When doing a research action plan students in school would know that the first thing to do is to know your topic well enough. From expecting science projects to work based on your predictions and the results that may have been quite the opposite from how you depicted them. This also rings true in businesses. There is a term for that and it is often associated with the subject Science, but can also be associated with business . Scientific method or a hypothesis.
A hypothesis is a scientific wild guess, a prediction in research . A wild guess, a say from someone without any known proof. A hypothesis can also mean a scientific, educated guess that most scientists and researchers do before planning out or doing experiments to check if their guesses or their scientific ideas based on their topics are exact or correct.
A well-structured hypothesis is crucial for guiding scientific research. Here’s a detailed format for writing a hypothesis, along with examples for each step:
Before writing a hypothesis, begin with a clear and concise research question . This question identifies the focus of your study.
Example Research Question: Does the amount of daily exercise affect weight loss?
Identify the independent and dependent variables in your research question.
Use the identified variables to create a testable statement . This statement should clearly express the expected relationship between the variables.
Research question: does caffeine affect cognitive performance, if-then statement:.
Non-directional hypothesis:.
Ensure that your hypothesis is specific, measurable, and testable. Avoid vague terms and focus on a single independent and dependent variable.
A hypothesis is a statement that predicts the relationship between variables. It serves as a foundation for research by providing a clear focus and direction for experiments and data analysis . Here are examples of hypotheses from various fields of research:
Does sunlight exposure affect plant growth?
Does sleep duration affect memory retention?
Do interactive teaching methods improve student engagement?
Does a new drug reduce blood pressure more effectively than the standard medication?
Does socioeconomic status affect access to higher education?
Psychology research often explores the relationships between various cognitive, behavioral, and emotional variables. Here are some well-structured hypothesis examples in psychology:
Does regular exercise reduce anxiety levels?
Does social media usage affect self-esteem in teenagers?
Is Cognitive Behavioral Therapy (CBT) effective in reducing symptoms of depression?
Does parental involvement influence academic achievement in children?
Scientific research often involves creating hypotheses to test the relationships between variables. Here are some well-structured hypothesis examples from various fields of science:
Does temperature affect the rate of a chemical reaction?
Does the mass of an object affect its speed when falling?
Do chemical fertilizers affect water quality in nearby lakes?
Does soil composition affect the rate of erosion?
In biology, hypotheses are used to explore relationships and effects within biological systems. Here are some well-structured hypothesis examples in various areas of biology:
How does light intensity affect the rate of photosynthesis in plants?
How does temperature affect the activity of the enzyme amylase?
Does the availability of nutrients in soil affect the growth of plants?
Does genetic variation in a population affect its resistance to diseases?
Does the pH level of water affect the health of aquatic life?
In sociology, hypotheses are used to explore and explain social phenomena, behaviors, and relationships within societies. Here are some well-structured hypothesis examples in various areas of sociology:
Does access to higher education affect social mobility?
Does income inequality influence crime rates in urban areas?
Does the use of social media affect face-to-face social interactions among teenagers?
Do traditional gender roles influence career choices among young adults?
Does cultural diversity in the workplace affect productivity levels?
1. research hypothesis.
A hypothesis is a statement that can be tested and is often used in scientific research to propose a relationship between two or more variables. Understanding the different types of hypotheses is essential for conducting effective research. Below are the main types of hypotheses:
The null hypothesis states that there is no relationship between the variables being studied. It assumes that any observed effect is due to chance. Researchers often aim to disprove the null hypothesis.
Example: There is no significant difference in test scores between students who study with music and those who study in silence.
The alternative hypothesis suggests that there is a relationship between the variables being studied. It is what researchers seek to prove.
Example: Students who study with music have higher test scores than those who study in silence.
A simple hypothesis predicts a relationship between a single independent variable and a single dependent variable.
Example: Increasing the amount of sunlight will increase the growth rate of plants.
A complex hypothesis predicts a relationship involving two or more independent variables and/or two or more dependent variables.
Example: Increasing sunlight and water will increase the growth rate and height of plants.
A directional hypothesis specifies the direction of the expected relationship between variables. It suggests whether the relationship is positive or negative.
Example: Students who study for more hours will score higher on exams.
A non-directional hypothesis does not specify the direction of the relationship. It only states that a relationship exists.
Example: There is a difference in test scores between students who study with music and those who study in silence.
A statistical hypothesis involves quantitative data and can be tested using statistical methods. It often includes both null and alternative hypotheses.
Example: The mean test scores of students who study with music are significantly different from those who study in silence.
A causal hypothesis proposes a cause-and-effect relationship between variables. It suggests that one variable causes a change in another.
Example: Smoking causes lung cancer.
An associative hypothesis suggests that variables are related but does not imply causation.
Example: There is an association between physical activity levels and body weight.
A research hypothesis is a broad statement that serves as the foundation for the research study. It is often the same as the alternative hypothesis.
Example: Implementing a new teaching strategy will improve student engagement and performance.
A hypothesis is a critical component of the research process, providing a clear direction for the study and forming the basis for drawing conclusions. Here’s a step-by-step guide on how to use a hypothesis in research:
Before formulating a hypothesis, clearly define the research problem or question. This step involves understanding what you aim to investigate and why it is significant.
Example: You want to study the impact of sleep on academic performance among college students.
Conduct a thorough review of existing literature to understand what is already known about the topic. This helps in identifying gaps in knowledge and forming a basis for your hypothesis.
Example: Previous studies suggest a positive correlation between sleep duration and academic performance but lack specific data on college students.
Based on the research problem and literature review, formulate a clear and testable hypothesis. Ensure it is specific and relates directly to the variables being studied.
Clearly define the independent and dependent variables involved in the hypothesis.
Choose an appropriate research design to test the hypothesis. This could be experimental, correlational, or observational, depending on the nature of your research question.
Example: Conduct a correlational study to examine the relationship between sleep duration and GPA among college students.
Gather data through surveys, experiments, or secondary data sources. Ensure the data collection methods are reliable and valid to accurately test the hypothesis.
Example: Use a questionnaire to collect data on students’ sleep duration and their GPAs.
Use appropriate statistical methods to analyze the data. This step involves testing the hypothesis to determine whether to accept or reject the null hypothesis.
Example: Perform a Pearson correlation analysis to examine the relationship between sleep duration and GPA.
Interpret the results of the statistical analysis. Determine if the data supports the alternative hypothesis or if the null hypothesis cannot be rejected.
Example: If the analysis shows a significant positive correlation, you can reject the null hypothesis and accept the alternative hypothesis that sleep duration is related to academic performance.
Draw conclusions based on the results of the hypothesis testing. Discuss the implications of the findings and how they contribute to the existing body of knowledge.
Example: Conclude that longer sleep duration is associated with higher GPA among college students and discuss potential implications for student health and academic policies.
Write a detailed report or research paper presenting the hypothesis, methodology, results, and conclusions. Share your findings with the academic community or relevant stakeholders.
Example: Publish the study in a peer-reviewed journal or present it at an academic conference.
Writing a hypothesis is a crucial step in the scientific method. A well-constructed hypothesis guides your research, helping you design experiments and analyze results. Here’s a step-by-step guide on how to write an effective hypothesis:
Start by clearly understanding the research question or problem you want to address. This helps in formulating a focused hypothesis.
Example: How does sunlight exposure affect plant growth?
Review existing literature related to your research question. This helps in understanding what is already known and identifying gaps in knowledge.
Example: Studies show that plants generally grow better with more sunlight, but the optimal amount varies.
Determine the independent and dependent variables for your study.
A simple hypothesis involves one independent and one dependent variable. Clearly state the expected relationship between these variables.
Example: Increasing sunlight exposure will increase plant growth.
Decide whether your hypothesis will be null or alternative, directional or non-directional.
Example of Directional Hypothesis: Plants exposed to more sunlight will grow taller than those exposed to less sunlight.
Make sure your hypothesis can be tested through experiments or observations. It should be measurable and falsifiable.
Example: Plants will be grown under different levels of sunlight, and their growth will be measured over time.
Write your hypothesis in a clear, concise, and specific manner. It should include the variables and the expected relationship between them.
Example: If plants are exposed to increased sunlight, then they will grow taller compared to plants that receive less sunlight.
Ensure that your hypothesis is specific and narrow enough to be testable but broad enough to cover the scope of your research.
Example: If tomato plants are exposed to 8 hours of sunlight per day, then they will grow taller and produce more fruit compared to tomato plants exposed to 4 hours of sunlight per day.
To formulate a hypothesis, identify the research question, review existing literature, define variables, and create a testable statement predicting the relationship between the variables.
The null hypothesis (H0) states there is no effect or relationship, while the alternative hypothesis (H1) proposes that there is an effect or relationship.
A hypothesis provides a clear focus for the study, guiding the research design, data collection, and analysis, ultimately helping to draw meaningful conclusions.
A hypothesis cannot be proven true; it can only be supported or refuted through experimentation and analysis. Even if supported, it remains open to further testing.
A good hypothesis is clear, concise, specific, testable, and based on existing knowledge. It should predict a relationship between variables that can be measured.
A hypothesis is tested through experiments or observations, collecting and analyzing data to determine if the results support or refute the hypothesis.
Types of hypotheses include null, alternative, simple, complex, directional, non-directional, statistical, causal, and associative.
A directional hypothesis specifies the expected direction of the relationship between variables, indicating whether the effect will be positive or negative.
A non-directional hypothesis states that a relationship exists between variables but does not specify the direction of the relationship.
Refine a hypothesis by ensuring it is specific, measurable, and testable. Remove any vague terms and focus on a single independent and dependent variable.
Text prompt
10 Examples of Public speaking
20 Examples of Gas lighting
Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.
In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.
Table of Content
Hypothesis meaning, characteristics of hypothesis, sources of hypothesis, types of hypothesis, simple hypothesis, complex hypothesis, directional hypothesis, non-directional hypothesis, null hypothesis (h0), alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis, hypothesis examples, simple hypothesis example, complex hypothesis example, directional hypothesis example, non-directional hypothesis example, alternative hypothesis (ha), functions of hypothesis, how hypothesis help in scientific research.
A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.
A hypothesis is a proposed statement that is testable and is given for something that happens or observed.
Here are some key characteristics of a hypothesis:
Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:
Here are some common types of hypotheses:
Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.
Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.
Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.
Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.
Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.
Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.
Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.
Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.
Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.
Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.
Following are the examples of hypotheses based on their types:
Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
Mathematics Maths Formulas Branches of Mathematics
A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.
The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .
The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.
What is a hypothesis.
A guess is a possible explanation or forecast that can be checked by doing research and experiments.
The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.
Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis
You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data
Yes, you can change or improve your ideas based on new information discovered during the research process.
Hypotheses are used to support scientific research and bring about advancements in knowledge.
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BMC Medical Education volume 24 , Article number: 678 ( 2024 ) Cite this article
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The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning in a clinical setting, and decision-making skills of nursing students.
This study was conducted at Shahid Sadoughi University of Medical Sciences in 2021–2023. The participants were 151 nursing students who studied their apprenticeship courses in the teaching hospitals. The three basic components of the cognitive load model, including working memory, cognitive load, and decision-making as the outcome of learning, were investigated in this study. Wechsler’s computerized working memory test was used to evaluate working memory. Cognitive Load Inventory for Handoffs including nine questions in three categories of intrinsic cognitive load, extraneous cognitive load, and germane cognitive load was used. The clinical decision-making skills of the participants were evaluated using a 24-question inventory by Lowry et al. based on a 5-point scale. The path analysis of AMOS 22 software was used to examine the relationships between components and test the model.
In this study, the goodness of fit of the model based on the cognitive load theory was reported (GIF = 0.99, CFI = 0.99, RMSEA = 0.03). The results of regression analysis showed that the scores of decision-making skills in nursing students were significantly related to extraneous cognitive load scores ( p -value = 0.0001). Intrinsic cognitive load was significantly different from the point of view of nursing students in different academic years ( p = 0.0001).
The present results showed that the CLT in workplace-based learning has a goodness of fit with the components of memory, intrinsic cognitive load, extraneous cognitive load, and clinical decision-making skill as the key learning outcomes in nursing education. The results showed that the relationship between nursing students’ decision-making skills and extraneous cognitive load is stronger than its relationship with intrinsic cognitive load and memory Workplace-based learning programs in nursing that aim to improve students’ decision-making skills are suggested to manage extraneous cognitive load by incorporating cognitive load principles into the instructional design of clinical education.
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Cognitive load was introduced as a key theory in medical education [ 1 ] This theory guides the components of human cognitive architecture concerning learning and education to create a correct understanding of the characteristics and conditions of education and learning [ 2 ].
The CLT was first proposed in the 1980s by John Sweller [ 3 ]. This theory explains learning according to three important aspects including types of memory (working and long-term memory), learning process, and forms of cognitive load that affect learning [ 4 ].
The cognitive architecture assumed by CLT includes long-term memory (LTM) and working memory (WM). The key subsystem of memory in the CLT is working memory [ 5 ].
Cognitive load is defined as the load that a specific task imposes on the learner’s cognitive system [ 6 ]. In the CLT, three types of cognitive load are proposed, including intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) [ 7 , 8 ]. ICL is related to the complexity of educational materials rather than their quantity [ 9 ]. ICL depends on several factors, including the individual’s skill, the number of information elements, and the degree of interaction of different elements of the tasks. ECL caused by the training format includes training strategies, training design, and teaching-learning methods [ 4 , 10 , 11 ]. GCL refers to the load imposed by the mental processes necessary for learning (such as the formation of schemata) [ 11 ]. Germane load means trying to build and modify learning schemata, which is mainly under the control of job components such as motivation, effort, and the learner’s metacognitive skills [ 7 ]. Also, the level of learner’s proficiency can moderate the ICL arising from the interaction of elements. This means that the availability and automaticity of the learner’s schemata can moderate intrinsic load [ 11 ].
Education in medical science systems is a complex and multidimensional process that is affected by many factors [ 12 ]. In the process of clinical education, students need to learn several professional tasks and activities and apply them in the provision of health care services by simultaneously integrating a set of knowledge, skills, and behaviors [ 11 ]. These characteristics of clinical education can impose a high cognitive load on students and harm their effective learning [ 11 , 13 ].
The CLT has emerged as one of the foremost models in educational psychology considered in different fields such as health professions education. The goal of CLT has been to improve learning at the individual student level in different environments including the classroom, and complex professional learning environments [ 14 ]. Sweller and colleagues showed there have been main developments in CLT and instructional design over the last 20 years. The ‘cognitive theory of multimedia learning’ focusing on the design of multimedia educational materials and the ‘four-component instructional design (4 C/ID)’ focusing on the design of whole-task courses and curricula have been built based on the CLT [ 15 ]. In addition, the CLT provides principles that are recommended to apply to the design of instructional messages and instructional units, such as lessons, written materials consisting of text and pictures, and educational multimedia (instructional animations, videos, simulations, games) [ 15 ].
The theoretical scope of the cognitive load has been expanded by including the physical environment as a key factor affecting cognitive load. Physical environments that evoke stress, emotions, and/or uncertainty raise new questions about how to deal with cognitive load. The questions require examining the human cognitive architecture of educational design in environments that are accompanied by uncertainty and stress [ 15 ]. Likewise, Paas et al. (2020) introduced variables affecting cognitive load and introduced factors including instructional design and learning environment as an effective factor that affects students’ learning process. They stated that the learning environment can affect cognitive load and suggested a way of managing it [ 5 ].
Advances in CLT have set the trends for future developments in different learning environments such as workplace-based learning, simulation, and games [ 15 ]. Most studies used the CLT principles in instructional design in simulation, virtual reality, and game settings in nursing education [ 1 , 16 , 17 , 18 ]. Yiin et al., (2023) indicated the multi-media interactive learning materials and an active learning mechanism reduced nursing students’ intrinsic and extrinsic cognitive load and encouraged the students to learn [ 19 ]. Takhdat et al., (2024) showed that mindfulness meditation practice optimizes cognitive load, and decreases the anxiety of nursing students in a simulation setting [ 20 ].
Clinical education in the workplace is defined as a main educational setting where students improve their competencies and prepare for their future careers. Sewell and colleagues (2019) in a BEME guide (Best Evidence in Medical Education guide) discussed cognitive load in workplace-based learning in the real environment [ 21 ]. The workplace-based learning in clinical education imposes high levels of cognitive load that negatively impact on learning of learners and their performances. Sewell et al. indicated the factors of, complex tasks, settings, and novice learners mostly predispose the students to high levels of cognitive load. They stated aspects of workplace environments contribute to extraneous load, and adversely impact capacity for engaging in tasks that enhance germane load and learning [ 15 ]. Further studies are recommended to understand the manner and the extent of the impact of cognitive load on different learning outcomes in various learning environments in systems of health professions education [ 1 , 16 , 17 ].
The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning, and decision-making skills of nursing students in clinical settings.
This cross-sectional study was conducted in 2021–2023 at Shahid Sadoughi University of Medical Sciences, Yazd, Iran. In the present study, the path analysis was used to predict a defined theoretical model that posits hypothesized linear relations among variables and decreases to the solution of one or more multiple regression analyses.
The present university has conducted a four-year nursing degree curriculum. The students have participated in workplace-based learning in the clinical setting from the second semester. They contributed to care processes as team members from the third semester of the academic course. In the present nursing curriculum, there is no reasoning and decision-making training course. The decision-making skills have been learned by the students in the process of workplace-based learning in the clinical environment. The stages of experiential learning, including observation, practice and repetition, feedback, and self-reflection, have been implemented in the nursing clinical education program. In clinical education courses, the students have used study guides nursing flowcharts, and clinical guidelines.
Undergraduate nursing students of the faculties affiliated with Shahid Sadoughi University of Medical Sciences participated in this study. The inclusion criteria were nursing students who had completed at least six months of apprenticeship courses in their field in the hospital. Students with working experience as health technicians ( Behvarz ) were excluded from the study. This exclusion criterion aims to control for potential confounding variables that could influence the study’s outcomes, such as previous professional experience impacting cognitive load assessments and decision-making skills [ 22 , 23 ].
The rule of thumb is to have at least 10–15 observations per parameter (i.e., 10–15 cases for each independent variable and the dependent variable) to have reliable estimates of the model parameters [ 24 ]. Thus, a total of 151 eligible students were randomly selected in this study.
To conduct the examination, the researcher explained the objectives of the research, the instruments of data collection, the duration of the examinations, and the confidentiality of data. The participants were asked to perform the Wechsler computerized working memory test and fill the Questionnaires of Cognitive Load Inventory for Handoffs and Clinical Decision-making in a calm environment and away from disturbing side factors. The informed consent form was completed by the students.
Working memory measurement tool: Wechsler’s computerized working memory test was used to evaluate working (active) memory [ 25 , 26 ]. In this test, two sections of forward and backward recall of digits are used to measure the memory span. The total working memory score is obtained from the sum of the scores of the two parts of forward and backward recall with a maximum score is 28. For the correct evaluation of the subject, the soft table is used for the desired ages. In this software, the score of memory span (auditory and visual) is also provided. This score represents the number of items memorized by the examinee.
The cognitive Load Inventory for Handoffs (CLIH) was compiled by Yang et al., (2016) [ 27 ] to assess the cognitive load of students in their clinical education. The questionnaire includes 9 questions in three domains of ICL, ECL, and GCL which is based on a 10-point Likert. The validity of the tool was confirmed in the present study. The qualitative content validity of the Persian version of the questionnaire was confirmed from the viewpoints of 15 experts. To determine content validity quantitatively, two indices “Content Validity Ratio (CVR)” and “Content Validity Index (CVI)” were used. The findings of the quantitative content validity assessment indicated that the CVR for all items was higher than the minimum acceptable value (= 0.49), and the CVI values of all items were above 0.79. According to the indices, all items were kept in the questionnaire. S-CVI/Ave was 0.94, which was desirable. The internal consistency of the tool was reported as Cronbach’s alpha coefficient = 0.86.
Clinical decision-making as a learning outcome of nursing students in clinical education was evaluated using the 24-item questionnaire designed by Lauri et al. (2001) which is based on a 5-point scale [ 12 ]. The reliability and validity were confirmed in the Karimi et al. study (2013) (Cronbach’s alpha coefficient of intrinsic consistency = 0.8) [ 28 ].
Demographic information of the participants (including gender, age, level of education, and the last externship/internship period of the students) was collected. Descriptive statistics (including frequency percentage, mean, and standard deviation) and analytical statistics (ANOVA) were used to investigate the variables. SPSS statistical software (Ver. 24) was used for data analysis.
This study employed path analysis as the primary statistical analysis method due to its ability to examine the relationships between multiple variables, including the direct and indirect effects of predictor variables on the outcome variable. Specifically, path analysis was used to investigate the relationships between memory, internal and external cognitive load, and decision-making skill, as well as the indirect effects of these variables on learning outcomes. Moreover, path analysis is suited for examining the relationships among the variables in this study due to the capability of path analysis to handle complex models and multiple relationships simultaneously. The use of path analysis was further justified by the need to examine the causal relationships between variables, as well as to account for measurement error and unexplained variance in the data. Path analysis allows for the estimation of standardized regression coefficients, which can be used to interpret the magnitude and direction of the relationships between variables.
In terms of model evaluation, this study employed several indices to assess the goodness-of-fit of the proposed model. The goodness-of-fit index (GFI) was also used to evaluate the model’s fit relative to a baseline model, with a value of 0.95 or higher indicating a good fit [ 29 ]. In addition, acceptable levels of indices of the path analysis include Adjusted Goodness-of-Fit Index (AGFI) > 0.8, Tucker-Lewis Index (TLI) > 0.9, the Incremental Fit Index (IFI) > 0.8. Regarding the Comparative Fit Index (CFI) with a value of greater than 0.90 is very good fit, 0.80 to 0.89 is adequate but marginal fit, 0.60 to 0.79 is poor fit, a and lower than 0.60 very poor fit. Finally, the root mean square error of approximation (RMSEA) was used to evaluate the model’s fit to the data, with a value of 0.05 or less indicating a good fit [ 30 ]. These results indicate that the proposed model provided an adequate representation of the relationships among the variables studied. In the present study, AMOS 22 software was used to assess the fitness of this model.
In total, 151 nursing students participated in this study, 77 of them (51%) were women and 74 (49%) were men. The mean age of the participants was 21.97 ± 2.20. The demographic information of the participants is shown in Table 1 .
The mean score of decision-making of nursing students was 78.37 ± 11.30 and the mean score of cognitive load perceived by students in the workplace-based learning process of clinical setting was 45.26 ± 8.84. Table 2 shows the mean score of the students in the studied variables.
The results of regression analysis showed that the students’ scores of nursing students in decision-making skills were significantly related to the ECL scores ( P = 0.0001). By increasing one ECL score, the score of students’ clinical decision-making skill increased by 1.2.
The mean scores of ICL and ECL of the students according to their academic year are reported in Table 3 . ANOVA showed that ICL was significantly different from the point of view of nursing students in different academic years ( P = 0.0001). The results of the Bonferroni test showed that ICL in novice (second-year) students was significantly lower than in third-year ( P = 0.0001) and fourth-year students ( P = 0.004). Figure 1 illustrate the path analysis model of CLT in the workplace-based leaning. Table 4 show a report of indices of goodness-of-fit in the model.
Path analysis model: standardized coefficient estimates
ICL: Intrinsic cognitive load, ECL: Extraneous cognitive load
The current study reported a statistically significant fit for the proposed path analysis model indicating a good fit in the data collected from the nursing students in the workplace-based learning at clinical setting.
The development of clinical decision-making skills is a main competency of nursing students in clinical education courses. Learning the decision-making skill is considered a complex and multi-dimensional process that is influenced by various factors for instance personal features, task experience, and situational awareness ability [ 22 ]. Moreover, educational factors such as instructional design, learning environments, and teaching methods direct the cognitive load and learning process of students [ 12 , 31 ]. The present results showed that nursing students’ decision-making skills have a significant positive relationship the capacity of the working memory of learners and ECL in workplace-based learning environments. In line with our results, the findings of studies confirmed management of ECL that depended on the characteristics of the instructional material, the instructional design, and the prior knowledge of learners in the process of clinical education have a positive relationship with learning [ 5 , 21 , 23 ]. The effect of cognitive load as a mediating relationship on clinical reasoning as the key outcome of learning was shown in the Jung et al. study (2022) [ 32 ]. In a review, Josephsen et al. (2015) showed that there is a positive relationship between the cognitive architecture of learners and educational design in nursing. Their results indicated that learners must be aware of cognitive architecture and educational processes in nursing to manage cognitive load and effective learning [ 16 ].
The present results showed that the decision-making scores of the nursing students had a significant positive relationship with ECL in workplace-bead learning. The students have experienced the experiential learning process in clinical nursing education. They learned through observing, exercising, receiving feedback, and reflecting in action and on action at the workplace-based learning in the clinical setting. In addition, nursing students used supportive resources such as a nursing flowchart, a study guide, and structured constructive feedback in clinical education. The use of CLT principles in the instructional design of workplace-based learning of nursing clinical education effects on the ECL. Many learning tasks, especially complex clinical activities, require memorizing and applying a large amount of information [ 11 ]. According to the CLT, the educational environment provides a trigger to use the information stored in LTM to determine the appropriate action in the environment according to the environmental-and-organizing linking principle. Moreover, specialized performance is developed through the creation of a large number of more complex schemata by combining elements consisting of lower-level schemata with higher-level schemata [ 5 ]. The schemata facilitate the decision-making process. The significant relationship between the ECL and learning has also been confirmed in the study of Sawicka et al. (2008) [ 9 ]. The application of strategies for ECL management is recommended by Sawicka et al. The tailored strategies with the workplace-based education were conducted in the clinical setting. These strategies include presenting educational materials from simple to complex and presenting familiar examples in the experiential learning process in clinical setting. The students were experienced the nursing care plan form simple cases to complex cases. The supplementary questions and diverse assignments were conducted in the clinical education by students. They experienced self-explanatory and supporting information in the feedback and reflection process. The use of the strategies in the clinical education of the nursing students in workplace-based learning may effect on our findings. Similarly, Skulmowski et al., (2022) acknowledged the use of aspects of constructive alignment, a strategy to balance the cognitive load and an approach of fostering deep forms of learning improved the learning outcomes [ 33 ].
In the CLT, the features of working memory including its capacity and time limitations were introduced as a key component that plays an important role in learning. This issue is emphasized in cognitive models [ 5 , 34 ]. The present results showed that the relationship between memory and ICL is stronger than ECL. Kilic et al. (2010) model indicated that working memory plays an effective role in providing information necessary for complex cognitive activities such as learning and clinical reasoning [ 34 ]. So, if the learning material is too difficult, the ICL imposed on learners may exceed their working memory capacity and hinder learning [ 9 ]. In line with our results, the relationship of working memory with ICL was stranger than ECL. Sawicka (2008) stated that the insufficiency of working memory resources to expand schemata hinders learning [ 9 ].
The present results showed that the fit of the model was favorable by considering working memory scores, cognitive load, and learning, but no significant relationship was observed between working memory and decision-making scores. Also, no significant relationship between ICL and learning was observed in the present study. In line with our results, Szulewski et al., (2021) presented a new model for medical education systems based on CLT. They stated the relationship between the working memory of healthcare workers cannot be discussed directly in the model. They expressed this as a limitation of their model and acknowledged that the capacity of working memory in complex medical education systems is affected by stress, emotions, and uncertainties, which can affect the performance of healthcare workers [ 14 ]. Although the significant relationship between the components was not approved in the present study, the good fit of the proposed path analysis model, indicated that these components interact with each other and require consideration as a coherent structure in instructional design of the workplace-based learning by planners.
Emotions, stress, and uncertainty are integrated with the learning process and environment in the educational systems of health professions. The educational systems of health professions integrate emotions, stress, and uncertainty into the learning process and environment. According to Sweller, emotions that are considered undesirable for learning result in extraneous load that can be reduced by preventing them. If emotion, stress, and uncertainty are seen as an integral element of the task that learners require to learn, they contribute to intrinsic cognitive load and must be dealt with in another way. Therefore, it is necessary to consider multi-faceted planning by using different components and systematically examining different aspects of cognitive load before formulating educational designs for workplace-based learning in the clinical setting [ 5 ].
Garvey et al. study (2017) introduced a model in which, in addition to the cognitive load components, the individual maturity component based on the years of education was also included in the model [ 35 ]. In the present study, individual maturity was considered in different academic years. The present results showed that there is a significant relationship between the learning maturity of individuals and ICL components. ICL is related to the complexity caused by training and depends on factors such as the individual’s skill, the number of information elements, and the degree of interaction of elements in the learning process. Our findings indicated the ICL of the second-year students was significantly lower compared to the third-year and the fourth-year. The results can be due to less work experience in the hospital, the smaller amount of material learned, and dealing with the limited clinical complexities of the students in the second year. Sewell’s results confirmed a negative relationship between GCL and ICL with the level of experience and performance of students [ 21 ]. These results were also aligned with the present results. Our results are in contrast to Schlairet’s findings (2015) which indicated that a negative relationship between the performance of novice nursing students and cognitive load was observed, although this relationship was not significant [ 36 ]. The difference in the level of students and the difference in the measured learning outcome (decision-making skills versus performance) and considering the cognitive load score without separating ICL and ECL can affect the results.
The results showed that the current model does not have a good fit considering the GCL. The current limitation can be due to measuring the GCL using only one question in CLIH [ 27 ]. Measuring the GCL as a mental process of learning is difficult and requires the measurement of supporting components such as motivation, effort, and metacognitive skills [ 7 ]. In a meta-analysis, Lapierre (2022) found that cognitive load measurement is one of the concerns of studies in the field of CLT. He stated that appropriate tools and the use of self-expression are among the concerns of studies in this field [ 1 ]. Therefore, it is recommended to use different tools to measure the desired cognitive load component in future studies [ 5 , 17 ]. Moreover, it is suggested that influential components such as factors affecting the GCL, learning maturity, and educational strategies should be taken into consideration in future studies.
CLT is a key theory in the purposeful guidance of the process of education, which can guide the educational processes to more effective learning in medical science education systems. The current results showed that CLT had a good fit with the components of memory, ICL, ECL, and clinical decision-making as the key learning outcomes in workplace-based learning in clinical settings. The results showed that the relationship between nursing students’ decision-making skills and extraneous cognitive load is stronger than its relationship with intrinsic cognitive load and memory. Workplace-based learning programs in nursing that aim to improve students’ decision-making skills are suggested to manage extraneous cognitive load by incorporating cognitive load principles into the instructional design of clinical education.
The datasets generated and/or analyzed during the current study are not publicly available due to the confidentiality of the data of participants but are available from the corresponding author at reasonable request.
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The authors appreciate the cooperation of Amir Houshang Mehrparvar. We would like to thank all the participants for their contribution.
The Shahid Sadoughi University of Medical Sciences, Yazd, Iran funded this project (ID: 16139). The grant supported the data collection process. The funders had no role in the design of the study and collection, analysis, interpretation of data, or preparation of the manuscript. The report of the study’s findings is sent by the authors to the funder at the end of the study.
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F.K. and SH.T. conceptualized and designed the study and SH.T. collected the data. S.J analyzed the data. F.K. and SH.T wrote the main manuscript text. The authors have met the criteria for authorship and had a role in preparing the manuscript. Also, all authors approved the final manuscript.
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Tabatabaee, S.S., Jambarsang, S. & Keshmiri, F. Cognitive load theory in workplace-based learning from the viewpoint of nursing students: application of a path analysis. BMC Med Educ 24 , 678 (2024). https://doi.org/10.1186/s12909-024-05664-z
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Within the framework of the theory of visual attention (TVA), the visual attention span (VAS) deficit among individuals with developmental dyslexia has been ascribed to the problems entailed by bottom-up (BotU) and top-down (TopD) attentional processes. The former involves two VAS subcomponents: the visual short-term memory storage and perceptual processing speed; the latter consists of the spatial bias of attentional weight and the inhibitory control. Then, what about the influences of the BotU and TopD components on reading? Are there differences in the roles of the two types of attentional processes in reading? This study addresses these issues by using two types of training tasks separately, corresponding to the BotU and TopD attentional components. Three groups of Chinese children with dyslexia-15 children each in the BotU training, TopD training, and non-trained active control groups were recruited here. Participants completed reading measures and a CombiTVA task which was used to estimate VAS subcomponents, before and after the training procedure. Results showed that BotU training improved both the within-category and between-category VAS subcomponents and sentence reading performance; meanwhile, TopD training enhanced character reading fluency through improving spatial attention capacity. Moreover, benefits on attentional capacities and reading skills in the two training groups were generally maintained three months after the intervention. The present findings revealed diverse patterns in the influences of VAS on reading within the TVA framework, which contributes to enriching the understanding of VAS-reading relation.
Keywords: Bottom-up attention; Developmental dyslexia; Theory of visual attention; Top-down attention; Training; Visual attention span.
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Manli Tian and Changxi Xue
Manli Tian and Changxi Xue *
Department of Optical Engineering, Changchun University of Science and Technology, Changchun 130022, China
* Corresponding author: [email protected]
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We propose a zoom dual-path augmented reality head-up display optical system. The system uses two image generation units to display the basic and interactive information. Based on the node aberration theory, the aberration evaluation function was constructed. By solving the optimal solution of the aberration evaluation function, the initial structure of the interactive information display optical path was obtained. In order to solve the vergence-accommodation conflict in the process of vehicle speed change, the interactive information optical path was designed as a zoom optical path. The zoom distance is 10–20 m, and the basic information optical path will not be interfered with in the process. In addition, the tolerance analysis of the system was carried out to prove the machinability and stability of the system. This study makes the imaging of the augmented reality head-up display system more in line with the characteristics of the human eye and improves driving comfort.
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Zi Wang, Yujian Pang, Yumeng Su, Qibin Feng, and Guoqiang Lv Appl. Opt. 63 (3) 692-698 (2024)
ShiLi Wei, ZiChao Fan, ZhengBo Zhu, and DongLin Ma Appl. Opt. 58 (7) 1675-1681 (2019)
Zong Qin, Shih-Ming Lin, Kuang-Tso Luo, Cheng-Huan Chen, and Yi-Pai Huang Appl. Opt. 58 (20) 5366-5374 (2019)
Yi Liu, Jiaqi Dong, Yuqing Qiu, Bo-Ru Yang, and Zong Qin Opt. Express 31 (22) 35922-35936 (2023)
Rundong Fan, Shili Wei, Huiru Ji, Zhuang Qian, Hao Tan, Yan Mo, and Donglin Ma Opt. Express 31 (6) 10758-10774 (2023)
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Equations (7).
Gisele Bennett, Editor-in-Chief
AR-HUD Design Parameters
Parameter | Value |
---|---|
Entrance pupil diameter/mm | 160 |
Field of view/(°) | Far: ; near: |
VID/m | Far: 10–20; near: 2.5 |
Eyebox/mm | |
Pupil diameter/mm | 6 |
Wavelength/µm | 0.55 |
Distortion/% | |
PGU | TFT-DLP DLP |
MTF/( ) |
Distribution of Tolerance Range of the Optical System
Mirror | Thickness/mm | Decentered Distance in X Direction/mm | Decentered Distance in Y Direction/mm | Tilt Angle in X Direction/(°) | Tilt Angle in Y Direction/(°) |
---|---|---|---|---|---|
Grid Distortion
Position | Left Upper | Right Upper | Medium | Left Lower | Right Lower |
---|---|---|---|---|---|
10 m | −1.5726% | −2.9296% | −1.7732% | 1.9172% | −2.5739% |
15 m | −1.5633% | −2.8923% | −1.7303% | 1.9052% | −2.5214% |
20 m | −1.5448% | −2.8114% | −1.6404% | 1.8802% | −2.4172% |
Comparison of the AR-HUD Designed in This Paper with Other Designs
Qin’s [ ] | Kong’s [ ] | Fan’s [ ] | Ours | |
---|---|---|---|---|
FOV | Far: Near: | Far: Near: | Far: Near: | Far: Near: |
VID | Far: 9 mNear: 2.5 m | Far: 7.5 mNear: 2.5 m | Far: 10 mNear: 3.5 m | Far: 10–20 mNear: 2.5 m |
Eyebox size | ||||
Volume | 8.5 L | / | 16 L | 26 L |
MTF | ||||
Distortion |
Field error.
Help | Advanced Search
Title: a digital human model for symptom progression of vestibular motion sickness based on subjective vertical conflict theory.
Abstract: Digital human models of motion sickness have been actively developed, among which models based on subjective vertical conflict (SVC) theory are the most actively studied. These models facilitate the prediction of motion sickness in various scenarios such as riding in a car. Most SVC theory models predict the motion sickness incidence (MSI), which is defined as the percentage of people who would vomit with the given specific motion stimulus. However, no model has been developed to describe milder forms of discomfort or specific symptoms of motion sickness, even though predicting milder symptoms is important for applications in automobiles and daily use vehicles. Therefore, the purpose of this study was to build a computational model of symptom progression of vestibular motion sickness based on SVC theory. We focused on a model of vestibular motion sickness with six degrees-of-freedom (6DoF) head motions. The model was developed by updating the output part of the state-of-the-art SVC model, termed the 6DoF-SVC (IN1) model, from MSI to the MIsery SCale (MISC), which is a subjective rating scale for symptom progression. We conducted an experiment to measure the progression of motion sickness during a straight fore-aft motion. It was demonstrated that our proposed method, with the parameters of the output parts optimized by the experimental results, fits well with the observed MISC.
Subjects: | Human-Computer Interaction (cs.HC); Neurons and Cognition (q-bio.NC) |
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Urban railway and subway systems have intricate structures that often include loops and branches. Over the past few decades, scientists have used a variety of methods to model these transportation networks, with the models ranging in both complexity and accuracy. Now researchers in Italy have developed a simple model based on network theory to reproduce the labyrinthine structures found in, for example, the UK’s London Underground subway system [ 1 ]. The researchers say that their model could enable more-optimized urban planning.
The team’s model generates a transportation network by mapping an urban area’s spatial features, such as the distribution of people and amenities it contains, to a lattice of interconnected nodes. The connection between a pair of nodes has a so-called weight, which represents how quickly that connection can be traversed in the simulated network. These weights are adjusted until the time needed to travel between two nodes is minimized for all pairs of nodes. A key novelty of the model is that this optimization process accounts for realistic human-travel behaviors and traffic-congestion effects.
To demonstrate their model, the researchers used data on the density of people and amenities in Greater London and generated an optimized subway system for the region. They found that the system they generated had remarkably similar features to the region’s actual subway system, the London Underground. The researchers suggest that their model could be extended to help urban planners improve existing transportation networks and even design new ones.
–Ryan Wilkinson
Ryan Wilkinson is a Corresponding Editor for Physics Magazine based in Durham, UK.
Sebastiano Bontorin, Giulia Cencetti, Riccardo Gallotti, Bruno Lepri, and Manlio De Domenico
Phys. Rev. X 14 , 021050 (2024)
Published June 21, 2024
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Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.
The formulation and testing of a hypothesis is part of the scientific method, the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition, or experience.
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. ... Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence. However, confirming evidence is always open to revision. Other ...
Step 5: Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology. ...
A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon. ... —because it provides a suggested outcome based on the evidence. However, some scientists reject the term "educated guess" as incorrect. Experimenters may test and reject several hypotheses before solving the problem. According to Schick and Vaughn, ...
Hypothesis testing example. You want to test whether there is a relationship between gender and height. Based on your knowledge of human physiology, you formulate a hypothesis that men are, on average, taller than women. To test this hypothesis, you restate it as: H 0: Men are, on average, not taller than women. H a: Men are, on average, taller ...
The scientific method. At the core of biology and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
Bibliography. A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an ...
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess — it should be based on ...
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure. 3.
A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true. Example: If you see no difference in the cleaning ability of various laundry detergents, you might ...
Theory vs. Hypothesis: Basics of the Scientific Method. Written by MasterClass. Last updated: Jun 7, 2021 • 2 min read. Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science. Though you may hear the terms "theory" and "hypothesis ...
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.
A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used. For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be ...
3. Formulate the Hypothesis. Based on the research problem and literature review, formulate a clear and testable hypothesis. Ensure it is specific and relates directly to the variables being studied. Types of Hypotheses: Null Hypothesis (H0): There is no significant relationship between sleep duration and academic performance among college ...
A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.
Purpose The present study aimed to test the relationship between the components of the Cognitive Load Theory (CLT) including memory, intrinsic and extraneous cognitive load in workplace-based learning in a clinical setting, and decision-making skills of nursing students. Methods This study was conducted at Shahid Sadoughi University of Medical Sciences in 2021-2023. The participants were 151 ...
Within the framework of the theory of visual attention (TVA), the visual attention span (VAS) deficit among individuals with developmental dyslexia has been ascribed to the problems entailed by bottom-up (BotU) and top-down (TopD) attentional processes. The former involves two VAS subcomponents: the …
We propose a zoom dual-path augmented reality head-up display optical system. The system uses two image generation units to display the basic and interactive information. Based on the node aberration theory, the aberration evaluation function was constructed. By solving the optimal solution of the aberration evaluation function, the initial structure of the interactive information display ...
One fundamental disparity is that humans possess a wealth of prior knowledge, while AI lacks the essential commonsense knowledge required for learning tasks. Guided by schema theory, we employ the design science research methodology to introduce a novel knowledge-aware learning framework to harness the knowledge-based processes in human learning.
Digital human models of motion sickness have been actively developed, among which models based on subjective vertical conflict (SVC) theory are the most actively studied. These models facilitate the prediction of motion sickness in various scenarios such as riding in a car. Most SVC theory models predict the motion sickness incidence (MSI), which is defined as the percentage of people who ...
A simple model based on network theory can reproduce the complex structures seen in urban transportation networks. Elusive Clock Transition in Strontium Revealed. Researchers have measured a hard-to-observe electronic transition in strontium that was predicted six decades ago. More Recent Articles »