, AI extends much further than just robotics, however, to include the human capacity to program computers and other technology-enabled devices, so that they comprehend the principles of intelligent thought and behaviour. As a key invention of the Fourth Industrial Revolution (4IR), AI is considered one of the most influential technologies of our time [ 19 ]. For the purposes of this research, AI will be taken to refer to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem solving, and decision making. From the point of view of lecturers, such integration would require them to adapt their teaching and learning approaches, to make them more efficient and effective in addressing diverse student needs [ 25 ].

It is against this background that the researchers felt the need to investigate what impact AI has on blended learning, which includes lecturers having to revisit the way in which they usually lecture (the educator teaching, and students listening and regurgitating what they have been taught), to scenarios where AI is infused into a hybrid learning approach. It is crucial to emphasise that, in the context of this paper, blended learning is deemed to comprise more than the mere incorporation of technology into an academic programme. The adoption of the term, in this instance, aligns with what Lee [ 24 ] describes as a hybrid teaching approach, integrating traditional F2F lecturing with the latest, updated technologies. This mode aims to enhance student success and promote the relevance of the course content. Interaction among students, and between lecturers and the student cohort, is accomplished through various internet-enabled learning technologies, including platforms such as online discussion forums [ 3 ]. These technologies play a crucial role in promoting communication between educational stakeholders. Consequently, the smooth integration of conventional classroom instruction with e-learning offers valuable support for students’ asynchronous and collaborative learning [ 15 ]. In addition, the use of AI supplements these interactions by providing personalised feedback, allowing for two-way discussions, and for learning resources to be adapted to individual students’ needs. This combination of traditional and e-learning environments through the adoption of AI technologies makes for a more engaging and effective educational experience. It improves educational access, and promotes inclusive and equitable education, resulting in a sustainable, efficient, and accessible system of blended learning [ 3 ].

Although blended learning is not a novel concept, its use has remained largely unchanged. Its numerous challenges require further and more in-depth research into its efficacy [ 5 , 38 ]. Various aspects, including the specific technological tools and learning approaches used, and the overall quality of the teaching and learning on offer, need examination [ 5 ]. While blended learning has long been used as an approach to enhance students’ learning experiences, much of the research has focused on countries in the Global North, such as Belgium, the United Kingdom (UK), and Italy [ 6 ]. Limited research has been conducted in the South African context in this regard [ 43 ]. Notably, a search on Google Scholar revealed that only minimal related research has been published in the past 5 years (only eight research resources), with none of them originating from South Africa. Despite the increased uptake of hybrid learning in academia, AI is often perceived as a separate technological tool with limited influence on teaching and learning approaches. To enhance the effectiveness of blended learning in higher education contexts, it is essential to identify and understand lecturers’ views on the incorporation of AI into their teaching and learning, taking into account the SAMR model [ 34 ].

The significance of the study thus lies in elucidating lecturers’ viewpoints on the impact which AI and blended learning have on teaching and learning. The researchers also set out to assist higher education institutions (HEIs) in creating, adapting or changing conditions so that they are more relevant and meaningful, and ultimately enable lecturers to ensure that students are more successful in achieving specific learning outcomes. Clearly, AI is a tool that must be embraced in this modern, ever-evolving technological world.

The main research question designed to guide the study, was:

How do lecturers perceive the influence of AI on blended learning in the context of a South African higher education institution?

Four sub-questions were also formulated in this regard:

How do lecturers in South African higher education institutions perceive and integrate AI technologies into blended learning lessons within the SAMR framework?

What challenges do lecturers identify when incorporating AI into blended learning lessons, considering the SAMR levels of substitution, augmentation, modification, and redefinition?

How does AI influence student engagement, interaction, and learning outcomes in blended learning environments?

What support mechanisms do lecturers require to ensure the successful incorporation of AI into blended learning lessons?

Following the above introductory discussion on lecturers’ perspectives on blended learning and AI integration, the sections which follow focus on a comprehensive literature review on the topic, the theoretical framework chosen for this research, an exploration of the selected research methodology, the findings, and recommendations for the successful implementation of blended learning infused with AI. Lastly, concluding remarks summarise the key findings, and outline implications for future research and educational practice.

After exploring the background and rationale for this study, it is crucial that this study examines the existing body of research related to blended learning and Artificial Intelligence integration in higher education which is the focus of the next section.

2 Literature review

2.1 blended learning as an approach to teaching and learning.

In recent years, the educational domain has experienced significant transformations, driven by the continued evolution of information technology. One notable outcome is the emergence of blended learning, a pedagogical approach that integrates diverse methods of delivering information, such as web-based software courses, coupled with the management of practical knowledge [ 33 ]. According to Damanik [ 12 ], Choi and Park [ 10 ], and Qiu et al. [ 35 ], blended learning can be implemented both on- and offline. Bozkurt [ 8 ] expands on this, emphasising that blended learning encompasses F2F interactions and online engagement through specific mediums. The positive impact of the blended learning model on students’ learning outcomes, through fostering heightened engagement, is echoed by Santosa et al. [ 39 ]. This model, as observed by Nugraha et al. [ 31 ], also enhances students’ problem-solving abilities and understanding of the module content. This ensures adaptability and flexibility that caters to individual students’ needs, preferences, and schedules [ 43 ]. While initially designed for specific modules and their content, this approach prioritises student-centred satisfaction [ 43 ], thereby supporting HEIs in pursuing their goals and ultimately achieving the successful attainment of the learning outcomes set [ 38 ]. At its core, the concept of blended learning is built on the understanding that learning is not a singular, isolated event, but rather an ongoing, continuous process [ 33 ]. This transformational shift aligns with the modification level of the SAMR model, as it goes beyond merely substituting traditional teaching methods with technology, instead modifying the entire learning experience. Admittedly, the development of efficient blended learning systems can be demanding, particularly in respect of their endurance and flexibility to adapt to modern technological developments [ 3 ].

The integration of Artificial Intelligence (AI) in blended learning environments has been the subject of increasing debate in recent years. A review of the literature reveals that while there are some global studies completed which have explored various aspects of AI in education, research originating from South Africa is notably sparse. Alshahrani [ 3 ], Ferry et al. [ 13 ], and Rahman et al. [ 37 ] have all examined the impact of AI on student engagement and learning outcomes in blended learning, highlighting the potential benefits of AI-driven feedback and personalised learning experiences. However, research from a South African context is underrepresented, which may limit the generalisability of these findings to local settings. This gap highlights the need for more region-specific studies, particularly in HEIs.

The year 2017 marked a significant milestone, with extraordinary and unique developments in our understanding of the possibilities of the merging of technology and AI. As a rapidly advancing field, AI has the potential to influence the future of information technology and, for this reason, training in that regard is imperative [ 33 ]. The study of AI is fascinating and intriguing, representing the future of information technology. AI has the potential to enhance people’s lives by ensuring that tasks are accomplished more rapidly and more accurately. Petrova [ 33 ] suggests that soon AI will be integrated into all platforms and technologies, across different spheres. This development represents a shift toward the redefinition level of Puentedura’s [ 34 ] SAMR model. It transcends the traditional roles of both lecturers and students and introduces new possibilities for teaching and learning through the use of technology. While there is still substantial work ahead, AI empowers lecturers to achieve more—and with greater efficiency—than ever before. In the past, AI was a technology that instilled fear in many. The notion that computers could think and learn like humans raised concerns about our ability to comprehend and constrain machines. However, as we move away from the pursuit of human-like AI, we can now view its progress as a tool serving to develop and enhance every industry [ 33 ].

AI stands out as a potential answer to improve the efficiency and durability of blended learning systems [ 3 , 23 ]. Through the use of AI techniques such as machine learning (ML), natural language processing (NLP), and chatbots, opportunities are created which allow for the automation of diverse features of the learning journey, including content delivery, assessment, and feedback [ 3 , 22 ]. Furthermore, AI allows for the customisation of the learning experience for individual students, ensuring increased engagement and enhancing learning outcomes [ 3 ]. The fact that AI makes it possible for lecturers to adapt and automate their teaching, represents a change in traditional teaching and learning methods, aligning with the substitution as well as modification levels of the SAMR model, as technology can be used as a direct substitute for conventional teaching and learning methods, while also accommodating or revealing new capabilities. It offers a vast range of new possibilities to help ensure the successful achievement of a module’s learning outcomes—something that was not possible with conventional approaches.

It became clear to the researchers that while relevant, limited studies on this theme exist in South Africa, Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Moreover, there is a critical need for research that addresses the localised implementation of AI-driven blended learning solutions, particularly in rural and under-resourced areas where access to technology is inconsistent [ 27 ]. Such studies would provide valuable insights into how AI can be utilised not only to enhance learning outcomes but also to bridge the educational disparities that persist across different regions of the country. While limited, some relevant studies do exist. Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Integrating AI into blended learning systems offers the potential to establish an education system that is not only efficient, but also sustainable. The use of AI in education, particularly in blended learning, revolves around delivering personalised learning experiences, and optimising course delivery [ 3 ,  2 , 24 ]. Through the adoption of AI technology in hybrid learning systems, it is easier for lecturers to analyse student performance data for a personalised learning experience which aligns with individual strengths, weaknesses, and interests [ 3 , 25 ]. The implementation of AI in blended learning streamlines tailored assistance for students. Alshahrani [ 3 ] concurs that AI allows for responsive interaction. This corresponds to the augmentation (A) level of the SAMR [ 34 ] model, where technology is used to improve the learning experience, exceeding what was achievable with traditional methods. Personalised support can easily be based on individual student needs. The personalised approach assists students in navigating complex concepts, thus helping to ensure the achievement of learning outcomes, and ultimate success.

AI is also conducive to enhancing teaching and learning methods, increasing efficiency through automated administrative tasks, and refining content delivery. Introducing AI tools to ensure a sustainable and efficient blended learning system allows lecturers to lessen the strain on the environment, by reducing paper usage and minimising the carbon dioxide emissions associated with physical (F2F) lectures or meetings. This not only improves educational effectiveness and accessibility, but also empowers students to acquire the essential knowledge and skills for building a sustainable future [ 3 ,  36 ].

Viktorivna et al. [ 40 ] point out that AI serves to enhance student engagement, and the effectiveness of their learning. AI also facilitates a more straightforward explanation of subject matter [ 32 ], thereby encouraging students to develop and enhance skills required in the twenty-first century [ 11 , 42 ]. AI is a valuable educational resource for blended learning, as it grants access to an ever-expanding range of learning materials. Furthermore, AI helps in the creation of lessons, quizzes, and rubrics which allow lecturers to reorganise the curriculum and content of a module. AI-generated resources can even be customised to align with students’ instructional preferences, thereby fostering a flexible and inclusive learning experience [ 3 ].

Various studies have shown that the infusion of AI in a blended learning module enriches the learning process for students, helping them attain specific learning outcomes [ 13 , 14 , 37 , 39 ]. The collaborative and conversational capabilities of AI enhance the overall learning experience, resulting in an enjoyment of the course and heightening active participation among the student cohort. Concerted engagement delivers improved learning outcomes, and a more profound understanding of the subject matter [ 3 ]. This aligns with the Augmentation (A) level of the SAMR model, as technology (AI in this instance) goes beyond merely enhancing traditional methods, to develop a more interactive and engaging learning environment, thus fostering increased student participation and leading to a better grasp of the subject matter.

The AI-based blended learning model boosts students’ digital literacy levels as well as their 21st-century thinking skills [ 37 ]. This innovative approach helps to improve their critical thinking skills, for use in the learning process [ 18 ]. Ultimately, models can be created using a variety of AI-based technologies, thereby saving lecturers time and enhancing students’ learning opportunities [ 37 ].

In higher education, large class sizes make it difficult for lecturers to offer individualised teaching and can impede swift and direct student support. AI negates this challenge by rendering personalised support. As such, AI delivers real-time answers and support, easing the workload on lecturers and enriching the learning experience. The rise in popularity of AI has initiated extensive discourse and research regarding its potential influence in the education sector, particularly in higher education where limited lecturer–student ratios present unique challenges [ 3 ]. Thus, it is clear that AI serves as a valuable asset in blended learning.

AI also enables the delivery of customised support, feedback, and motivation to students. Investigating these aspects will further our understanding of AI’s integration in blended learning, unveiling fresh insights to guide the design, implementation, and ethical use of related technologies in educational environments that adopt a hybrid learning approach [ 3 ]. Since this is a relatively new technological development and thus a relatively novel approach to teaching and learning, further research is needed, especially at HEIs, to analyse the exact impact on students’ performance. As Rahman et al. [ 37 ] concur, this approach needs further development. This research paper extends the current knowledge base in the field of blended learning, particularly in higher education, by providing insights into the integration of AI for enhanced student literacy, thereby filling a significant gap in the existing literature. Closing this gap will not only expand our understanding of emerging educational practices, but also provide valuable insights for educators, institutions, and policymakers aiming to optimise the student learning experience.

The follow section reviews the theoretical framework that guided this research.

3 Theoretical framework

For this research, the Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model was selected, to establish a solid foundation for investigating intricate aspects of AI’s influence on blended learning in HEIs. The model was chosen for its applicability to an understanding of the transformative impact of AI on blended learning, within the South African higher education milieu.

As per Puentedura’s [ 34 ] SAMR model, digital technologies can either enhance or transform educational practice. Enhancement involves substitution without functional change, or augmentation with functional improvement. Transformation, by contrast, requires significant task redesign or redefinition, leading to the creation of new tasks that were previously inconceivable. The model, which explores the creative application of technology to enhance the learning experience, serves as a useful guide for lecturers facing pedagogical changes as a result of using new learning technologies in their courses [ 30 ].

The SAMR model comprises four hierarchical levels. Firstly, the substitution level in which technology is used as a direct substitute for a traditional tool, with no functional change. At this level, the lecturer is tasked with substituting an older technology to perform the same activities as previously. While this may set the stage for future development, it is unlikely to have a significant impact on student outcomes at this stage [ 30 ]. The second level, augmentation, prompts lecturers to consider whether or not the available technology improves their teaching and learning. Instead of merely observing how students performed a given task before, lecturers must now focus on specific features of the technology, to accomplish the task more effectively, informatively, and swiftly. This approach aims to enhance students’ performance in completing assigned tasks [ 30 ]. Thus, technology acts as a direct substitute, with some functional improvement. The third level is the modification level in which technology allows for significant task redesign and, during modification, the lecturers’ objectives are to successfully achieve lesson outcomes with technological assistance. Teaching methods are thus adapted to ensure the incorporation of technology. While the syllabus remains unchanged, teaching approaches are modified to enable students to attain new goals that were previously deemed challenging [ 30 ]. The final level of redefinition empowers lecturers to replace older teaching techniques with newer, more effective teaching ideas. This is achieved through the use of technology, which allows for the creation of tasks once deemed inconceivable [ 7 ]. These teaching methods mainly seek to capture and retain students’ attention [ 30 ].

The SAMR framework enhances the value of the accumulated data, by offering a decision-making model for assessing the design of research interventions. The lowest levels—substitution and augmentation—encourage participants to actively engage, thus overcoming challenges related to technology, pedagogy, and their consequences. At the higher levels—modification and redefinition—the design of research questions becomes crucial for considering potential challenges in participants’ understanding of increasingly complex topics. This approach aims to purposefully overcome obstacles associated with the evolving nature of the scheduled tasks [ 4 ].

The application of the SAMR model in the context of this research involved a comprehensive examination of how AI influences blended learning practices. At the Substitution level, the study explored how AI replaces or replicates traditional teaching methods, offering insights into its role in directly substituting conventional approaches. Moving to the Augmentation level, the research assessed how AI enhances or improves existing educational practices, particularly in terms of providing additional features or functionalities that support teaching and learning. The Modification level focused on analysing how AI introduces significant changes in the execution of educational tasks, transforming traditional methods into more dynamic and effective practices. Finally, at the Redefinition level, the study evaluated how AI facilitates entirely new and transformative educational practices that were previously unattainable, showcasing its potential to revolutionise blended learning environments in ways that were not possible before.

Using this framework ensured that the research could follow a systematic approach to assessing the influence AI exerts on blended learning. It allowed the research to progress from simple enhancements to transformative changes. By offering a structured method for assessing the extent of AI integration in various facets of teaching and learning, the researchers gained valuable insights into the evolving landscape of educational technologies.

It is against this background that the chosen methodology is discussed next.

4 Methodology

4.1 research approach.

This study applied a qualitative methodology to investigate lecturers’ views on the influence AI has on blended learning. A qualitative approach involves a thorough exploration and grasp of phenomena, using non-numerical data and highlighting context, meanings, and subjective experiences [ 21 ]. The researchers deemed this method best suited for its exploratory nature of extracting relevant information. Focus group interviews were conducted, as Islam and Aldaihani [ 21 ] suggest, to allow for the coordination of discussions among a small group of participants, to mine and gather their views on a specific topic or phenomenon. For this research the focus was on the modules, lessons, and assessments of the participating lecturers. In addition, the researchers employed document analysis, which enabled them to explore the actual course content, lesson plans, and discussion forums.

In this way, the researchers arrived at an in-depth understanding of the specific phenomenon under investigation, as proposed by Morgan [ 29 ]. This approach enabled the researchers to scrutinise the lecturers’ experiences and opinions, focusing on their knowledge of, and encounters with, AI and blended learning.

The researchers applied a singular case study research design. This involved focusing on a single participant or unit of analysis, for an in-depth exploration of the intricacies and dynamics of a specific case [ 1 ]. This approach made it possible to conduct a thorough examination of the perceptions of lecturers employed in the College of Education at the HEI in question. The choice of design was prompted by ongoing developments in both AI and blended learning, which enabled the researchers to gain insights from lecturers actively engaged in related emerging educational practices.

4.2 Population and sample

Identifying a population for a particular research study enables the researchers to gather pertinent information from a smaller representative sample. This ensures that each distinct element of the collected information with similar characteristics is given the opportunity to be part of the sample. The researchers opted to employ a homogeneous purposeful sampling technique, intentionally selecting a group of participants who shared specific characteristics or traits deemed relevant to the research objectives. Participants were thus chosen based on shared traits, including gender, age, years of experience, the college in which they lectured, and their use of AI and blended learning, in order to align with the study’s purpose and objectives (Table 1 ).

Here, the group of participants selected were part of the same college at the specific HEI. The criteria for selection encompassed their approachability, availability to actively participate in the study, responsiveness to the interview questions, and willingness to share the content of their modules, lessons, and assessments. For this study, 15 lecturers agreed to participate: two males and 13 females, ranging in age between 32 and 63. Importantly, age has an impact on a user’s acceptance and embrace of AI in teaching and learning. Older lecturers often express discomfort with new technology adoption, and tend to be resistant to change. They are usually more comfortable with traditional ways of teaching and are fearful of using cutting-edge technological innovations. The participants’ readiness to openly share their course content, lessons and assessments, assisted the researchers in effectively analysing the collected data through the chosen document analysis data-collection technique. Consequently, the participants contributed valuable information that enhanced the depth of the study. Their active involvement in university affairs (especially the teaching and learning programmes) provided information that was highly relevant to this research .

4.3 Data collection

For this study, data were acquired by conducting interviews with the participating lecturers, enhanced by document analysis (see appendices A and B). The application of these data-collection techniques enabled the researchers to gather pertinent insights into the lecturers’ practical encounters with AI and blended learning in their teaching and learning. The use of open-ended, semi-structured interviews, along with document analysis, facilitated the analysis of the data, thus ensuring a thorough and precise in-depth study of the subject matter. The thematic approach adopted in this research aimed to pinpoint repeated topics identified in the data gathered. This enabled the researchers to concentrate on emerging themes specific to the realm of AI and blended learning, rather than providing mere synopses of the data [ 9 ].

4.4 Data analysis

To gain valuable insights from the participants’ answers to the interview questions, and information derived from the document analysis, a thorough study and interpretation of the collected data was imperative—an analytical process which is crucial for answering the research questions effectively. The researchers actively engaged in interpreting, consolidating, and synthesising the lecturer participants’ statements, to assign meaning to the data. This involved transcribing, comparing, and scrutinising the interview responses, along with the content of the modules, lessons, and assessments. The participant responses were coded manually, using letters of the alphabet, to ensure anonymity. Each response was tagged with a corresponding letter, making it possible to trace every piece of data back to the specific participant who supplied it. Each statement was carefully linked to specific codes and themes, especially given the fact that AI does not replace F2F lecturing, but rather augments teaching and learning. The coding process involved categorising data into the SAMR [ 34 ] levels, to reach conclusions about how lecturers perceive AI's influence on different aspects of blended learning.

The thematic approach was used to identify patterns and themes in the data, which were then related back to Puentedura’s [ 34 ] SAMR model. This allowed for a comprehensive review of how AI is being used at different levels of integration in a specific hybrid learning environment. An inductive approach, specifically axial coding, was followed to analyse the data collected. This involved a systematic comparison of the gathered data to identify codes, categories, and subcategories. A natural analysis of the data, without preconceived notions, was achieved by using an inductive approach, which enabled an unbiased analysis of the lecturers’ actual experiences. Through this comparative analysis, the researchers aligned the collected data with information derived from the literature review. The adoption of these methodologies facilitated the analysis of findings, reinforcing the credibility and reliability of the data. The theoretical justifications for this approach included grounding the findings within the SAMR framework, to enable the data-analysis process to align with the study objectives and research questions throughout.

4.5 Trustworthiness in data collection and analysis

Ensuring the credibility and trustworthiness of research findings is the prerogative of every qualitative researcher. In this study, the researchers developed a lasting, reliable, and open relationship with the participants. This approach guaranteed the latter’s willingness to actively participate in the study, and to share their personal experiences of the impact which AI has on blended learning. Moreover, the lecturers were encouraged to review and offer feedback on the researchers’ summary of the interview responses, further confirming the accurate representation of all data, and strengthening the trustworthiness of the research.

The coding process for this study was primarily conducted by Researcher A who began the initial coding of the qualitative data, identifying preliminary themes and patterns. To enhance the reliability of the analysis, researcher B participated in the second phase, where both researchers reviewed and validated the initial codes and themes. This collaborative approach involved both open coding and axial coding and ensured a thorough and unbiased interpretation of the data. A critical reader provided feedback and suggestions, which helped refine the coding framework and resolve any discrepancies. This process promoted credibility by introducing different perspectives, which prevented individual prejudice and improved the accuracy of the data interpretation. Transparency was achieved by clearly documenting each researcher’s role and contributions, making the process open to scrutiny and validation by other future researchers. The method ensured the reliability and comprehensiveness of the data analysis and actual results.

The researchers adhered strictly to qualitative research principles, ensuring transparency in their data-collection methods and meticulousness in their data-analysis techniques. Participants were continually asked to check the researchers’ notes, interpretation of the interviews, and transcriptions (member checking). Detailed descriptions of the participants’ experiences were provided to enable the transferability of the findings. This precise approach guaranteed the reliability and validity of the findings. By integrating the findings from the interviews, document analysis, and literature review, the validity and trustworthiness of the conclusions were further enhanced. Through this methodological approach, the researchers ensured the trustworthiness of the research findings and were able to make informed recommendations based on the results reported on here.

4.6 Ethical issues

The chair of the department in which the research was undertaken, obtained comprehensive ethical clearance covering the entire department from the Research Ethics Review Committee of the College of Education of the particular HEI. This clearance authorises all researchers in the department to conduct research within the institution, under ethics clearance number 90060059MC.

In ensuring that the highest ethical standards were maintained, the researchers pledged to use codes to protect the identity and privacy of the participating lecturers. The lecturers were also required to give the researchers permission to record the interviews, and to analyse their module content, lessons, and assignments. They were explicitly informed that their participation was voluntary, and that they were free to withdraw from the study at any stage without fear of penalty.

4.7 Research findings

Here, the researchers summarise the outcomes of the research based on insights derived from the responses provided during the interviews with the participating lecturers, and the document analysis. The findings are organised to address the main research question and sub-questions.

4.8 Lecturers’ perceptions of incorporating AI technologies in their blended learning and teaching approach

Of the 15 participants interviewed, 12 reported using AI to ensure that student queries were answered, and that they could find additional information as required, thus personalising the entire academic journey. In the words of Lecturer H:

I use AI in my modules to ensure that students can easily obtain answers to their questions. It is an amazing tool which helps suggest supplementary resources based on students' progress. This ensures a learning experience which is better, as it is adapted to my students’ progress.

Lecturer C corroborated this:

These systems can answer questions, provide information, and simulate conversation, creating an amazing and enjoyable interactive environment.

The same 12 lecturers deemed AI very useful for facilitating discussions between lecturers and students, and students amongst themselves. This was achieved because AI streamlined communication, enhanced interaction, and provided valuable support. Lecturer F said:

AI has significantly improved communication channels; it allows me to develop interactive and engaging discussions between students and between students and myself, and even encourages students to discuss the course content amongst themselves.

Lecturer H concurred:

The use of AI chatbots has created a space for students to collaborate effectively. This offers immediate assistance and helps develop a sense of collaboration in our blended learning environment.

All the interviewees maintained that the use of AI to generate relevant and customised learning materials and assessments was a very useful feature that could easily be adopted in blended learning modules. In this regard, Lecturer C said:

I use AI to create customised learning materials, quizzes and even games that align with the specific learning outcomes of my modules.

Lecturer G stated:

I find that the fact that AI can create adaptive assessments that adjust difficulty levels based on the individual performance of my students, is very useful.

Five participants highlighted the value of AI for translation. This was considered extremely useful, particularly in the South African context with 11 official languages. Lecturer M explained:

The ability of AI to facilitate translation greatly benefits our students from diverse backgrounds. It is so easy for any of us [lecturers and students] to quickly translate a word or even a whole paragraph, which makes the understanding of the module so much easier.

Lecturer H added:

I find that it helps students who are more comfortable in their home language to participate in the course content. This ensures that learning materials are accessible to everyone, regardless of their language preference.

The researchers’ document analysis showed that lecturers who mentioned the benefits of AI for creating customised learning materials and adaptive assessments had indeed merged these elements into their module sites. This correlated with the findings obtained from the interviews, where 12 of the 15 participating lecturers highlighted the positive impact AI had on facilitating communication, enhancing interaction, and offering support in hybrid learning environments. In addition, the analysis revealed instances where AI tools were used to support F2F classes by providing real-time feedback and interactive activities, thus enriching the blended learning experience. Using technology to individualise learning experiences and adapt teaching strategies in real-time helps students adapt to such approaches, thereby supporting traditional teaching methods and enriches learning environments.

Lecturer C had integrated AI-generated, scenario-based case studies into the course material. The document analysis revealed a scenario related to cultural integration through language teaching and learning. Students were presented with a case study involving a classroom with learners from diverse linguistic and cultural backgrounds. They were tasked with designing a language lesson that not only focused on language acquisition, but also promoted cultural understanding and integration. AI was used to evaluate the students’ answers to the case study. Based on individual performance, the system provided feedback to each individual student, and suggested additional resources or challenges to focus on specific areas of improvement in designing the language lesson.

The document analysis (as outlined in the second criterion, which aimed to “examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes”) also ascertained the presence of adaptive assessments that were able to adjust complexity levels based on individual student performance. Lecturer G, who felt that AI was beneficial for creating such assessments, had incorporated quizzes with dynamic difficulty levels into the module site. Students were able to complete personalised assessments, with questions based on their previous performance.

The researchers noted the integration of AI-based translation services. Lecturer M, who highlighted the value of AI for translation, had implemented an AI-driven language translation tool on the module site. The researchers noted that some students had translated sections of the course content into their preferred language, promoting inclusivity and ensuring that the specific learning materials were clear to everyone, regardless of their language preference.

4.9 Lecturers’ perspectives on the challenges of incorporating AI into blended learning

Four of the lecturers interviewed, described the adaptation of new methods of teaching and learning, when using AI in their blended learning modules, as a challenge. In response to interview question 5 (What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?), Lecturer H commented:

Incorporating AI into my modules requires a delicate balance. I found that at times AI tends to minimise the importance of traditional teaching and learning methods, and not actually enhance them.

Lecturer B said:

Finding the right blend is crucial, so students benefit from the best of both worlds. AI must enrich my module and definitely not disrupt it … [We have to find] a balance between the technology and the personalised touch.

Lecturer C indicated:

… it can be a challenge to decide exactly where AI should be incorporated into the actual content of the course. Determining this often requires me to rethink my learning outcomes and approaches to teaching the content of my modules.

Twelve participants expressed the view that resistance to change was a major impediment to the successful adaptation of AI in blended learning modules. This aligns with responses to Interview Question 9 (In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?) where Lecturer G noted:

Change is always met with resistance, especially when it comes to technology, particularly amongst us older lecturers. Some may see AI as a threat to the traditional way of teaching.

Lecturer H stated:

There's a comfort in the familiar, and AI represents a significant shift. Overcoming resistance requires effective communication. It also requires practically exploring the uses and benefits of AI.

All the participants mentioned that, although AI definitely saved time, problems were experienced with finding additional time to investigate new technologies and adapt their modules accordingly. In the words of Lecturer A:

While AI streamlines certain processes, the challenge lies in actually finding dedicated time for exploring its full potential to ensure that AI helps both me and my students successfully achieve the outcomes of the specific module.

Lecturer G mentioned:

Despite the efficiency AI brings, we must confront the reality of time constraints. It is essential to find a balance between adopting new technologies and meeting existing teaching demands.

Eight participants mentioned that it was becoming increasingly challenging to cope with the problem of the “digital divide”, which pertains to the technological proficiency of the students. Lecturer E noted:

There's a noticeable difference in access to technology among our students, and it's becoming increasingly challenging for us lecturers to bridge this gap as a result of the fast pace of new technological developments.

Lecturer F concurred, adding:

The issue of unequal access is growing. We need effective strategies to ensure all students are given equal learning experiences, regardless of their experience using computers for actual learning.

Several lecturers discussed ethical and privacy-related challenges with regard to the integration of AI in their blended learning module. As Lecturer F indicated:

I find that a huge challenge is that of ethical considerations, especially with regard to the privacy of student data. Finding the correct balance between using AI and protecting our students' privacy is an ongoing challenge. Additionally, there's a need for clear guidelines from management on how AI should be used ethically in our teaching, to avoid unintended consequences.

Lecturer G opined:

The challenge lies in providing the benefits of using AI to achieve the outcomes of our modules without compromising the privacy rights of our students. Open discussions on ethical guidelines and continuous awareness among lecturers and management as well as lecturers and students [are] essential to overcoming these challenges successfully.

4.10 Lecturers’ perspectives on AI's impact on student engagement

All 15 participating lecturers noted that using AI in their blended learning modules was beneficial, but not all believed they were using AI to its full potential, admitting there was room for improvement. Lecturer F stated:

While AI has enhanced certain aspects of my lecturing and interaction with my students, I really feel that there's much further potential for the use of AI in my modules, especially with regard to the advanced AI functionalities and typing in the correct prompts.

Lecturer O opined:

Integrating AI into blended learning helps me improve the actual teaching of the content of my modules. This allows me to individualise the learning experiences of each of my students, to ensure that their needs and preferences are met.

Lecturer A agreed:

Using AI in my blended learning course helps me adapt to my students' needs. This makes the teaching and learning much more flexible and meaningful, as it allows me to develop an individualised teaching approach to each student's strengths and weaknesses.

Nine of the participants highlighted the significance of AI’s prompt feedback to the inputs provided and queries posted on the AI system. In response to interview question 6 ("Have you received any feedback from students regarding their experiences with AI-infused blended learning?"), Lecturer B mentioned,

The quick feedback of AI has really changed the learning experience. Students receive real-time feedback [on] their progress, allowing them to make [the] necessary changes immediately.

Lecturer K echoed this:

I see AI as a game changer. Its ability to offer instant, personalised feedback has been a real […] eye-opener. It helps students understand their strengths and weaknesses without delay. This helps ensure a more integrated and authentic learning environment. It helps in identifying gaps in understanding and adapting teaching strategies.

Lecturer N concurred, adding:

From where I stand, AI's ability to analyse student data can provide valuable insights for personalised teaching and learning, and allows for instantaneous feedback. As a result, students' entire learning process is enhanced, resulting in an improved ability to achieve their learning goals.

Lecturer B, who viewed the instant feedback of AI as beneficial for enhancing teaching and learning, had used AI to create scenario-based feedback activities. The document analysis identified instances where students were presented with virtual scenarios representing diverse language teaching situations, such as classroom settings, one-on-one tutoring sessions, and language immersion programmes. AI was able to instantaneously analyse students' responses and actions in each scenario, providing immediate, real-time personalised feedback on their answers. This integration of AI thus enhanced both asynchronous learning and synchronous F2F interactions, by offering immediate feedback during live sessions.

The interview responses of seven of the participants revealed that AI is able to easily automate administrative tasks, through machine learning algorithms and natural language processing. This analytical capability allows instructional approaches to be adapted to individual student needs, ensuring that they successfully attain the learning outcomes of the module. Lecturer C said:

AI tools can streamline administrative tasks, allowing me to devote more time to my students and support them, especially where they are encountering challenges.

Lecturer F added:

I've used AI to analyse student performance data, which helps me adapt the content of my modules and teaching methods to make them more interactive. This can easily be based on my individual students’ needs.

The document analysis, which aimed to examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes (the fourth criterion on the document analysis) also showed that modules where AI was integrated into feedback mechanisms saw improved student engagement. Studying the module site of Lecturer F, the researchers discovered that s/he used AI to automatically grade assignments (multiple-choice and written) and give immediate feedback. The reports generated were instantaneous and showed specific trends which helped the lecturer adapt the teaching and learning of this particular module.

It is indeed important to note how AI supports F2F teaching in class. As a result of this approach, learning during live lectures is made more dynamic and responsive to student needs. This point was highlighted by Lecturer M, who said:

The use of AI tools allows for instantaneous feedback to my students’ questions during lectures. It can give them various suggestions for additional materials and let them engage in interactive activities during face-to-face classes that will allow them to engage more deeply with the material.

4.11 Lecturers’ perspectives on the support they require to successfully implement AI in blended learning

All the participating lecturers confirmed the importance of comprehensive training and professional development. The need for comprehensive training and institutional support emerged as a critical theme. Interview Question 8 ("What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?") prompted responses highlighting the importance of ongoing professional development. In the words of Lecturer G:

Access to ongoing professional development courses focused on AI is essential for us lecturers to keep up to date with the latest developments in this field.

Lecturer M noted:

Professional development should include […] theoretical knowledge of AI as well as, specifically for us, its practical application in blended learning contexts.

Four participants stated that technological support was imperative if AI was to be instituted successfully. Lecturer O suggested:

Dedicated support teams must be specifically set up to assist with any technical challenges we may come across during the implementation of AI into our teaching and learning. This includes prompt responses to technical glitches and troubleshooting, to ensure that everything works properly for both me and my students.

We need assistance with initial setup and implementation, and with ongoing technical issues that may arise. This could be problematic as our IT help desk is already so overburdened. More IT staff definitely need to be employed.

Having institutional support for incorporating AI into the curriculum, is crucial. This involves not only providing resources, but also creating a culture that values and encourages the integration of AI technologies into teaching practices. This was echoed by all the lecturers interviewed. In the words of Lecturer A:

Having institutional support for incorporating AI into the curriculum is crucial. This involves providing resources as well as creating an institution that values and encourages the integration of AI into our teaching and learning.

Lecturer O echoed this:

Institutional commitment is key to the successful integration of AI. This should also include dedicated policies, so that we lecturers know exactly the correct process of AI.

Additionally, setting aside dedicated time for lecturers to adopt AI technologies was deemed imperative, as mentioned by ten of the participants. Lecturer H opined:

Allocating specific time for training and hands-on experience with AI tools is crucial. We need the opportunity to explore and familiarise ourselves with this new, exciting technology. This will definitely help us.

Lecturer E noted:

Having dedicated time for learning and experimentation is essential. This would give us more confidence in the actual implementation. But our schedules are already so busy that I have to wonder if this is at all possible.

Next, we examine the findings of the research.

5 Discussion of research findings

Using the research findings as a starting point for drawing meaningful conclusions and contributing to scholarly discourse on the subject, this section provides a summary of the findings that correlate with the literature review. From the utterances of many of the participants it became clear that there is a positive attitude towards AI, its significance for blended learning, and the benefits for tertiary students, as long as HEIs make certain adaptations. This aligns with the Redefinition and Modification aspects of the SAMR [ 34 ] model used for this study.

The research questions sought to explore how AI influences student engagement, interaction, and learning outcomes in blended learning environments. Lecturer N’s opinion, that AI boosts the learning process as a whole, resulting in an improved ability to successfully complete the course , is consistent with the findings of Alshahrani [ 3 ], Ferry et al. [ 13 ], Fradila et al. [ 14 ], Rahman et al. [ 37 ] and Santosa et al. [ 39 ], who found that infusing AI into a blended learning module enriches the learning process for students, helping them to achieve the specified learning outcomes. The collaborative and conversational capabilities of AI enhance the overall learning experience, leading to an enjoyment of the course, and active participation by students. These findings support the SAMR [ 34 ] model’s Redefinition level, where AI transforms the learning experience. Accordingly, the researchers of this study recognised that while AI does enhance learning experiences, its integration must be carefully managed to avoid over-reliance on technology at the expense of fundamental pedagogical principles.

The research findings corroborate the potential benefits AI holds for blended learning, as identified by the interviewees. Lecturer H's use of AI for immediate student support aligns with the views of Alsaleem and Alghalith [ 2 ], Alshahrani [ 3 ] and Lee [ 24 ], who emphasise AI’s capacity for personalising learning experiences. Moreover, Lecturer B's opinion on the importance of using AI for the prompt integration of AI-driven feedback, is consistent with the findings of Alshahrani [ 3 ] and Khosravi and Heidari [ 22 ], which emphasise AI’s functionality of supplying instantaneous feedback to enhance the learning experience. This aligns with the Augmentation level of the SAMR [ 34 ] model, where AI enhances existing teaching and learning practices. This made it clear to the researchers that while AI-driven feedback can significantly improve learning efficiency, it also raises concerns about data privacy and the need for transparent feedback mechanisms.

The views of Weber et al. [ 41 ]—that resistance to change may be an obstacle to the effective implementation of AI—are consistent with the opinions of 12 of the study participants. Specifically, Lecturer G noted that transformation is often met with resistance, especially when it comes to technology, and AI may be perceived as a risk to the conventional mode of teaching. Addressing this resistance requires policy interventions and professional development programs to ease the transition and encourage AI adoption. This indicated to the researchers that creating a culture of continuous improvement and gradually embracing this new approach may prevent resistance to adopting AI by lecturers and their higher education institutions.

The perspectives of all the participants, as regards the significance of tailored training and professional development which are customised to their specific needs, align with the findings of Luckin et al. [ 26 ]. According to that study, training should be more specific, and be contextually relevant to the unique demands and settings of the educational environment. This approach encourages active engagement and participation. Lecturer M specifically noted that any related training should focus mainly on its application to blended learning, to be successful. This highlights the importance of ongoing professional development to keep pace with technological advances. Clearly, HEIs need to adapt their policies to integrate AI tools that support personalised and interactive learning experiences. This suggested to the researchers that for AI technologies to be successful in higher education, professional development programmes must be made easily accessible for lecturers.

Finally, as featured in Alshahrani’s [ 3 ] study, the ethical use of AI in educational environments that adopt a blended learning approach, must be considered. Two participants (F and G) expressed the same sentiment, stating that open discussions on ethical guidelines and continuous dialogue among lecturers, management, and students are essential for navigating these issues. This suggests that policy should include ethical guidelines for AI use in education, ensuring that such integration supports not only academic integrity, but also responsible teaching and learning practices. In view of these findings, the researchers concluded that there was a distinct need for the creation of specific ethical frameworks that would assist all stakeholders to address the emerging ethical concerns associated with AI use in higher education institutions.

5.1 Limitations of the research

It is important to note the limitations of this study, which affect the generalisability of the findings. First, the study was restricted to a single South African higher HEI and one specific college, which may limit the applicability of the results to other contexts or institutions. Additionally, the full impact of AI on the blended learning approach may only become apparent in the future, as the students from this cohort progress in their careers and enter their respective professions. Furthermore, AI is a rapidly evolving field, and its continual advancements could mean that the study’s findings might become outdated relatively quickly. Finally, the successful implementation of AI in blended learning modules may be hindered by the lack of requisite technological resources and infrastructure in some educational institutions, potentially affecting the feasibility and effectiveness of AI integration.

6 Conclusion and recommendations

This paper discussed the impact of AI on a blended approach to teaching and learning in a particular HEI. It was based on the perceptions of 15 participating lecturers who lecture in the same college, albeit in different departments. The insights were based on the lecturers’ familiarity, experiences of, and involvement with, AI, and its impact on their teaching and learning. This positioned them to discuss the perceived advantages, disadvantages and supportive measures needed for such an approach to be successful. The use of focus group interviews and document analysis enabled the researchers to correlate what was actually taking place in this field of research, with the literature review undertaken.

Puentedura’s [ 34 ] SAMR model was chosen as theoretical framework to guide this undertaking, since it enabled the researchers to investigate how AI could bring about transformative changes in blended learning within the domain of higher education. The results highlight the significance of using AI in hybrid learning contexts, which has great potential for transforming traditional teaching methods. The study highlighted the implications of adopting AI to enhance the effectiveness of blended learning which offers personalised feedback, interactive discussions, and adaptive resources to cater to individual student needs. The findings draw attention to the crucial role of supportive measures such as management backing, improved training and professional development opportunities, reliable technological infrastructure, and improved internet connectivity, in ensuring the successful use of AI for blended learning modules. The findings thus enhance the knowledge base of this emerging field of study, by clarifying the perspectives of the lecturer participants at a particular HEI. Moreover, the findings can support future research on this topic, and may be used by other educational institutions—even those catering for different age groups.

6.1 Recommendations for further research

Recommendations for further research include several key areas to enhance the understanding and implementation of AI in blended learning environments. First, investigating AI and blended learning across various HEIs, both within South Africa and internationally, would provide a more comprehensive understanding of lecturers' perceptions of AI's impact. Additionally, research should focus on the effect of AI on students’ achievement of learning outcomes, their engagement with modules, and their overall enjoyment of learning within hybrid environments. Examining specific support measures, particularly relevant training, could further assist lecturers in effectively integrating AI into their modules. Longitudinal studies are also recommended to track changes in lecturers’ perceptions as they adapt to and integrate AI over time. A thorough exploration of the challenges HEIs face during the implementation process should be considered to address potential barriers. Furthermore, research into the ethical implications of AI in education, including the development of necessary guidelines, is essential. Finally, future studies should aim to validate and expand upon these findings using quantitative methods, as this study was purely qualitative.

Data availability

The data that support the findings of this study are not openly available due to the privacy and confidentiality agreements with the participants. However, the data will be made available by the corresponding author upon reasonable request, subject to review and approval by the research ethics committee of the involved institution. Requests for data access can be made by contacting the corresponding author at [email protected].

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The authors acknowledge the cooperation of the lecturers who participated in the data-collection process, and the HEI under study, for allowing the research to be conducted.

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1.1 Appendix A: Interview question

The following is the set of open-ended interview questions the researchers used by the researchers to assess the lecturer’s view of the impact of AI on blended learning:

Can you describe your experience incorporating AI technologies into your blended learning lessons?

What specific AI technologies or tools have you used in your blended learning approach?

Can you share examples of instances where AI enhanced the effectiveness of your blended learning lessons?

In your opinion, what are the key advantages of integrating AI into blended learning?

What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?

Have you received any feedback from students regarding their experiences with AI-infused blended learning?

Have you noticed any differences in student performance or understanding between traditional and AI-infused blended learning?

What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?

In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?

1.2 Appendix B: document analysis guide

The researchers used the following guidelines when analysing the module contents, lessons and assessments:

Assess whether the content and learning objectives of the module feature the integration of AI technologies ─ look for objectives that explicitly mention the use of AI to enhance specific skills or competencies.

Identify specific occurrences where AI enhances interactivity within lessons.

Look for evidence that assessments capture the unique contributions of AI to student learning outcomes.

Search for features that assist in the immediacy and effectiveness of feedback mechanisms through AI.

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Sanders, D.A., Mukhari, S.S. Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution. Discov Educ 3 , 135 (2024). https://doi.org/10.1007/s44217-024-00235-2

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• Artificial Intelligence (AI)
• Blended learning
• Face-to-face learning
• Online learning
• Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model

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