quantitative research title for grade 12 stem

Best 151+ Quantitative Research Topics for STEM Students

In today’s rapidly evolving world, STEM (Science, Technology, Engineering, and Mathematics) fields have gained immense significance. For STEM students, engaging in quantitative research is a pivotal aspect of their academic journey. Quantitative research involves the systematic collection and interpretation of numerical data to address research questions or test hypotheses. Choosing the right research topic is essential to ensure a successful and meaningful research endeavor.

In this blog, we will explore 151+ quantitative research topics for STEM students. Whether you are an aspiring scientist, engineer, or mathematician, this comprehensive list will inspire your research journey. But we understand that the journey through STEM education and research can be challenging at times. That’s why we’re here to support you every step of the way with our Engineering Assignment Help service.

What is Quantitative Research in STEM?

Table of Contents

Quantitative research is a scientific approach that relies on numerical data and statistical analysis to draw conclusions and make predictions. In STEM fields, quantitative research encompasses a wide range of methodologies, including experiments, surveys, and data analysis. The key characteristics of quantitative research in STEM include:

Data Collection: Systematic gathering of numerical data through experiments, observations, or surveys.
Statistical Analysis: Application of statistical techniques to analyze data and draw meaningful conclusions.
Hypothesis Testing: Testing hypotheses and theories using quantitative data.
Replicability: The ability to replicate experiments and obtain consistent results.
Generalizability: Drawing conclusions that can be applied to larger populations or phenomena.

Importance of Quantitative Research Topics for STEM Students

Quantitative research plays a pivotal role in STEM education and research for several reasons:

1. Empirical Evidence

It provides empirical evidence to support or refute scientific theories and hypotheses.

2. Data-Driven Decision-Making

STEM professionals use quantitative research to make informed decisions, from designing experiments to developing new technologies.

3. Innovation

It fuels innovation by providing data-driven insights that lead to the creation of new products, processes, and technologies.

4. Problem Solving

STEM students learn critical problem-solving skills through quantitative research, which are invaluable in their future careers.

5. Interdisciplinary Applications

Quantitative research transcends STEM disciplines, facilitating collaboration and the tackling of complex, real-world problems.

Also Read: Google Scholar Research Topics

Quantitative Research Topics for STEM Students

Now, let’s explore important quantitative research topics for STEM students:

Biology and Life Sciences

Here are some quantitative research topics in biology and life science:

1. The impact of climate change on biodiversity.

2. Analyzing the genetic basis of disease susceptibility.

3. Studying the effectiveness of vaccines in preventing infectious diseases.

4. Investigating the ecological consequences of invasive species.

5. Examining the role of genetics in aging.

6. Analyzing the effects of pollution on aquatic ecosystems.

7. Studying the evolution of antibiotic resistance.

8. Investigating the relationship between diet and lifespan.

9. Analyzing the impact of deforestation on wildlife.

10. Studying the genetics of cancer development.

11. Investigating the effectiveness of various plant fertilizers.

12. Analyzing the impact of microplastics on marine life.

13. Studying the genetics of human behavior.

14. Investigating the effects of pollution on plant growth.

15. Analyzing the microbiome’s role in human health.

16. Studying the impact of climate change on crop yields.

17. Investigating the genetics of rare diseases.

Let’s get started with some quantitative research topics for stem students in chemistry:

1. Studying the properties of superconductors at different temperatures.

2. Analyzing the efficiency of various catalysts in chemical reactions.

3. Investigating the synthesis of novel polymers with unique properties.

4. Studying the kinetics of chemical reactions.

5. Analyzing the environmental impact of chemical waste disposal.

6. Investigating the properties of nanomaterials for drug delivery.

7. Studying the behavior of nanoparticles in different solvents.

8. Analyzing the use of renewable energy sources in chemical processes.

9. Investigating the chemistry of atmospheric pollutants.

10. Studying the properties of graphene for electronic applications.

11. Analyzing the use of enzymes in industrial processes.

12. Investigating the chemistry of alternative fuels.

13. Studying the synthesis of pharmaceutical compounds.

14. Analyzing the properties of materials for battery technology.

15. Investigating the chemistry of natural products for drug discovery.

16. Analyzing the effects of chemical additives on food preservation.

17. Investigating the chemistry of carbon capture and utilization technologies.

Here are some quantitative research topics in physics for stem students:

1. Investigating the behavior of subatomic particles in high-energy collisions.

2. Analyzing the properties of dark matter and dark energy.

3. Studying the quantum properties of entangled particles.

4. Investigating the dynamics of black holes and their gravitational effects.

5. Analyzing the behavior of light in different mediums.

6. Studying the properties of superfluids at low temperatures.

7. Investigating the physics of renewable energy sources like solar cells.

8. Analyzing the properties of materials at extreme temperatures and pressures.

9. Studying the behavior of electromagnetic waves in various applications.

10. Investigating the physics of quantum computing.

11. Analyzing the properties of magnetic materials for data storage.

12. Studying the behavior of particles in plasma for fusion energy research.

13. Investigating the physics of nanoscale materials and devices.

14. Analyzing the properties of materials for use in semiconductors.

15. Studying the principles of thermodynamics in energy efficiency.

16. Investigating the physics of gravitational waves.

17. Analyzing the properties of materials for use in quantum technologies.

Engineering

Let’s explore some quantitative research topics for stem students in engineering:

1. Investigating the efficiency of renewable energy systems in urban environments.

2. Analyzing the impact of 3D printing on manufacturing processes.

3. Studying the structural integrity of materials in aerospace engineering.

4. Investigating the use of artificial intelligence in autonomous vehicles.

5. Analyzing the efficiency of water treatment processes in civil engineering.

6. Studying the impact of robotics in healthcare.

7. Investigating the optimization of supply chain logistics using quantitative methods.

8. Analyzing the energy efficiency of smart buildings.

9. Studying the effects of vibration on structural engineering.

10. Investigating the use of drones in agricultural practices.

11. Analyzing the impact of machine learning in predictive maintenance.

12. Studying the optimization of transportation networks.

13. Investigating the use of nanomaterials in electronic devices.

14. Analyzing the efficiency of renewable energy storage systems.

15. Studying the impact of AI-driven design in architecture.

16. Investigating the optimization of manufacturing processes using Industry 4.0 technologies.

17. Analyzing the use of robotics in underwater exploration.

Environmental Science

Here are some top quantitative research topics in environmental science for students:

1. Investigating the effects of air pollution on respiratory health.

2. Analyzing the impact of deforestation on climate change.

3. Studying the biodiversity of coral reefs and their conservation.

4. Investigating the use of remote sensing in monitoring deforestation.

5. Analyzing the effects of plastic pollution on marine ecosystems.

6. Studying the impact of climate change on glacier retreat.

7. Investigating the use of wetlands for water quality improvement.

8. Analyzing the effects of urbanization on local microclimates.

9. Studying the impact of oil spills on aquatic ecosystems.

10. Investigating the use of renewable energy in mitigating greenhouse gas emissions.

11. Analyzing the effects of soil erosion on agricultural productivity.

12. Studying the impact of invasive species on native ecosystems.

13. Investigating the use of bioremediation for soil cleanup.

14. Analyzing the effects of climate change on migratory bird patterns.

15. Studying the impact of land use changes on water resources.

16. Investigating the use of green infrastructure for urban stormwater management.

17. Analyzing the effects of noise pollution on wildlife behavior.

Computer Science

Let’s get started with some simple quantitative research topics for stem students:

1. Investigating the efficiency of machine learning algorithms for image recognition.

2. Analyzing the security of blockchain technology in financial transactions.

3. Studying the impact of quantum computing on cryptography.

4. Investigating the use of natural language processing in chatbots and virtual assistants.

5. Analyzing the effectiveness of cybersecurity measures in protecting sensitive data.

6. Studying the impact of algorithmic trading in financial markets.

7. Investigating the use of deep learning in autonomous robotics.

8. Analyzing the efficiency of data compression algorithms for large datasets.

9. Studying the impact of virtual reality in medical simulations.

10. Investigating the use of artificial intelligence in personalized medicine.

11. Analyzing the effectiveness of recommendation systems in e-commerce.

12. Studying the impact of cloud computing on data storage and processing.

13. Investigating the use of neural networks in predicting disease outbreaks.

14. Analyzing the efficiency of data mining techniques in customer behavior analysis.

15. Studying the impact of social media algorithms on user behavior.

16. Investigating the use of machine learning in natural language translation.

17. Analyzing the effectiveness of sentiment analysis in social media monitoring.

Mathematics

Let’s explore the quantitative research topics in mathematics for students:

1. Investigating the properties of prime numbers and their distribution.

2. Analyzing the behavior of chaotic systems using differential equations.

3. Studying the optimization of algorithms for solving complex mathematical problems.

4. Investigating the use of graph theory in network analysis.

5. Analyzing the properties of fractals in natural phenomena.

6. Studying the application of probability theory in risk assessment.

7. Investigating the use of numerical methods in solving partial differential equations.

8. Analyzing the properties of mathematical models for population dynamics.

9. Studying the optimization of algorithms for data compression.

10. Investigating the use of topology in data analysis.

11. Analyzing the behavior of mathematical models in financial markets.

12. Studying the application of game theory in strategic decision-making.

13. Investigating the use of mathematical modeling in epidemiology.

14. Analyzing the properties of algebraic structures in coding theory.

15. Studying the optimization of algorithms for image processing.

16. Investigating the use of number theory in cryptography.

17. Analyzing the behavior of mathematical models in climate prediction.

Earth Sciences

Here are some quantitative research topics for stem students in earth science:

1. Investigating the impact of volcanic eruptions on climate patterns.

2. Analyzing the behavior of earthquakes along tectonic plate boundaries.

3. Studying the geomorphology of river systems and erosion.

4. Investigating the use of remote sensing in monitoring wildfires.

5. Analyzing the effects of glacier melt on sea-level rise.

6. Studying the impact of ocean currents on weather patterns.

7. Investigating the use of geothermal energy in renewable power generation.

8. Analyzing the behavior of tsunamis and their destructive potential.

9. Studying the impact of soil erosion on agricultural productivity.

10. Investigating the use of geological data in mineral resource exploration.

11. Analyzing the effects of climate change on coastal erosion.

12. Studying the geomagnetic field and its role in navigation.

13. Investigating the use of radar technology in weather forecasting.

14. Analyzing the behavior of landslides and their triggers.

15. Studying the impact of groundwater depletion on aquifer systems.

16. Investigating the use of GIS (Geographic Information Systems) in land-use planning.

17. Analyzing the effects of urbanization on heat island formation.

Health Sciences and Medicine

Here are some quantitative research topics for stem students in health science and medicine:

1. Investigating the effectiveness of telemedicine in improving healthcare access.

2. Analyzing the impact of personalized medicine in cancer treatment.

3. Studying the epidemiology of infectious diseases and their spread.

4. Investigating the use of wearable devices in monitoring patient health.

5. Analyzing the effects of nutrition and exercise on metabolic health.

6. Studying the impact of genetics in predicting disease susceptibility.

7. Investigating the use of artificial intelligence in medical diagnosis.

8. Analyzing the behavior of pharmaceutical drugs in clinical trials.

9. Studying the effectiveness of mental health interventions in schools.

10. Investigating the use of gene editing technologies in treating genetic disorders.

11. Analyzing the properties of medical imaging techniques for early disease detection.

12. Studying the impact of vaccination campaigns on public health.

13. Investigating the use of regenerative medicine in tissue repair.

14. Analyzing the behavior of pathogens in antimicrobial resistance.

15. Studying the epidemiology of chronic diseases like diabetes and heart disease.

16. Investigating the use of bioinformatics in genomics research.

17. Analyzing the effects of environmental factors on health outcomes.

Quantitative research is the backbone of STEM fields, providing the tools and methodologies needed to explore, understand, and innovate in the world of science and technology . As STEM students, embracing quantitative research not only enhances your analytical skills but also equips you to address complex real-world challenges. With the extensive list of 155+ quantitative research topics for stem students provided in this blog, you have a starting point for your own STEM research journey. Whether you’re interested in biology, chemistry, physics, engineering, or any other STEM discipline, there’s a wealth of quantitative research topics waiting to be explored. So, roll up your sleeves, grab your lab coat or laptop, and embark on your quest for knowledge and discovery in the exciting world of STEM.

I hope you enjoyed this blog post about quantitative research topics for stem students.

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200+ Experimental Quantitative Research Topics For STEM Students In 2023

STEM stands for Science, Technology, Engineering, and Math, but these are not the only subjects we learn in school. STEM is like a treasure chest of skills that help students become great problem solvers, ready to tackle the real world’s challenges.

In this blog, we are exploring the world of Research Topics for STEM Students. We will explain what STEM really means and why it is so important for students. We will also give you the lowdown on how to pick a fascinating research topic. We will explain a list of 200+ Experimental Quantitative Research Topics For STEM Students.

And when it comes to writing a research title, we will guide you step by step. So, stay with us as we unlock the exciting world of STEM research – it is not just about grades; it is about growing smarter, more confident, and happier along the way.

What Is STEM?

Table of Contents

STEM is Science, Technology, Engineering, and Mathematics. It is a way of talking about things like learning, jobs, and activities related to these four important subjects. Science is about understanding the world around us, technology is about using tools and machines to solve problems, engineering is about designing and building things, and mathematics is about numbers and solving problems with them. STEM helps us explore, discover, and create cool stuff that makes our world better and more exciting.

Why STEM Research Is Important?

STEM research is important because it helps us learn new things about the world and solve problems. When scientists, engineers, and mathematicians study these subjects, they can discover cures for diseases, create new technology that makes life easier, and build things that help us live better. It is like a big puzzle where we put together pieces of knowledge to make our world safer, healthier, and more fun.

STEM research leads to discoveries and solutions.
It helps find cures for diseases.
STEM technology makes life easier.
Engineers build things that improve our lives.
Mathematics helps us understand and solve complex problems. There are various Mathematic formulas that students should know.

How to Choose a Topic for STEM Research Paper

Here are some steps to choose a topic for STEM Research Paper:

Step 1: Identify Your Interests

Think about what you like and what excites you in science, technology, engineering, or math. It could be something you learned in school, saw in the news, or experienced in your daily life. Choosing a topic you’re passionate about makes the research process more enjoyable.

Step 2: Research Existing Topics

Look up different STEM research areas online, in books, or at your library. See what scientists and experts are studying. This can give you ideas and help you understand what’s already known in your chosen field.

Step 3: Consider Real-World Problems

Think about the problems you see around you. Are there issues in your community or the world that STEM can help solve? Choosing a topic that addresses a real-world problem can make your research impactful.

Step 4: Talk to Teachers and Mentors

Discuss your interests with your teachers, professors, or mentors. They can offer guidance and suggest topics that align with your skills and goals. They may also provide resources and support for your research.

Step 5: Narrow Down Your Topic

Once you have some ideas, narrow them down to a specific research question or project. Make sure it’s not too broad or too narrow. You want a topic that you can explore in depth within the scope of your research paper.

Here we will discuss 200+ Experimental Quantitative Research Topics For STEM Students:

Qualitative Research Topics for STEM Students:

Qualitative research focuses on exploring and understanding phenomena through non-numerical data and subjective experiences. Here are 10 qualitative research topics for STEM students:

Exploring the experiences of female STEM students in overcoming gender bias in academia.
Understanding the perceptions of teachers regarding the integration of technology in STEM education.
Investigating the motivations and challenges of STEM educators in underprivileged schools.
Exploring the attitudes and beliefs of parents towards STEM education for their children.
Analyzing the impact of collaborative learning on student engagement in STEM subjects.
Investigating the experiences of STEM professionals in bridging the gap between academia and industry.
Understanding the cultural factors influencing STEM career choices among minority students.
Exploring the role of mentorship in the career development of STEM graduates.
Analyzing the perceptions of students towards the ethics of emerging STEM technologies like AI and CRISPR. You may check the best AI tools like Top 10 AI Chatbots in 2024: Efficient ChatGPT Alternatives or Rise Of Generative AI: Transforming The Way Businesses Create Content .
Investigating the emotional well-being and stress levels of STEM students during their academic journey.

Easy Experimental Research Topics for STEM Students:

These experimental research topics are relatively straightforward and suitable for STEM students who are new to research:

Measuring the effect of different light wavelengths on plant growth.
Investigating the relationship between exercise and heart rate in various age groups.
Testing the effectiveness of different insulating materials in conserving heat.
Examining the impact of pH levels on the rate of chemical reactions.
Studying the behavior of magnets in different temperature conditions.
Investigating the effect of different concentrations of a substance on bacterial growth.
Testing the efficiency of various sunscreen brands in blocking UV radiation.
Measuring the impact of music genres on concentration and productivity.
Examining the correlation between the angle of a ramp and the speed of a rolling object.
Investigating the relationship between the number of blades on a wind turbine and energy output.

Research Topics for STEM Students in the Philippines:

These research topics are tailored for STEM students in the Philippines:

Assessing the impact of climate change on the biodiversity of coral reefs in the Philippines.
Studying the potential of indigenous plants in the Philippines for medicinal purposes.
Investigating the feasibility of harnessing renewable energy sources like solar and wind in rural Filipino communities.
Analyzing the water quality and pollution levels in major rivers and lakes in the Philippines.
Exploring sustainable agricultural practices for small-scale farmers in the Philippines.
Assessing the prevalence and impact of dengue fever outbreaks in urban areas of the Philippines.
Investigating the challenges and opportunities of STEM education in remote Filipino islands.
Studying the impact of typhoons and natural disasters on infrastructure resilience in the Philippines.
Analyzing the genetic diversity of endemic species in the Philippine rainforests.
Assessing the effectiveness of disaster preparedness programs in Philippine communities.

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Good Research Topics for STEM Students:

These research topics are considered good because they offer interesting avenues for investigation and learning:

Developing a low-cost and efficient water purification system for rural communities.
Investigating the potential use of CRISPR-Cas9 for gene therapy in genetic disorders.
Studying the applications of blockchain technology in securing medical records.
Analyzing the impact of 3D printing on customized prosthetics for amputees.
Exploring the use of artificial intelligence in predicting and preventing forest fires.
Investigating the effects of microplastic pollution on aquatic ecosystems.
Analyzing the use of drones in monitoring and managing crops.
Studying the potential of quantum computing in solving complex optimization problems.
Investigating the development of biodegradable materials for sustainable packaging.
Exploring the ethical implications of gene editing in humans.

Unique Research Topics for STEM Students:

Unique research topics can provide STEM students with the opportunity to explore unconventional and innovative ideas. Here are 10 unique research topics for STEM students:

Investigating the use of bioluminescent organisms for sustainable lighting solutions.
Studying the potential of using spider silk proteins for advanced materials in engineering.
Exploring the application of quantum entanglement for secure communication in the field of cryptography.
Analyzing the feasibility of harnessing geothermal energy from underwater volcanoes.
Investigating the use of CRISPR-Cas12 for rapid and cost-effective disease diagnostics.
Studying the interaction between artificial intelligence and human creativity in art and music generation.
Exploring the development of edible packaging materials to reduce plastic waste.
Investigating the impact of microgravity on cellular behavior and tissue regeneration in space.
Analyzing the potential of using sound waves to detect and combat invasive species in aquatic ecosystems.
Studying the use of biotechnology in reviving extinct species, such as the woolly mammoth.

Experimental Research Topics for STEM Students in the Philippines

Research topics for STEM students in the Philippines can address specific regional challenges and opportunities. Here are 10 experimental research topics for STEM students in the Philippines:

Assessing the effectiveness of locally sourced materials for disaster-resilient housing construction in typhoon-prone areas.
Investigating the utilization of indigenous plants for natural remedies in Filipino traditional medicine.
Studying the impact of volcanic soil on crop growth and agriculture in volcanic regions of the Philippines.
Analyzing the water quality and purification methods in remote island communities.
Exploring the feasibility of using bamboo as a sustainable construction material in the Philippines.
Investigating the potential of using solar stills for freshwater production in water-scarce regions.
Studying the effects of climate change on the migration patterns of bird species in the Philippines.
Analyzing the growth and sustainability of coral reefs in marine protected areas.
Investigating the utilization of coconut waste for biofuel production.
Studying the biodiversity and conservation efforts in the Tubbataha Reefs Natural Park.

Capstone Research Topics for STEM Students in the Philippines:

Capstone research projects are often more comprehensive and can address real-world issues. Here are 10 capstone research topics for STEM students in the Philippines:

Designing a low-cost and sustainable sanitation system for informal settlements in urban Manila.
Developing a mobile app for monitoring and reporting natural disasters in the Philippines.
Assessing the impact of climate change on the availability and quality of drinking water in Philippine cities.
Designing an efficient traffic management system to address congestion in major Filipino cities.
Analyzing the health implications of air pollution in densely populated urban areas of the Philippines.
Developing a renewable energy microgrid for off-grid communities in the archipelago.
Assessing the feasibility of using unmanned aerial vehicles (drones) for agricultural monitoring in rural Philippines.
Designing a low-cost and sustainable aquaponics system for urban agriculture.
Investigating the potential of vertical farming to address food security in densely populated urban areas.
Developing a disaster-resilient housing prototype suitable for typhoon-prone regions.

Experimental Quantitative Research Topics for STEM Students:

Experimental quantitative research involves the collection and analysis of numerical data to conclude. Here are 10 Experimental Quantitative Research Topics For STEM Students interested in experimental quantitative research:

Examining the impact of different fertilizers on crop yield in agriculture.
Investigating the relationship between exercise and heart rate among different age groups.
Analyzing the effect of varying light intensities on photosynthesis in plants.
Studying the efficiency of various insulation materials in reducing building heat loss.
Investigating the relationship between pH levels and the rate of corrosion in metals.
Analyzing the impact of different concentrations of pollutants on aquatic ecosystems.
Examining the effectiveness of different antibiotics on bacterial growth.
Trying to figure out how temperature affects how thick liquids are.
Finding out if there is a link between the amount of pollution in the air and lung illnesses in cities.
Analyzing the efficiency of solar panels in converting sunlight into electricity under varying conditions.

Descriptive Research Topics for STEM Students

Descriptive research aims to provide a detailed account or description of a phenomenon. Here are 10 topics for STEM students interested in descriptive research:

Describing the physical characteristics and behavior of a newly discovered species of marine life.
Documenting the geological features and formations of a particular region.
Creating a detailed inventory of plant species in a specific ecosystem.
Describing the properties and behavior of a new synthetic polymer.
Documenting the daily weather patterns and climate trends in a particular area.
Providing a comprehensive analysis of the energy consumption patterns in a city.
Describing the structural components and functions of a newly developed medical device.
Documenting the characteristics and usage of traditional construction materials in a region.
Providing a detailed account of the microbiome in a specific environmental niche.
Describing the life cycle and behavior of a rare insect species.

Research Topics for STEM Students in the Pandemic:

The COVID-19 pandemic has raised many research opportunities for STEM students. Here are 10 research topics related to pandemics:

Analyzing the effectiveness of various personal protective equipment (PPE) in preventing the spread of respiratory viruses.
Studying the impact of lockdown measures on air quality and pollution levels in urban areas.
Investigating the psychological effects of quarantine and social isolation on mental health.
Analyzing the genomic variation of the SARS-CoV-2 virus and its implications for vaccine development.
Studying the efficacy of different disinfection methods on various surfaces.
Investigating the role of contact tracing apps in tracking & controlling the spread of infectious diseases.
Analyzing the economic impact of the pandemic on different industries and sectors.
Studying the effectiveness of remote learning in STEM education during lockdowns.
Investigating the social disparities in healthcare access during a pandemic.
Analyzing the ethical considerations surrounding vaccine distribution and prioritization.

Research Topics for STEM Students Middle School

Research topics for middle school STEM students should be engaging and suitable for their age group. Here are 10 research topics:

Investigating the growth patterns of different types of mold on various food items.
Studying the negative effects of music on plant growth and development.
Analyzing the relationship between the shape of a paper airplane and its flight distance.
Investigating the properties of different materials in making effective insulators for hot and cold beverages.
Studying the effect of salt on the buoyancy of different objects in water.
Analyzing the behavior of magnets when exposed to different temperatures.
Investigating the factors that affect the rate of ice melting in different environments.
Studying the impact of color on the absorption of heat by various surfaces.
Analyzing the growth of crystals in different types of solutions.
Investigating the effectiveness of different natural repellents against common pests like mosquitoes.

Technology Research Topics for STEM Students

Technology is at the forefront of STEM fields. Here are 10 research topics for STEM students interested in technology:

Developing and optimizing algorithms for autonomous drone navigation in complex environments.
Exploring the use of blockchain technology for enhancing the security and transparency of supply chains.
Investigating the applications of virtual reality (VR) and augmented reality (AR) in medical training and surgery simulations.
Studying the potential of 3D printing for creating personalized prosthetics and orthopedic implants.
Analyzing the ethical and privacy implications of facial recognition technology in public spaces.
Investigating the development of quantum computing algorithms for solving complex optimization problems.
Explaining the use of machine learning and AI in predicting and mitigating the impact of natural disasters.
Studying the advancement of brain-computer interfaces for assisting individuals with
disabilities.
Analyzing the role of wearable technology in monitoring and improving personal health and wellness.
Investigating the use of robotics in disaster response and search and rescue operations.

Scientific Research Topics for STEM Students

Scientific research encompasses a wide range of topics. Here are 10 research topics for STEM students focusing on scientific exploration:

Investigating the behavior of subatomic particles in high-energy particle accelerators.
Studying the ecological impact of invasive species on native ecosystems.
Analyzing the genetics of antibiotic resistance in bacteria and its implications for healthcare.
Exploring the physics of gravitational waves and their detection through advanced interferometry.
Investigating the neurobiology of memory formation and retention in the human brain.
Studying the biodiversity and adaptation of extremophiles in harsh environments.
Analyzing the chemistry of deep-sea hydrothermal vents and their potential for life beyond Earth.
Exploring the properties of superconductors and their applications in technology.
Investigating the mechanisms of stem cell differentiation for regenerative medicine.
Studying the dynamics of climate change and its impact on global ecosystems.

Interesting Research Topics for STEM Students:

Engaging and intriguing research topics can foster a passion for STEM. Here are 10 interesting research topics for STEM students:

Exploring the science behind the formation of auroras and their cultural significance.
Investigating the mysteries of dark matter and dark energy in the universe.
Studying the psychology of decision-making in high-pressure situations, such as sports or
emergencies.
Analyzing the impact of social media on interpersonal relationships and mental health.
Exploring the potential for using genetic modification to create disease-resistant crops.
Investigating the cognitive processes involved in solving complex puzzles and riddles.
Studying the history and evolution of cryptography and encryption methods.
Analyzing the physics of time travel and its theoretical possibilities.
Exploring the role of Artificial Intelligence in creating art and music.
Investigating the science of happiness and well-being, including factors contributing to life satisfaction.

Practical Research Topics for STEM Students

Practical research often leads to real-world solutions. Here are 10 practical research topics for STEM students:

Developing an affordable and sustainable water purification system for rural communities.
Designing a low-cost, energy-efficient home heating and cooling system.
Investigating strategies for reducing food waste in the supply chain and households.
Studying the effectiveness of eco-friendly pest control methods in agriculture.
Analyzing the impact of renewable energy integration on the stability of power grids.
Developing a smartphone app for early detection of common medical conditions.
Investigating the feasibility of vertical farming for urban food production.
Designing a system for recycling and upcycling electronic waste.
Studying the environmental benefits of green roofs and their potential for urban heat island mitigation.
Analyzing the efficiency of alternative transportation methods in reducing carbon emissions.

Experimental Research Topics for STEM Students About Plants

Plants offer a rich field for experimental research. Here are 10 experimental research topics about plants for STEM students:

Investigating the effect of different light wavelengths on plant growth and photosynthesis.
Studying the impact of various fertilizers and nutrient solutions on crop yield.
Analyzing the response of plants to different types and concentrations of plant hormones.
Investigating the role of mycorrhizal in enhancing nutrient uptake in plants.
Studying the effects of drought stress and water scarcity on plant physiology and adaptation mechanisms.
Analyzing the influence of soil pH on plant nutrient availability and growth.
Investigating the chemical signaling and defense mechanisms of plants against herbivores.
Studying the impact of environmental pollutants on plant health and genetic diversity.
Analyzing the role of plant secondary metabolites in pharmaceutical and agricultural applications.
Investigating the interactions between plants and beneficial microorganisms in the rhizosphere.

Qualitative Research Topics for STEM Students in the Philippines

Qualitative research in the Philippines can address local issues and cultural contexts. Here are 10 qualitative research topics for STEM students in the Philippines:

Exploring indigenous knowledge and practices in sustainable agriculture in Filipino communities.
Studying the perceptions and experiences of Filipino fishermen in coping with climate change impacts .
Analyzing the cultural significance and traditional uses of medicinal plants in indigenous Filipino communities.
Investigating the barriers and facilitators of STEM education access in remote Philippine islands.
Exploring the role of traditional Filipino architecture in natural disaster resilience.
Studying the impact of indigenous farming methods on soil conservation and fertility.
Analyzing the cultural and environmental significance of mangroves in coastal Filipino regions.
Investigating the knowledge and practices of Filipino healers in treating common ailments.
Exploring the cultural heritage and conservation efforts of the Ifugao rice terraces.
Studying the perceptions and practices of Filipino communities in preserving marine biodiversity.

Science Research Topics for STEM Students

Science offers a diverse range of research avenues. Here are 10 science research topics for STEM students:

Investigating the potential of gene editing techniques like CRISPR-Cas9 in curing genetic diseases.
Studying the ecological impacts of species reintroduction programs on local ecosystems.
Analyzing the effects of microplastic pollution on aquatic food webs and ecosystems.
Investigating the link between air pollution and respiratory health in urban populations.
Studying the role of epigenetics in the inheritance of acquired traits in organisms.
Analyzing the physiology and adaptations of extremophiles in extreme environments on Earth.
Investigating the genetics of longevity and factors influencing human lifespan.
Studying the behavioral ecology and communication strategies of social insects.
Analyzing the effects of deforestation on global climate patterns and biodiversity loss.
Investigating the potential of synthetic biology in creating bioengineered organisms for beneficial applications.

Correlational Research Topics for STEM Students

Correlational research focuses on relationships between variables. Here are 10 correlational research topics for STEM students:

Analyzing the correlation between dietary habits and the incidence of chronic diseases.
Studying the relationship between exercise frequency and mental health outcomes.
Investigating the correlation between socioeconomic status and access to quality healthcare.
Analyzing the link between social media usage and self-esteem in adolescents.
Studying the correlation between academic performance and sleep duration among students.
Investigating the relationship between environmental factors and the prevalence of allergies.
Analyzing the correlation between technology use and attention span in children.
Studying how environmental factors are related to the frequency of allergies.
Investigating the link between parental involvement in education and student achievement.
Analyzing the correlation between temperature fluctuations and wildlife migration patterns.

Quantitative Research Topics for STEM Students in the Philippines

Quantitative research in the Philippines can address specific regional issues. Here are 10 quantitative research topics for STEM students in the Philippines

Analyzing the impact of typhoons on coastal erosion rates in the Philippines.
Studying the quantitative effects of land use change on watershed hydrology in Filipino regions.
Investigating the quantitative relationship between deforestation and habitat loss for endangered species.
Analyzing the quantitative patterns of marine biodiversity in Philippine coral reef ecosystems.
Studying the quantitative assessment of water quality in major Philippine rivers and lakes.
Investigating the quantitative analysis of renewable energy potential in specific Philippine provinces.
Analyzing the quantitative impacts of agricultural practices on soil health and fertility.
Studying the quantitative effectiveness of mangrove restoration in coastal protection in the Philippines.
Investigating the quantitative evaluation of indigenous agricultural practices for sustainability .
Analyzing the quantitative patterns of air pollution and its health impacts in urban Filipino areas.

Things That Must Keep In Mind While Writing Quantitative Research Title

Here are a few things that must be kept in mind while writing a quantitative research:

1. Be Clear and Precise

Make sure your research title is clear and says exactly what your study is about. People should easily understand the topic and goals of your research by reading the title.

2. Use Important Words

Include words that are crucial to your research, like the main subjects, who you’re studying, and how you’re doing your research. This helps others find your work and understand what it’s about.

3. Avoid Confusing Words

Stay away from words that might confuse people. Your title should be easy to grasp, even if someone isn’t an expert in your field.

4. Show Your Research Approach

Tell readers what kind of research you did, like experiments or surveys. This gives them a hint about how you conducted your study.

5. Match Your Title with Your Research Questions

Make sure your title matches the questions you’re trying to answer in your research. It should give a sneak peek into what your study is all about and keep you on the right track as you work on it.

STEM students, addressing what STEM is and why research matters in this field. It offered an extensive list of research topics , including experimental, qualitative, and regional options, catering to various academic levels and interests. Whether you’re a middle school student or pursuing advanced studies, these topics offer a wealth of ideas. The key takeaway is to choose a topic that resonates with your passion and aligns with your goals, ensuring a successful journey in STEM research. Choose the best Experimental Quantitative Research Topics For STEM students today!

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189+ Good Quantitative Research Topics For STEM Students

Quantitative research is an essential part of STEM (Science, Technology, Engineering, and Mathematics) fields. It involves collecting and analyzing numerical data to answer research questions and test hypotheses.

In 2023, STEM students have a wealth of exciting research opportunities in various disciplines. Whether you’re an undergraduate or graduate student, here are quantitative research topics to consider for your next project.

If you are looking for the best list of quantitative research topics for stem students, then you can check the given list in each field. It offers STEM students numerous opportunities to explore and contribute to their respective fields in 2023 and beyond.

Whether you’re interested in astrophysics, biology, engineering, mathematics, or any other STEM field.

Also Read: Most Exciting Qualitative Research Topics For Students

What Is Quantitative Research

Table of Contents

Quantitative research is a type of research that focuses on the organized collection, analysis, and evaluation of numerical data to answer research questions, test theories, and find trends or connections between factors. It is an organized, objective way to do study that uses measurable data and scientific methods to come to results.

Quantitative research is often used in many areas, such as the natural sciences, social sciences, economics, psychology, education, and market research. It gives useful information about patterns, trends, cause-and-effect relationships, and how often things happen. Quantitative tools are used by researchers to answer questions like “How many?” and “How often?” “Is there a significant difference?” or “What is the relationship between the variables?”

In comparison to quantitative research, qualitative research uses non-numerical data like conversations, notes, and open-ended surveys to understand and explore the ideas, experiences, and points of view of people or groups. Researchers often choose between quantitative and qualitative methods based on their research goals, questions, and the type of thing they are studying.

How To Choose Quantitative Research Topics For STEM

Here’s a step-by-step guide on how to choose quantitative research topics for STEM:

Step 1:- Identify Your Interests and Passions

Start by reflecting on your personal interests within STEM. What areas or subjects in STEM excite you the most? Choosing a topic you’re passionate about will keep you motivated throughout the research process.

Step 2:- Review Coursework and Textbooks

Look through your coursework, textbooks, and class notes. Identify concepts, theories, or areas that you found particularly intriguing or challenging. These can be a source of potential research topics.

Step 3:- Consult with Professors and Advisors

Discuss your research interests with professors, academic advisors, or mentors. They can provide valuable insights, suggest relevant topics, and guide you toward areas with research opportunities.

Step 4:- Read Recent Literature

Explore recent research articles, journals, and publications in STEM fields. This will help you identify current trends, gaps in knowledge, and areas where further research is needed.

Step 5:- Narrow Down Your Focus

Once you have a broad area of interest, narrow it down to a specific research focus. Consider questions like:

What specific problem or phenomenon do you want to investigate?
Are there unanswered questions or controversies in this area?
What impact could your research have on the field or society?

Step 6:- Consider Resources and Access

Assess the resources available to you, including access to laboratories, equipment, databases, and funding. Ensure that your chosen topic aligns with the resources you have or can access.

Step 7:- Think About Practicality

Consider the feasibility of conducting research on your chosen topic. Are the data readily available, or will you need to collect data yourself? Can you complete the research within your available time frame?

Step 8:- Define Your Research Question

Formulate a clear and specific research question or hypothesis. Your research question should guide your entire study and provide a focus for your data collection and analysis.

Step 9:- Conduct a Literature Review

Dive deeper into the existing literature related to your chosen topic. This will help you understand the current state of research, identify gaps, and refine your research question.

Step 10:- Consider the Impact

Think about the potential impact of your research. How does your topic contribute to the advancement of knowledge in your field? Does it have practical applications or implications for society?

Step 11:- Brainstorm Research Methods

Determine the quantitative research methods and data collection techniques you plan to use. Consider whether you’ll conduct experiments, surveys, data analysis, simulations, or use existing datasets.

Step 12:- Seek Feedback

Share your research topic and ideas with peers, advisors, or mentors. They can provide valuable feedback and help you refine your research focus.

Step 13:- Assess Ethical Considerations

Consider ethical implications related to your research, especially if it involves human subjects, sensitive data, or potential environmental impacts. Ensure that your research adheres to ethical guidelines.

Step 14:- Finalize Your Research Topic

Once you’ve gone through these steps, finalize your research topic. Write a clear and concise research proposal that outlines your research question, objectives, methods, and expected outcomes.

Step 15:- Stay Open to Adjustments

Be open to adjusting your research topic as you progress. Sometimes, new insights or challenges may lead you to refine or adapt your research focus.

Following are the most interesting quantitative research topics for stem students. These are given below.

Quantitative Research Topics In Physics and Astronomy

Quantum Computing Algorithms : Investigate new algorithms for quantum computers and their potential applications.
Dark Matter Detection Methods : Explore innovative approaches to detect dark matter particles.
Quantum Teleportation : Study the principles and applications of quantum teleportation.
Exoplanet Characterization : Analyze data from telescopes to characterize exoplanets.
Nuclear Fusion Modeling : Create mathematical models for nuclear fusion reactions.
Superconductivity at High Temperatures : Research the properties and applications of high-temperature superconductors.
Gravitational Wave Analysis : Analyze gravitational wave data to study astrophysical phenomena.
Black Hole Thermodynamics : Investigate the thermodynamics of black holes and their entropy.

Quantitative Research Topics In Biology and Life Sciences

Genome-Wide Association Studies (GWAS) : Conduct GWAS to identify genetic factors associated with diseases.
Pharmacokinetics and Pharmacodynamics : Study drug interactions in the human body.
Ecological Modeling : Model ecosystems to understand population dynamics.
Protein Folding : Research the kinetics and thermodynamics of protein folding.
Cancer Epidemiology : Analyze cancer incidence and risk factors in specific populations.
Neuroimaging Analysis : Develop algorithms for analyzing brain imaging data.
Evolutionary Genetics : Investigate evolutionary patterns using genetic data.
Stem Cell Differentiation : Study the factors influencing stem cell differentiation.

Engineering and Technology Quantitative Research Topics

Renewable Energy Efficiency : Optimize the efficiency of solar panels or wind turbines.
Aerodynamics of Drones : Analyze the aerodynamics of drone designs.
Autonomous Vehicle Safety : Evaluate safety measures for autonomous vehicles.
Machine Learning in Robotics : Implement machine learning algorithms for robot control.
Blockchain Scalability : Research methods to scale blockchain technology.
Quantum Computing Hardware : Design and test quantum computing hardware components.
IoT Security : Develop security protocols for the Internet of Things (IoT).
3D Printing Materials Analysis : Study the mechanical properties of 3D-printed materials.

Quantitative Research Topics In Mathematics and Statistics

Following are the best Quantitative Research Topics For STEM Students in mathematics and statistics.

Prime Number Distribution : Investigate the distribution of prime numbers.
Graph Theory Algorithms : Develop algorithms for solving graph theory problems.
Statistical Analysis of Financial Markets : Analyze financial data and market trends.
Number Theory Research : Explore unsolved problems in number theory.
Bayesian Machine Learning : Apply Bayesian methods to machine learning models.
Random Matrix Theory : Study the properties of random matrices in mathematics and physics.
Topological Data Analysis : Use topology to analyze complex data sets.
Quantum Algorithms for Optimization : Research quantum algorithms for optimization problems.

Experimental Quantitative Research Topics In Science and Earth Sciences

Climate Change Modeling : Develop climate models to predict future trends.
Biodiversity Conservation Analysis : Analyze data to support biodiversity conservation efforts.
Geographic Information Systems (GIS) : Apply GIS techniques to solve environmental problems.
Oceanography and Remote Sensing : Use satellite data for oceanographic research.
Air Quality Monitoring : Develop sensors and models for air quality assessment.
Hydrological Modeling : Study the movement and distribution of water resources.
Volcanic Activity Prediction : Predict volcanic eruptions using quantitative methods.
Seismology Data Analysis : Analyze seismic data to understand earthquake patterns.

Chemistry and Materials Science Quantitative Research Topics

Nanomaterial Synthesis and Characterization : Research the synthesis and properties of nanomaterials.
Chemoinformatics : Analyze chemical data for drug discovery and materials science.
Quantum Chemistry Simulations : Perform quantum simulations of chemical reactions.
Materials for Renewable Energy : Investigate materials for energy storage and conversion.
Catalysis Kinetics : Study the kinetics of chemical reactions catalyzed by materials.
Polymer Chemistry : Research the properties and applications of polymers.
Analytical Chemistry Techniques : Develop new analytical techniques for chemical analysis.
Sustainable Chemistry : Explore green chemistry approaches for sustainable materials.

Computer Science and Information Technology Topics

Natural Language Processing (NLP) : Work on NLP algorithms for language understanding.
Cybersecurity Analytics : Analyze cybersecurity threats and vulnerabilities.
Big Data Analytics : Apply quantitative methods to analyze large data sets.
Machine Learning Fairness : Investigate bias and fairness issues in machine learning models.
Human-Computer Interaction (HCI) : Study user behavior and interaction patterns.
Software Performance Optimization : Optimize software applications for performance.
Distributed Systems Analysis : Analyze the performance of distributed computing systems.
Bioinformatics Data Mining : Develop algorithms for mining biological data.

Good Quantitative Research Topics Students In Medicine and Healthcare

Clinical Trial Data Analysis : Analyze clinical trial data to evaluate treatment effectiveness.
Epidemiological Modeling : Model disease spread and intervention strategies.
Healthcare Data Analytics : Analyze healthcare data for patient outcomes and cost reduction.
Medical Imaging Algorithms : Develop algorithms for medical image analysis.
Genomic Medicine : Apply genomics to personalized medicine approaches.
Telemedicine Effectiveness : Study the effectiveness of telemedicine in healthcare delivery.
Health Informatics : Analyze electronic health records for insights into patient care.

Agriculture and Food Sciences Topics

Precision Agriculture : Use quantitative methods for optimizing crop production.
Food Safety Analysis : Analyze food safety data and quality control.
Aquaculture Sustainability : Research sustainable practices in aquaculture.
Crop Disease Modeling : Model the spread of diseases in agricultural crops.
Climate-Resilient Agriculture : Develop strategies for agriculture in changing climates.
Food Supply Chain Optimization : Optimize food supply chain logistics.
Soil Health Assessment : Analyze soil data for sustainable land management.

Social Sciences with Quantitative Approaches

Educational Data Mining : Analyze educational data for improving learning outcomes.
Sociodemographic Surveys : Study social trends and demographics using surveys.
Psychometrics : Develop and validate psychological measurement instruments.
Political Polling Analysis : Analyze political polling data and election trends.
Economic Modeling : Develop economic models for policy analysis.
Urban Planning Analytics : Analyze data for urban planning and infrastructure.
Climate Policy Evaluation : Evaluate the impact of climate policies on society.

Environmental Engineering Quantitative Research Topics

Water Quality Assessment : Analyze water quality data for environmental monitoring.
Waste Management Optimization : Optimize waste collection and recycling programs.
Environmental Impact Assessments : Evaluate the environmental impact of projects.
Air Pollution Modeling : Model the dispersion of air pollutants in urban areas.
Sustainable Building Design : Apply quantitative methods to sustainable architecture.

Quantitative Research Topics Robotics and Automation

Robotic Swarm Behavior : Study the behavior of robot swarms in different tasks.
Autonomous Drone Navigation : Develop algorithms for autonomous drone navigation.
Humanoid Robot Control : Implement control algorithms for humanoid robots.
Robotic Grasping and Manipulation : Study robotic manipulation techniques.
Reinforcement Learning for Robotics : Apply reinforcement learning to robotic control.

Quantitative Research Topics Materials Engineering

Additive Manufacturing Process Optimization : Optimize 3D printing processes.
Smart Materials for Aerospace : Research smart materials for aerospace applications.
Nanostructured Materials for Energy Storage : Investigate energy storage materials.
Corrosion Prevention : Develop corrosion-resistant materials and coatings.

Nuclear Engineering Quantitative Research Topics

Nuclear Reactor Safety Analysis : Study safety aspects of nuclear reactor designs.
Nuclear Fuel Cycle Analysis : Analyze the nuclear fuel cycle for efficiency.
Radiation Shielding Materials : Research materials for radiation protection.

Quantitative Research Topics In Biomedical Engineering

Medical Device Design and Testing : Develop and test medical devices.
Biomechanics Analysis : Analyze biomechanics in sports or rehabilitation.
Biomaterials for Medical Implants : Investigate materials for medical implants.

Good Quantitative Research Topics Chemical Engineering

Chemical Process Optimization : Optimize chemical manufacturing processes.
Industrial Pollution Control : Develop strategies for pollution control in industries.
Chemical Reaction Kinetics : Study the kinetics of chemical reactions in industries.

Best Quantitative Research Topics In Renewable Energy

Energy Storage Systems : Research and optimize energy storage solutions.
Solar Cell Efficiency : Improve the efficiency of photovoltaic cells.
Wind Turbine Performance Analysis : Analyze and optimize wind turbine designs.

Brilliant Quantitative Research Topics In Astronomy and Space Sciences

Astrophysical Simulations : Simulate astrophysical phenomena using numerical methods.
Spacecraft Trajectory Optimization : Optimize spacecraft trajectories for missions.
Exoplanet Detection Algorithms : Develop algorithms for exoplanet detection.

Quantitative Research Topics In Psychology and Cognitive Science

Cognitive Psychology Experiments : Conduct quantitative experiments in cognitive psychology.
Emotion Recognition Algorithms : Develop algorithms for emotion recognition in AI.
Neuropsychological Assessments : Create quantitative assessments for brain function.

Geology and Geological Engineering Quantitative Research Topics

Geological Data Analysis : Analyze geological data for mineral exploration.
Geological Hazard Prediction : Predict geological hazards using quantitative models.

Great Quantitative Research Topics In Cybersecurity

Network Intrusion Detection : Develop quantitative methods for intrusion detection.
Cryptocurrency Analysis : Analyze blockchain data and cryptocurrency trends.

Mathematical Biology Quantitative Research Topics

Epidemiological Modeling : Model disease spread and control in populations.
Population Genetics : Analyze genetic data to understand population dynamics.

Quantitative Research Topics In Chemical Analysis

Analytical Chemistry Methods : Develop quantitative methods for chemical analysis.
Spectroscopy Analysis : Analyze spectroscopic data for chemical identification.

Mathematics Education Quantitative Research Topics

Mathematics Curriculum Analysis : Analyze curriculum effectiveness in mathematics education.
Mathematics Assessment Development : Develop quantitative assessments for mathematics skills.

Quantitative Research Topics In Social Research

Social Network Analysis : Analyze social network structures and dynamics.
Survey Research : Conduct quantitative surveys on social issues and trends.

Quantitative Research Topics In Computational Neuroscience

Neural Network Modeling : Model neural networks and brain functions computationally.
Brain Connectivity Analysis : Analyze functional and structural brain connectivity.

Best Topics In Transportation Engineering

Traffic Flow Modeling : Model and optimize traffic flow in urban areas.
Public Transportation Efficiency : Analyze the efficiency of public transportation systems.

Good Quantitative Research Topics In Energy Economics

Energy Policy Analysis : Evaluate the economic impact of energy policies.
Renewable Energy Cost-Benefit Analysis : Assess the economic viability of renewable energy projects.

Quantum Information Science

Quantum Cryptography Protocols : Develop and analyze quantum cryptography protocols.
Quantum Key Distribution : Study the security of quantum key distribution systems.

Human Genetics

Genome Editing Ethics : Investigate ethical issues in genome editing technologies.
Population Genomics : Analyze genomic data for population genetics research.

Marine Biology

Coral Reef Health Assessment : Quantitatively assess the health of coral reefs.
Marine Ecosystem Modeling : Model marine ecosystems and biodiversity.

Data Science and Machine Learning

Machine Learning Explainability : Develop methods for explaining machine learning models.
Data Privacy in Machine Learning : Study privacy issues in machine learning applications.
Deep Learning for Image Analysis : Develop deep learning models for image recognition.

Environmental Engineering

Robotics and automation, materials engineering, nuclear engineering, biomedical engineering, chemical engineering, renewable energy, astronomy and space sciences, psychology and cognitive science, geology and geological engineering, forensic science, cybersecurity, mathematical biology, chemical analysis, mathematics education, quantitative social research, computational neuroscience, quantitative research topics in transportation engineering, quantitative research topics in energy economics, topics in quantum information science, amazing quantitative research topics in human genetics, quantitative research topics in marine biology, what is a common goal of qualitative and quantitative research.

A common goal of both qualitative and quantitative research is to generate knowledge and gain a deeper understanding of a particular phenomenon or topic. However, they approach this goal in different ways:

1. Understanding a Phenomenon

Both types of research aim to understand and explain a specific phenomenon, whether it’s a social issue, a natural process, a human behavior, or a complex event.

2. Testing Hypotheses

Both qualitative and quantitative research can involve hypothesis testing. While qualitative research may not use statistical hypothesis tests in the same way as quantitative research, it often tests hypotheses or research questions by examining patterns and themes in the data.

3. Contributing to Knowledge

Researchers in both approaches seek to contribute to the body of knowledge in their respective fields. They aim to answer important questions, address gaps in existing knowledge, and provide insights that can inform theory, practice, or policy.

4. Informing Decision-Making

Research findings from both qualitative and quantitative studies can be used to inform decision-making in various domains, whether it’s in academia, government, industry, healthcare, or social services.

5. Enhancing Understanding

Both approaches strive to enhance our understanding of complex phenomena by systematically collecting and analyzing data. They aim to provide evidence-based explanations and insights.

6. Application

Research findings from both qualitative and quantitative studies can be applied to practical situations. For example, the results of a quantitative study on the effectiveness of a new drug can inform medical treatment decisions, while qualitative research on customer preferences can guide marketing strategies.

7. Contributing to Theory

In academia, both types of research contribute to the development and refinement of theories in various disciplines. Quantitative research may provide empirical evidence to support or challenge existing theories, while qualitative research may generate new theoretical frameworks or perspectives.

Conclusion – Quantitative Research Topics For STEM Students

So, selecting a quantitative research topic for STEM students is a pivotal decision that can shape the trajectory of your academic and professional journey. The process involves a thoughtful exploration of your interests, a thorough review of the existing literature, consideration of available resources, and the formulation of a clear and specific research question.

Your chosen topic should resonate with your passions, align with your academic or career goals, and offer the potential to contribute to the body of knowledge in your STEM field. Whether you’re delving into physics, biology, engineering, mathematics, or any other STEM discipline, the right research topic can spark curiosity, drive innovation, and lead to valuable insights.

Moreover, quantitative research in STEM not only expands the boundaries of human knowledge but also has the power to address real-world challenges, improve technology, and enhance our understanding of the natural world. It is a journey that demands dedication, intellectual rigor, and an unwavering commitment to scientific inquiry.

What is quantitative research in STEM?

Quantitative research in this context is designed to improve our understanding of the science system’s workings, structural dependencies and dynamics.

What are good examples of quantitative research?

Surveys and questionnaires serve as common examples of quantitative research. They involve collecting data from many respondents and analyzing the results to identify trends, patterns

What are the 4 C’s in STEM?

They became known as the “Four Cs” — critical thinking, communication, collaboration, and creativity.

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Exploring Grade 12 Quantitative Research Titles for Student Inquiry

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Grade 12 is a pivotal year for students, as they approach the culmination of their high school journey and prepare for the next phase of their lives. One significant aspect of this final year is the completion of a quantitative research project, which not only demonstrates their academic abilities but also fosters critical thinking, problem-solving, and analytical skills. In this article, we delve into a selection of compelling quantitative research titles suitable for Grade 12 students. These titles encompass various disciplines and offer students the opportunity to explore diverse topics while honing their research skills.

The Impact of Social Media Usage on Academic Performance Among High School Students:

In an era dominated by social media, this research title presents a relevant and pressing issue for Grade 12 students to investigate. Students can delve into the correlation between excessive social media usage and academic achievement, exploring factors such as time management, distraction, and psychological effects.

Gender Disparities in STEM Education: A Quantitative Analysis:

STEM (Science, Technology, Engineering, and Mathematics) education remains an area where gender disparities persist. This research title allows students to examine statistical data to identify patterns of participation and achievement among male and female students in STEM subjects. Potential factors such as societal expectations, teacher bias, and self-perception can be explored.

The Relationship Between Sleep Quality and Academic Performance:

Sleep plays a crucial role in cognitive function and academic success. Students can design surveys or conduct experiments to collect data on their peers’ sleep habits and academic outcomes. Analyzing this data can provide insights into the impact of sleep quality on factors such as memory retention, concentration, and overall performance in school.

Investigating the Effects of Exercise on Stress Levels Among High School Students:

With academic pressure and extracurricular activities, high school can be a stressful environment for students. This research title offers students the opportunity to explore the relationship between physical activity and stress reduction. By collecting quantitative data through surveys or physiological measurements, students can assess the effectiveness of exercise in managing stress levels.

The Influence of Parental Involvement on Student Achievement:

Parental involvement has long been recognized as a significant factor in student success. This research title allows students to investigate the extent to which parental support, communication, and involvement impact academic performance. Surveys or interviews can be utilized to gather data from both students and parents, facilitating an analysis of correlations and trends.

Examining the Relationship Between Socioeconomic Status and Access to Education Resources:

Equity in education remains a critical issue worldwide. This research title encourages students to explore disparities in access to educational resources such as textbooks, technology, and tutoring services based on socioeconomic status. By collecting quantitative data through surveys or case studies, students can assess the extent of these disparities and their implications for academic achievement.

The Efficacy of Technology Integration in the Classroom:

As technology continues to transform education, it is essential to evaluate its impact on student learning. This research title enables students to investigate the effectiveness of technology integration in enhancing academic engagement, comprehension, and outcomes. Quantitative methods such as pre- and post-tests or student performance metrics can be utilized to measure the impact of technology-based interventions.

Analyzing Factors Influencing Career Aspirations Among High School Students:

Career exploration is a crucial aspect of the transition from high school to post-secondary education or the workforce. This research title allows students to examine various factors, such as interests, aptitudes, parental influence, and socioeconomic background, that shape students’ career aspirations. Surveys or longitudinal studies can be employed to gather quantitative data and identify trends over time.

Conclusion:

Grade 12 quantitative research projects offer students the opportunity to engage in meaningful inquiry, explore real-world issues, and develop essential research skills. The diverse range of research titles presented in this article reflects the interdisciplinary nature of contemporary education and encourages students to think critically, analyze data, and draw evidence-based conclusions. By undertaking these research projects, Grade 12 students not only contribute to the body of knowledge but also prepare themselves for future academic and professional endeavors.

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100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

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Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

Consider your area of studies
Look out for relevant subjects in the area
Expert advice may come in handy
Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

The title should seem interesting to readers
Ensure that the title represents the content of the research paper
Reflect on the tone of the writing in the title
The title should contain important keywords in your chosen subject to help readers find your paper
The title should not be too lengthy
It should be grammatically correct and creative
It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

What is the correlation between inflation rates and unemployment rates?
Has climate adaptation influenced the mitigation of funds allocation?
Job satisfaction and employee turnover: What is the link?
A look at the relationship between poor households and the development of entrepreneurship skills
Urbanization and economic growth: What is the link between these elements?
Does education achievement influence people’s economic status?
What is the impact of solar electricity on the wholesale energy market?
Debt accumulation and retirement: What is the relationship between these concepts?
Can people with psychiatric disorders develop independent living skills?
Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

The difference between the colorific intake of men and women in your country
Top strategies used to measure customer satisfaction and how they work
Black Friday sales: are they profitable?
The correlation between estimated target market and practical competitive risk assignment
Are smartphones making us brighter or dumber?
Nuclear families Vs. Joint families: Is there a difference?
What will society look like in the absence of organized religion?
A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
How does emotional stability influence your overall well-being?
The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

How much exercise is good for lasting physical well-being?
A comparison of the nutritional therapy uses and contemporary medical approaches
Does sugar intake have a direct impact on diabetes diagnosis?
Education attainment: Does it influence crime rates in society?
Is there an actual link between obesity and cancer rates?
Do kids with siblings have better social skills than those without?
Computer games and their impact on the young generation
Has social media marketing taken over conventional marketing strategies?
The impact of technology development on human relationships and communication
What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

Habitation fragmentation and biodiversity loss: What is the link?
Radiation has affected biodiversity: Assessing its effects
An assessment of the impact of the CORONA virus on global population growth
Is the pandemic truly over, or have human bodies built resistance against the virus?
The ozone hole and its impact on the environment
The greenhouse gas effect: What is it and how has it impacted the atmosphere
GMO crops: are they good or bad for your health?
Is there a direct link between education quality and job attainment?
How have education systems changed from traditional to modern times?
The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

Online classes: are they helpful or not?
What changes has the global CORONA pandemic had on the population growth curve?
Daily habits influenced by the global pandemic
An analysis of the impact of culture on people’s personalities
How has feminism influenced the education system’s approach to the girl child’s education?
Academic competition: what are its benefits and downsides for students?
Is there a link between education and student integrity?
An analysis of how the education sector can influence a country’s economy
An overview of the link between crime rates and concern for crime
Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

What are the impacts of online games on students?
Sex education in schools: how important is it?
Should schools be teaching about safe sex in their sex education classes?
The correlation between extreme parent interference on student academic performance
Is there a real link between academic marks and intelligence?
Teacher feedback: How necessary is it, and how does it help students?
An analysis of modern education systems and their impact on student performance
An overview of the link between academic performance/marks and intelligence
Are grading systems helpful or harmful to students?
What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

A look at the pre-corona and post-corona economy
How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
An evaluation of mortality rates of heart attacks
Effective treatments for cardiovascular issues and their prevention
A comparison of the effectiveness of home care and nursing home care
Strategies for managing effective dissemination of information to modern students
How does educational discrimination influence students’ futures?
The impacts of unfavorable classroom environment and bullying on students and teachers
An overview of the implementation of STEM education to K-12 students
How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

An elaborate study of the influence of telemedicine in healthcare practices
How has scientific innovation influenced the defense or military system?
The link between technology and people’s mental health
Has social media helped create awareness or worsened people’s mental health?
How do engineers promote green technology?
How can engineers raise sustainability in building and structural infrastructures?
An analysis of how decision-making is dependent on someone’s sub-conscious
A comprehensive study of ADHD and its impact on students’ capabilities
The impact of racism on people’s mental health and overall wellbeing
How has the current surge in social activism helped shape people’s relationships?

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The prevalence of nonverbal communication in social control and people’s interactions
The impacts of stress on people’s behavior in society
A study of the connection between capital structures and corporate strategies
How do changes in credit ratings impact equality returns?
A quantitative analysis of the effect of bond rating changes on stock prices
The impact of semantics on web technology
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The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
The role of income inequality in economy growth
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Does studying mathematics help implement data safety for businesses
How are art-related subjects interdependent with mathematics?
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A deep insight into how people view eco-tourisms
Religion vs. hospitality: Details on their correlation
Has your country’s tourist sector revived after the pandemic?
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Are there similarities between the English and French vocabulary?
How do politicians use persuasive language in political speeches?
The correlation between popular culture and translation

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How do world leaders use language to change the emotional climate in their nations?
Extensive research on how linguistics cultivate political buzzwords
The impact of globalization on the global tourism sector
An analysis of the effects of the pandemic on the worldwide hospitality sector
The influence of social media platforms on people’s choice of tourism destinations
Educational tourism: What is it and what you should know about it
Why do college students experience math anxiety?
Is math anxiety a phenomenon?
A guide on effective ways to fight cultural bias in modern society
Creative ways to solve the overpopulation issue

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The link between global warming and climate change
What is the greenhouse gas impact on biodiversity and the atmosphere
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The impact of alcohol addiction on the younger generation
How has social media influenced human relationships?
Has education helped boost feminism among men and women?
Are computers in classrooms beneficial or detrimental to students?
How has social media improved bullying rates among teenagers?

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What is the right age to introduce sex education for students
Can extreme punishment help reduce alcohol consumption among teenagers?
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The link between globalization and the local economy collapses
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The correlation between video games and crime rates
Do college studies impact future job satisfaction?
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How the country’s economy is fairing after the pandemic
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How is the war in Ukraine affecting the global economy?
Can social media riots affect political decisions?

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Military support and economic development: What is the impact in developing nations?
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Is there a scientific link between abortion and the definition of viability?

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Does weight loss indicate good or poor health?
Should schools do away with grading systems?
The impact of culture on student interactions and personalities
How can parents successfully protect their kids from the dangers of the internet?
Is the US education system better or worse than Europe’s?

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Creating shareholder value in business: How important is it?
The changes in credit ratings and their impact on equity returns
The importance of data privacy laws in business operations
How do businesses benefit from e-waste and carbon footprint reduction?
Organizational culture in business: what is its importance?

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Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program

Locke Davenport Huyer ORCID: orcid.org/0000-0003-1526-7122 1 , 2 na1 ,
Neal I. Callaghan ORCID: orcid.org/0000-0001-8214-3395 1 , 3 na1 ,
Sara Dicks 4 ,
Edward Scherer 4 ,
Andrey I. Shukalyuk 1 ,
Margaret Jou 4 &
Dawn M. Kilkenny ORCID: orcid.org/0000-0002-3899-9767 1 , 5

npj Science of Learning volume 5 , Article number: 17 ( 2020 ) Cite this article

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The multi-disciplinary nature of science, technology, engineering, and math (STEM) careers often renders difficulty for high school students navigating from classroom knowledge to post-secondary pursuits. Discrepancies between the knowledge-based high school learning approach and the experiential approach of future studies leaves some students disillusioned by STEM. We present Discovery , a term-long inquiry-focused learning model delivered by STEM graduate students in collaboration with high school teachers, in the context of biomedical engineering. Entire classes of high school STEM students representing diverse cultural and socioeconomic backgrounds engaged in iterative, problem-based learning designed to emphasize critical thinking concomitantly within the secondary school and university environments. Assessment of grades and survey data suggested positive impact of this learning model on students’ STEM interests and engagement, notably in under-performing cohorts, as well as repeating cohorts that engage in the program on more than one occasion. Discovery presents a scalable platform that stimulates persistence in STEM learning, providing valuable learning opportunities and capturing cohorts of students that might otherwise be under-engaged in STEM.

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Introduction.

High school students with diverse STEM interests often struggle to understand the STEM experience outside the classroom 1 . The multi-disciplinary nature of many career fields can foster a challenge for students in their decision to enroll in appropriate high school courses while maintaining persistence in study, particularly when these courses are not mandatory 2 . Furthermore, this challenge is amplified by the known discrepancy between the knowledge-based learning approach common in high schools and the experiential, mastery-based approaches afforded by the subsequent undergraduate model 3 . In the latter, focused classes, interdisciplinary concepts, and laboratory experiences allow for the application of accumulated knowledge, practice in problem solving, and development of both general and technical skills 4 . Such immersive cooperative learning environments are difficult to establish in the secondary school setting and high school teachers often struggle to implement within their classroom 5 . As such, high school students may become disillusioned before graduation and never experience an enriched learning environment, despite their inherent interests in STEM 6 .

It cannot be argued that early introduction to varied math and science disciplines throughout high school is vital if students are to pursue STEM fields, especially within engineering 7 . However, the majority of literature focused on student interest and retention in STEM highlights outcomes in US high school learning environments, where the sciences are often subject-specific from the onset of enrollment 8 . In contrast, students in the Ontario (Canada) high school system are required to complete Level 1 and 2 core courses in science and math during Grades 9 and 10; these courses are offered as ‘applied’ or ‘academic’ versions and present broad topics of content 9 . It is not until Levels 3 and 4 (generally Grades 11 and 12, respectively) that STEM classes become subject-specific (i.e., Biology, Chemistry, and/or Physics) and are offered as “university”, “college”, or “mixed” versions, designed to best prepare students for their desired post-secondary pursuits 9 . Given that Levels 3 and 4 science courses are not mandatory for graduation, enrollment identifies an innate student interest in continued learning. Furthermore, engagement in these post-secondary preparatory courses is also dependent upon achieving successful grades in preceding courses, but as curriculum becomes more subject-specific, students often yield lower degrees of success in achieving course credit 2 . Therefore, it is imperative that learning supports are best focused on ensuring that those students with an innate interest are able to achieve success in learning.

When given opportunity and focused support, high school students are capable of successfully completing rigorous programs at STEM-focused schools 10 . Specialized STEM schools have existed in the US for over 100 years; generally, students are admitted after their sophomore year of high school experience (equivalent to Grade 10) based on standardized test scores, essays, portfolios, references, and/or interviews 11 . Common elements to this learning framework include a diverse array of advanced STEM courses, paired with opportunities to engage in and disseminate cutting-edge research 12 . Therein, said research experience is inherently based in the processes of critical thinking, problem solving, and collaboration. This learning framework supports translation of core curricular concepts to practice and is fundamental in allowing students to develop better understanding and appreciation of STEM career fields.

Despite the described positive attributes, many students do not have the ability or resources to engage within STEM-focused schools, particularly given that they are not prevalent across Canada, and other countries across the world. Consequently, many public institutions support the idea that post-secondary led engineering education programs are effective ways to expose high school students to engineering education and relevant career options, and also increase engineering awareness 13 . Although singular class field trips are used extensively to accomplish such programs, these may not allow immersive experiences for application of knowledge and practice of skills that are proven to impact long-term learning and influence career choices 14 , 15 . Longer-term immersive research experiences, such as after-school programs or summer camps, have shown successful at recruiting students into STEM degree programs and careers, where longevity of experience helps foster self-determination and interest-led, inquiry-based projects 4 , 16 , 17 , 18 , 19 .

Such activities convey the elements that are suggested to make a post-secondary led high school education programs successful: hands-on experience, self-motivated learning, real-life application, immediate feedback, and problem-based projects 20 , 21 . In combination with immersion in university teaching facilities, learning is authentic and relevant, similar to the STEM school-focused framework, and consequently representative of an experience found in actual STEM practice 22 . These outcomes may further be a consequence of student engagement and attitude: Brown et al. studied the relationships between STEM curriculum and student attitudes, and found the latter played a more important role in intention to persist in STEM when compared to self-efficacy 23 . This is interesting given that student self-efficacy has been identified to influence ‘motivation, persistence, and determination’ in overcoming challenges in a career pathway 24 . Taken together, this suggests that creation and delivery of modern, exciting curriculum that supports positive student attitudes is fundamental to engage and retain students in STEM programs.

Supported by the outcomes of identified effective learning strategies, University of Toronto (U of T) graduate trainees created a novel high school education program Discovery , to develop a comfortable yet stimulating environment of inquiry-focused iterative learning for senior high school students (Grades 11 & 12; Levels 3 & 4) at non-specialized schools. Built in strong collaboration with science teachers from George Harvey Collegiate Institute (Toronto District School Board), Discovery stimulates application of STEM concepts within a unique term-long applied curriculum delivered iteratively within both U of T undergraduate teaching facilities and collaborating high school classrooms 25 . Based on the volume of medically-themed news and entertainment that is communicated to the population at large, the rapidly-growing and diverse field of biomedical engineering (BME) were considered an ideal program context 26 . In its definition, BME necessitates cross-disciplinary STEM knowledge focused on the betterment of human health, wherein Discovery facilitates broadening student perspective through engaging inquiry-based projects. Importantly, Discovery allows all students within a class cohort to work together with their classroom teacher, stimulating continued development of a relevant learning community that is deemed essential for meaningful context and important for transforming student perspectives and understandings 27 , 28 . Multiple studies support the concept that relevant learning communities improve student attitudes towards learning, significantly increasing student motivation in STEM courses, and consequently improving the overall learning experience 29 . Learning communities, such as that provided by Discovery , also promote the formation of self-supporting groups, greater active involvement in class, and higher persistence rates for participating students 30 .

The objective of Discovery , through structure and dissemination, is to engage senior high school science students in challenging, inquiry-based practical BME activities as a mechanism to stimulate comprehension of STEM curriculum application to real-world concepts. Consequent focus is placed on critical thinking skill development through an atmosphere of perseverance in ambiguity, something not common in a secondary school knowledge-focused delivery but highly relevant in post-secondary STEM education strategies. Herein, we describe the observed impact of the differential project-based learning environment of Discovery on student performance and engagement. We identify the value of an inquiry-focused learning model that is tangible for students who struggle in a knowledge-focused delivery structure, where engagement in conceptual critical thinking in the relevant subject area stimulates student interest, attitudes, and resulting academic performance. Assessment of study outcomes suggests that when provided with a differential learning opportunity, student performance and interest in STEM increased. Consequently, Discovery provides an effective teaching and learning framework within a non-specialized school that motivates students, provides opportunity for critical thinking and problem-solving practice, and better prepares them for persistence in future STEM programs.

Program delivery

The outcomes of the current study result from execution of Discovery over five independent academic terms as a collaboration between Institute of Biomedical Engineering (graduate students, faculty, and support staff) and George Harvey Collegiate Institute (science teachers and administration) stakeholders. Each term, the program allowed senior secondary STEM students (Grades 11 and 12) opportunity to engage in a novel project-based learning environment. The program structure uses the problem-based engineering capstone framework as a tool of inquiry-focused learning objectives, motivated by a central BME global research topic, with research questions that are inter-related but specific to the curriculum of each STEM course subject (Fig. 1 ). Over each 12-week term, students worked in teams (3–4 students) within their class cohorts to execute projects with the guidance of U of T trainees ( Discovery instructors) and their own high school teacher(s). Student experimental work was conducted in U of T teaching facilities relevant to the research study of interest (i.e., Biology and Chemistry-based projects executed within Undergraduate Teaching Laboratories; Physics projects executed within Undergraduate Design Studios). Students were introduced to relevant techniques and safety procedures in advance of iterative experimentation. Importantly, this experience served as a course term project for students, who were assessed at several points throughout the program for performance in an inquiry-focused environment as well as within the regular classroom (Fig. 1 ). To instill the atmosphere of STEM, student teams delivered their outcomes in research poster format at a final symposium, sharing their results and recommendations with other post-secondary students, faculty, and community in an open environment.

The general program concept (blue background; top left ) highlights a global research topic examined through student dissemination of subject-specific research questions, yielding multifaceted student outcomes (orange background; top right ). Each program term (term workflow, yellow background; bottom panel ), students work on program deliverables in class (blue), iterate experimental outcomes within university facilities (orange), and are assessed accordingly at numerous deliverables in an inquiry-focused learning model.

Over the course of five terms there were 268 instances of tracked student participation, representing 170 individual students. Specifically, 94 students participated during only one term of programming, 57 students participated in two terms, 16 students participated in three terms, and 3 students participated in four terms. Multiple instances of participation represent students that enrol in more than one STEM class during their senior years of high school, or who participated in Grade 11 and subsequently Grade 12. Students were surveyed before and after each term to assess program effects on STEM interest and engagement. All grade-based assessments were performed by high school teachers for their respective STEM class cohorts using consistent grading rubrics and assignment structure. Here, we discuss the outcomes of student involvement in this experiential curriculum model.

Student performance and engagement

Student grades were assigned, collected, and anonymized by teachers for each Discovery deliverable (background essay, client meeting, proposal, progress report, poster, and final presentation). Teachers anonymized collective Discovery grades, the component deliverable grades thereof, final course grades, attendance in class and during programming, as well as incomplete classroom assignments, for comparative study purposes. Students performed significantly higher in their cumulative Discovery grade than in their cumulative classroom grade (final course grade less the Discovery contribution; p < 0.0001). Nevertheless, there was a highly significant correlation ( p < 0.0001) observed between the grade representing combined Discovery deliverables and the final course grade (Fig. 2a ). Further examination of the full dataset revealed two student cohorts of interest: the “Exceeds Expectations” (EE) subset (defined as those students who achieved ≥1 SD [18.0%] grade differential in Discovery over their final course grade; N = 99 instances), and the “Multiple Term” (MT) subset (defined as those students who participated in Discovery more than once; 76 individual students that collectively accounted for 174 single terms of assessment out of the 268 total student-terms delivered) (Fig. 2b, c ). These subsets were not unrelated; 46 individual students who had multiple experiences (60.5% of total MTs) exhibited at least one occasion in achieving a ≥18.0% grade differential. As students participated in group work, there was concern that lower-performing students might negatively influence the Discovery grade of higher-performing students (or vice versa). However, students were observed to self-organize into groups where all individuals received similar final overall course grades (Fig. 2d ), thereby alleviating these concerns.

a Linear regression of student grades reveals a significant correlation ( p = 0.0009) between Discovery performance and final course grade less the Discovery contribution to grade, as assessed by teachers. The dashed red line and intervals represent the theoretical 1:1 correlation between Discovery and course grades and standard deviation of the Discovery -course grade differential, respectively. b , c Identification of subgroups of interest, Exceeds Expectations (EE; N = 99, orange ) who were ≥+1 SD in Discovery -course grade differential and Multi-Term (MT; N = 174, teal ), of which N = 65 students were present in both subgroups. d Students tended to self-assemble in working groups according to their final course performance; data presented as mean ± SEM. e For MT students participating at least 3 terms in Discovery , there was no significant correlation between course grade and time, while ( f ) there was a significant correlation between Discovery grade and cumulative terms in the program. Histograms of total absences per student in ( g ) Discovery and ( h ) class (binned by 4 days to be equivalent in time to a single Discovery absence).

The benefits experienced by MT students seemed progressive; MT students that participated in 3 or 4 terms ( N = 16 and 3, respectively ) showed no significant increase by linear regression in their course grade over time ( p = 0.15, Fig. 2e ), but did show a significant increase in their Discovery grades ( p = 0.0011, Fig. 2f ). Finally, students demonstrated excellent Discovery attendance; at least 91% of participants attended all Discovery sessions in a given term (Fig. 2g ). In contrast, class attendance rates reveal a much wider distribution where 60.8% (163 out of 268 students) missed more than 4 classes (equivalent in learning time to one Discovery session) and 14.6% (39 out of 268 students) missed 16 or more classes (equivalent in learning time to an entire program of Discovery ) in a term (Fig. 2h ).

Discovery EE students (Fig. 3 ), roughly by definition, obtained lower course grades ( p < 0.0001, Fig. 3a ) and higher final Discovery grades ( p = 0.0004, Fig. 3b ) than non-EE students. This cohort of students exhibited program grades higher than classmates (Fig. 3c–h ); these differences were significant in every category with the exception of essays, where they outperformed to a significantly lesser degree ( p = 0.097; Fig. 3c ). There was no statistically significant difference in EE vs. non-EE student classroom attendance ( p = 0.85; Fig. 3i, j ). There were only four single day absences in Discovery within the EE subset; however, this difference was not statistically significant ( p = 0.074).

The “Exceeds Expectations” (EE) subset of students (defined as those who received a combined Discovery grade ≥1 SD (18.0%) higher than their final course grade) performed ( a ) lower on their final course grade and ( b ) higher in the Discovery program as a whole when compared to their classmates. d – h EE students received significantly higher grades on each Discovery deliverable than their classmates, except for their ( c ) introductory essays and ( h ) final presentations. The EE subset also tended ( i ) to have a higher relative rate of attendance during Discovery sessions but no difference in ( j ) classroom attendance. N = 99 EE students and 169 non-EE students (268 total). Grade data expressed as mean ± SEM.

Discovery MT students (Fig. 4 ), although not receiving significantly higher grades in class than students participating in the program only one time ( p = 0.29, Fig. 4a ), were observed to obtain higher final Discovery grades than single-term students ( p = 0.0067, Fig. 4b ). Although trends were less pronounced for individual MT student deliverables (Fig. 4c–h ), this student group performed significantly better on the progress report ( p = 0.0021; Fig. 4f ). Trends of higher performance were observed for initial proposals and final presentations ( p = 0.081 and 0.056, respectively; Fig. 4e, h ); all other deliverables were not significantly different between MT and non-MT students (Fig. 4c, d, g ). Attendance in Discovery ( p = 0.22) was also not significantly different between MT and non-MT students, although MT students did miss significantly less class time ( p = 0.010) (Fig. 4i, j ). Longitudinal assessment of individual deliverables for MT students that participated in three or more Discovery terms (Fig. 5 ) further highlights trend in improvement (Fig. 2f ). Greater performance over terms of participation was observed for essay ( p = 0.0295, Fig. 5a ), client meeting ( p = 0.0003, Fig. 5b ), proposal ( p = 0.0004, Fig. 5c ), progress report ( p = 0.16, Fig. 5d ), poster ( p = 0.0005, Fig. 5e ), and presentation ( p = 0.0295, Fig. 5f ) deliverable grades; these trends were all significant with the exception of the progress report ( p = 0.16, Fig. 5d ) owing to strong performance in this deliverable in all terms.

The “multi-term” (MT) subset of students (defined as having attended more than one term of Discovery ) demonstrated favorable performance in Discovery , ( a ) showing no difference in course grade compared to single-term students, but ( b outperforming them in final Discovery grade. Independent of the number of times participating in Discovery , MT students did not score significantly differently on their ( c ) essay, ( d ) client meeting, or ( g ) poster. They tended to outperform their single-term classmates on the ( e ) proposal and ( h ) final presentation and scored significantly higher on their ( f ) progress report. MT students showed no statistical difference in ( i ) Discovery attendance but did show ( j ) higher rates of classroom attendance than single-term students. N = 174 MT instances of student participation (76 individual students) and 94 single-term students. Grade data expressed as mean ± SEM.

Longitudinal assessment of a subset of MT student participants that participated in three ( N = 16) or four ( N = 3) terms presents a significant trend of improvement in their ( a ) essay, ( b ) client meeting, ( c ) proposal, ( e ) poster, and ( f ) presentation grade. d Progress report grades present a trend in improvement but demonstrate strong performance in all terms, limiting potential for student improvement. Grade data are presented as individual student performance; each student is represented by one color; data is fitted with a linear trendline (black).

Finally, the expansion of Discovery to a second school of lower LOI (i.e., nominally higher aggregate SES) allowed for the assessment of program impact in a new population over 2 terms of programming. A significant ( p = 0.040) divergence in Discovery vs. course grade distribution from the theoretical 1:1 relationship was found in the new cohort (S 1 Appendix , Fig. S 1 ), in keeping with the pattern established in this study.

Teacher perceptions

Qualitative observation in the classroom by high school teachers emphasized the value students independently placed on program participation and deliverables. Throughout the term, students often prioritized Discovery group assignments over other tasks for their STEM courses, regardless of academic weight and/or due date. Comparing within this student population, teachers spoke of difficulties with late and incomplete assignments in the regular curriculum but found very few such instances with respect to Discovery -associated deliverables. Further, teachers speculated on the good behavior and focus of students in Discovery programming in contrast to attentiveness and behavior issues in their school classrooms. Multiple anecdotal examples were shared of renewed perception of student potential; students that exhibited poor academic performance in the classroom often engaged with high performance in this inquiry-focused atmosphere. Students appeared to take a sense of ownership, excitement, and pride in the setting of group projects oriented around scientific inquiry, discovery, and dissemination.

Student perceptions

Students were asked to consider and rank the academic difficulty (scale of 1–5, with 1 = not challenging and 5 = highly challenging) of the work they conducted within the Discovery learning model. Considering individual Discovery terms, at least 91% of students felt the curriculum to be sufficiently challenging with a 3/5 or higher ranking (Term 1: 87.5%, Term 2: 93.4%, Term 3: 85%, Term 4: 93.3%, Term 5: 100%), and a minimum of 58% of students indicating a 4/5 or higher ranking (Term 1: 58.3%, Term 2: 70.5%, Term 3: 67.5%, Term 4: 69.1%, Term 5: 86.4%) (Fig. 6a ).

a Histogram of relative frequency of perceived Discovery programming academic difficulty ranked from not challenging (1) to highly challenging (5) for each session demonstrated the consistently perceived high degree of difficulty for Discovery programming (total responses: 223). b Program participation increased student comfort (94.6%) with navigating lab work in a university or college setting (total responses: 220). c Considering participation in Discovery programming, students indicated their increased (72.4%) or decreased (10.1%) likelihood to pursue future experiences in STEM as a measure of program impact (total responses: 217). d Large majority of participating students (84.9%) indicated their interest for future participation in Discovery (total responses: 212). Students were given the opportunity to opt out of individual survey questions, partially completed surveys were included in totals.

The majority of students (94.6%) indicated they felt more comfortable with the idea of performing future work in a university STEM laboratory environment given exposure to university teaching facilities throughout the program (Fig. 6b ). Students were also queried whether they were (i) more likely, (ii) less likely, or (iii) not impacted by their experience in the pursuit of STEM in the future. The majority of participants (>82%) perceived impact on STEM interests, with 72.4% indicating they were more likely to pursue these interests in the future (Fig. 6c ). When surveyed at the end of term, 84.9% of students indicated they would participate in the program again (Fig. 6d ).

We have described an inquiry-based framework for implementing experiential STEM education in a BME setting. Using this model, we engaged 268 instances of student participation (170 individual students who participated 1–4 times) over five terms in project-based learning wherein students worked in peer-based teams under the mentorship of U of T trainees to design and execute the scientific method in answering a relevant research question. Collaboration between high school teachers and Discovery instructors allowed for high school student exposure to cutting-edge BME research topics, participation in facilitated inquiry, and acquisition of knowledge through scientific discovery. All assessments were conducted by high school teachers and constituted a fraction (10–15%) of the overall course grade, instilling academic value for participating students. As such, students exhibited excitement to learn as well as commitment to their studies in the program.

Through our observations and analysis, we suggest there is value in differential learning environments for students that struggle in a knowledge acquisition-focused classroom setting. In general, we observed a high level of academic performance in Discovery programming (Fig. 2a ), which was highlighted exceptionally in EE students who exhibited greater academic performance in Discovery deliverables compared to normal coursework (>18% grade improvement in relevant deliverables). We initially considered whether this was the result of strong students influencing weaker students; however, group organization within each course suggests this is not the case (Fig. 2d ). With the exception of one class in one term (24 participants assigned by their teacher), students were allowed to self-organize into working groups and they chose to work with other students of relatively similar academic performance (as indicated by course grade), a trend observed in other studies 31 , 32 . Remarkably, EE students not only excelled during Discovery when compared to their own performance in class, but this cohort also achieved significantly higher average grades in each of the deliverables throughout the program when compared to the remaining Discovery cohort (Fig. 3 ). This data demonstrates the value of an inquiry-based learning environment compared to knowledge-focused delivery in the classroom in allowing students to excel. We expect that part of this engagement was resultant of student excitement with a novel learning opportunity. It is however a well-supported concept that students who struggle in traditional settings tend to demonstrate improved interest and motivation in STEM when given opportunity to interact in a hands-on fashion, which supports our outcomes 4 , 33 . Furthermore, these outcomes clearly represent variable student learning styles, where some students benefit from a greater exchange of information, knowledge and skills in a cooperative learning environment 34 . The performance of the EE group may not be by itself surprising, as the identification of the subset by definition required high performers in Discovery who did not have exceptionally high course grades; in addition, the final Discovery grade is dependent on the component assignment grades. However, the discrepancies between EE and non-EE groups attendance suggests that students were engaged by Discovery in a way that they were not by regular classroom curriculum.

In addition to quantified engagement in Discovery observed in academic performance, we believe remarkable attendance rates are indicative of the value students place in the differential learning structure. Given the differences in number of Discovery days and implications of missing one day of regular class compared to this immersive program, we acknowledge it is challenging to directly compare attendance data and therefore approximate this comparison with consideration of learning time equivalence. When combined with other subjective data including student focus, requests to work on Discovery during class time, and lack of discipline/behavior issues, the attendance data importantly suggests that students were especially engaged by the Discovery model. Further, we believe the increased commute time to the university campus (students are responsible for independent transit to campus, a much longer endeavour than the normal school commute), early program start time, and students’ lack of familiarity with the location are non-trivial considerations when determining the propensity of students to participate enthusiastically in Discovery . We feel this suggests the students place value on this team-focused learning and find it to be more applicable and meaningful to their interests.

Given post-secondary admission requirements for STEM programs, it would be prudent to think that students participating in multiple STEM classes across terms are the ones with the most inherent interest in post-secondary STEM programs. The MT subset, representing students who participated in Discovery for more than one term, averaged significantly higher final Discovery grades. The increase in the final Discovery grade was observed to result from a general confluence of improved performance over multiple deliverables and a continuous effort to improve in a STEM curriculum. This was reflected in longitudinal tracking of Discovery performance, where we observed a significant trend of improved performance. Interestingly, the high number of MT students who were included in the EE group suggests that students who had a keen interest in science enrolled in more than one course and in general responded well to the inquiry-based teaching method of Discovery , where scientific method was put into action. It stands to reason that students interested in science will continue to take STEM courses and will respond favorably to opportunities to put classroom theory to practical application.

The true value of an inquiry-based program such as Discovery may not be based in inspiring students to perform at a higher standard in STEM within the high school setting, as skills in critical thinking do not necessarily translate to knowledge-based assessment. Notably, students found the programming equally challenging throughout each of the sequential sessions, perhaps somewhat surprising considering the increasing number of repeat attendees in successive sessions (Fig. 6a ). Regardless of sub-discipline, there was an emphasis of perceived value demonstrated through student surveys where we observed indicated interest in STEM and comfort with laboratory work environments, and desire to engage in future iterations given the opportunity. Although non-quantitative, we perceive this as an indicator of significant student engagement, even though some participants did not yield academic success in the program and found it highly challenging given its ambiguity.

Although we observed that students become more certain of their direction in STEM, further longitudinal study is warranted to make claim of this outcome. Additionally, at this point in our assessment we cannot effectively assess the practical outcomes of participation, understanding that the immediate effects observed are subject to a number of factors associated with performance in the high school learning environment. Future studies that track graduates from this program will be prudent, in conjunction with an ever-growing dataset of assessment as well as surveys designed to better elucidate underlying perceptions and attitudes, to continue to understand the expected benefits of this inquiry-focused and partnered approach. Altogether, a multifaceted assessment of our early outcomes suggests significant value of an immersive and iterative interaction with STEM as part of the high school experience. A well-defined divergence from knowledge-based learning, focused on engagement in critical thinking development framed in the cutting-edge of STEM, may be an important step to broadening student perspectives.

In this study, we describe the short-term effects of an inquiry-based STEM educational experience on a cohort of secondary students attending a non-specialized school, and suggest that the framework can be widely applied across virtually all subjects where inquiry-driven and mentored projects can be undertaken. Although we have demonstrated replication in a second cohort of nominally higher SES (S 1 Appendix , Supplementary Fig. 1 ), a larger collection period with more students will be necessary to conclusively determine impact independent of both SES and specific cohort effects. Teachers may also find this framework difficult to implement depending on resources and/or institutional investment and support, particularly if post-secondary collaboration is inaccessible. Offerings to a specific subject (e.g., physics) where experiments yielding empirical data are logistically or financially simpler to perform may be valid routes of adoption as opposed to the current study where all subject cohorts were included.

As we consider Discovery in a bigger picture context, expansion and implementation of this model is translatable. Execution of the scientific method is an important aspect of citizen science, as the concepts of critical thing become ever-more important in a landscape of changing technological landscapes. Giving students critical thinking and problem-solving skills in their primary and secondary education provides value in the context of any career path. Further, we feel that this model is scalable across disciplines, STEM or otherwise, as a means of building the tools of inquiry. We have observed here the value of differential inclusive student engagement and critical thinking through an inquiry-focused model for a subset of students, but further to this an engagement, interest, and excitement across the body of student participants. As we educate the leaders of tomorrow, we suggest that use of an inquiry-focused model such as Discovery could facilitate growth of a data-driven critical thinking framework.

In conclusion, we have presented a model of inquiry-based STEM education for secondary students that emphasizes inclusion, quantitative analysis, and critical thinking. Student grades suggest significant performance benefits, and engagement data suggests positive student attitude despite the perceived challenges of the program. We also note a particular performance benefit to students who repeatedly engage in the program. This framework may carry benefits in a wide variety of settings and disciplines for enhancing student engagement and performance, particularly in non-specialized school environments.

Study design and implementation

Participants in Discovery include all students enrolled in university-stream Grade 11 or 12 biology, chemistry, or physics at the participating school over five consecutive terms (cohort summary shown in Table 1 ). Although student participation in educational content was mandatory, student grades and survey responses (administered by high school teachers) were collected from only those students with parent or guardian consent. Teachers replaced each student name with a unique coded identifier to preserve anonymity but enable individual student tracking over multiple terms. All data collected were analyzed without any exclusions save for missing survey responses; no power analysis was performed prior to data collection.

Ethics statement

This study was approved by the University of Toronto Health Sciences Research Ethics Board (Protocol # 34825) and the Toronto District School Board External Research Review Committee (Protocol # 2017-2018-20). Written informed consent was collected from parents or guardians of participating students prior to the acquisition of student data (both post-hoc academic data and survey administration). Data were anonymized by high school teachers for maintenance of academic confidentiality of individual students prior to release to U of T researchers.

Educational program overview

Students enrolled in university-preparatory STEM classes at the participating school completed a term-long project under the guidance of graduate student instructors and undergraduate student mentors as a mandatory component of their respective course. Project curriculum developed collaboratively between graduate students and participating high school teachers was delivered within U of T Faculty of Applied Science & Engineering (FASE) teaching facilities. Participation allows high school students to garner a better understanding as to how undergraduate learning and career workflows in STEM vary from traditional high school classroom learning, meanwhile reinforcing the benefits of problem solving, perseverance, teamwork, and creative thinking competencies. Given that Discovery was a mandatory component of course curriculum, students participated as class cohorts and addressed questions specific to their course subject knowledge base but related to the defined global health research topic (Fig. 1 ). Assessment of program deliverables was collectively assigned to represent 10–15% of the final course grade for each subject at the discretion of the respective STEM teacher.

The Discovery program framework was developed, prior to initiation of student assessment, in collaboration with one high school selected from the local public school board over a 1.5 year period of time. This partner school consistently scores highly (top decile) in the school board’s Learning Opportunities Index (LOI). The LOI ranks each school based on measures of external challenges affecting its student population therefore schools with the greatest level of external challenge receive a higher ranking 35 . A high LOI ranking is inversely correlated with socioeconomic status (SES); therefore, participating students are identified as having a significant number of external challenges that may affect their academic success. The mandatory nature of program participation was established to reach highly capable students who may be reluctant to engage on their own initiative, as a means of enhancing the inclusivity and impact of the program. The selected school partner is located within a reasonable geographical radius of our campus (i.e., ~40 min transit time from school to campus). This is relevant as participating students are required to independently commute to campus for Discovery hands-on experiences.

Each program term of Discovery corresponds with a five-month high school term. Lead university trainee instructors (3–6 each term) engaged with high school teachers 1–2 months in advance of high school student engagement to discern a relevant overarching global healthcare theme. Each theme was selected with consideration of (a) topics that university faculty identify as cutting-edge biomedical research, (b) expertise that Discovery instructors provide, and (c) capacity to showcase the diversity of BME. Each theme was sub-divided into STEM subject-specific research questions aligning with provincial Ministry of Education curriculum concepts for university-preparatory Biology, Chemistry, and Physics 9 that students worked to address, both on-campus and in-class, during a term-long project. The Discovery framework therefore provides students a problem-based learning experience reflective of an engineering capstone design project, including a motivating scientific problem (i.e., global topic), subject-specific research question, and systematic determination of a professional recommendation addressing the needs of the presented problem.

Discovery instructors were volunteers recruited primarily from graduate and undergraduate BME programs in the FASE. Instructors were organized into subject-specific instructional teams based on laboratory skills, teaching experience, and research expertise. The lead instructors of each subject (the identified 1–2 trainees that built curriculum with high school teachers) were responsible to organize the remaining team members as mentors for specific student groups over the course of the program term (~1:8 mentor to student ratio).

All Discovery instructors were familiarized with program expectations and trained in relevant workspace safety, in addition to engagement at a teaching workshop delivered by the Faculty Advisor (a Teaching Stream faculty member) at the onset of term. This workshop was designed to provide practical information on teaching and was co-developed with high school teachers based on their extensive training and experience in fundamental teaching methods. In addition, group mentors received hands-on training and guidance from lead instructors regarding the specific activities outlined for their respective subject programming (an exemplary term of student programming is available in S 2 Appendix) .

Discovery instructors were responsible for introducing relevant STEM skills and mentoring high school students for the duration of their projects, with support and mentorship from the Faculty Mentor. Each instructor worked exclusively throughout the term with the student groups to which they had been assigned, ensuring consistent mentorship across all disciplinary components of the project. In addition to further supporting university trainees in on-campus mentorship, high school teachers were responsible for academic assessment of all student program deliverables (Fig. 1 ; the standardized grade distribution available in S 3 Appendix ). Importantly, trainees never engaged in deliverable assessment; for continuity of overall course assessment, this remained the responsibility of the relevant teacher for each student cohort.

Throughout each term, students engaged within the university facilities four times. The first three sessions included hands-on lab sessions while the fourth visit included a culminating symposium for students to present their scientific findings (Fig. 1 ). On average, there were 4–5 groups of students per subject (3–4 students per group; ~20 students/class). Discovery instructors worked exclusively with 1–2 groups each term in the capacity of mentor to monitor and guide student progress in all project deliverables.

After introducing the selected global research topic in class, teachers led students in completion of background research essays. Students subsequently engaged in a subject-relevant skill-building protocol during their first visit to university teaching laboratory facilities, allowing opportunity to understand analysis techniques and equipment relevant for their assessment projects. At completion of this session, student groups were presented with a subject-specific research question as well as the relevant laboratory inventory available for use during their projects. Armed with this information, student groups continued to work in their classroom setting to develop group-specific experimental plans. Teachers and Discovery instructors provided written and oral feedback, respectively , allowing students an opportunity to revise their plans in class prior to on-campus experimental execution.

Once at the relevant laboratory environment, student groups executed their protocols in an effort to collect experimental data. Data analysis was performed in the classroom and students learned by trial & error to optimize their protocols before returning to the university lab for a second opportunity of data collection. All methods and data were re-analyzed in class in order for students to create a scientific poster for the purpose of study/experience dissemination. During a final visit to campus, all groups presented their findings at a research symposium, allowing students to verbally defend their process, analyses, interpretations, and design recommendations to a diverse audience including peers, STEM teachers, undergraduate and graduate university students, postdoctoral fellows and U of T faculty.

Data collection

Teachers evaluated their students on the following associated deliverables: (i) global theme background research essay; (ii) experimental plan; (iii) progress report; (iv) final poster content and presentation; and (v) attendance. For research purposes, these grades were examined individually and also as a collective Discovery program grade for each student. For students consenting to participation in the research study, all Discovery grades were anonymized by the classroom teacher before being shared with study authors. Each student was assigned a code by the teacher for direct comparison of deliverable outcomes and survey responses. All instances of “Final course grade” represent the prorated course grade without the Discovery component, to prevent confounding of quantitative analyses.

Survey instruments were used to gain insight into student attitudes and perceptions of STEM and post-secondary study, as well as Discovery program experience and impact (S 4 Appendix ). High school teachers administered surveys in the classroom only to students supported by parental permission. Pre-program surveys were completed at minimum 1 week prior to program initiation each term and exit surveys were completed at maximum 2 weeks post- Discovery term completion. Surveys results were validated using a principal component analysis (S 1 Appendix , Supplementary Fig. 2 ).

Identification and comparison of population subsets

From initial analysis, we identified two student subpopulations of particular interest: students who performed ≥1 SD [18.0%] or greater in the collective Discovery components of the course compared to their final course grade (“EE”), and students who participated in Discovery more than once (“MT”). These groups were compared individually against the rest of the respective Discovery population (“non-EE” and “non-MT”, respectively ). Additionally, MT students who participated in three or four (the maximum observed) terms of Discovery were assessed for longitudinal changes to performance in their course and Discovery grades. Comparisons were made for all Discovery deliverables (introductory essay, client meeting, proposal, progress report, poster, and presentation), final Discovery grade, final course grade, Discovery attendance, and overall attendance.

Statistical analysis

Student course grades were analyzed in all instances without the Discovery contribution (calculated from all deliverable component grades and ranging from 10 to 15% of final course grade depending on class and year) to prevent correlation. Aggregate course grades and Discovery grades were first compared by paired t-test, matching each student’s course grade to their Discovery grade for the term. Student performance in Discovery ( N = 268 instances of student participation, comprising 170 individual students that participated 1–4 times) was initially assessed in a linear regression of Discovery grade vs. final course grade. Trends in course and Discovery performance over time for students participating 3 or 4 terms ( N = 16 and 3 individuals, respectively ) were also assessed by linear regression. For subpopulation analysis (EE and MT, N = 99 instances from 81 individuals and 174 instances from 76 individuals, respectively ), each dataset was tested for normality using the D’Agostino and Pearson omnibus normality test. All subgroup comparisons vs. the remaining population were performed by Mann–Whitney U -test. Data are plotted as individual points with mean ± SEM overlaid (grades), or in histogram bins of 1 and 4 days, respectively , for Discovery and class attendance. Significance was set at α ≤ 0.05.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author DMK. These data are not publicly available due to privacy concerns of personal data according to the ethical research agreements supporting this study.

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Acknowledgements

This study has been possible due to the support of many University of Toronto trainee volunteers, including Genevieve Conant, Sherif Ramadan, Daniel Smieja, Rami Saab, Andrew Effat, Serena Mandla, Cindy Bui, Janice Wong, Dawn Bannerman, Allison Clement, Shouka Parvin Nejad, Nicolas Ivanov, Jose Cardenas, Huntley Chang, Romario Regeenes, Dr. Henrik Persson, Ali Mojdeh, Nhien Tran-Nguyen, Ileana Co, and Jonathan Rubianto. We further acknowledge the staff and administration of George Harvey Collegiate Institute and the Institute of Biomedical Engineering (IBME), as well as Benjamin Rocheleau and Madeleine Rocheleau for contributions to data collation. Discovery has grown with continued support of Dean Christopher Yip (Faculty of Applied Science and Engineering, U of T), and the financial support of the IBME and the National Science and Engineering Research Council (NSERC) PromoScience program (PROSC 515876-2017; IBME “Igniting Youth Curiosity in STEM” initiative co-directed by DMK and Dr. Penney Gilbert). LDH and NIC were supported by Vanier Canada graduate scholarships from the Canadian Institutes of Health Research and NSERC, respectively . DMK holds a Dean’s Emerging Innovation in Teaching Professorship in the Faculty of Engineering & Applied Science, U of T.

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These authors contributed equally: Locke Davenport Huyer, Neal I. Callaghan.

Authors and Affiliations

Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer, Neal I. Callaghan, Andrey I. Shukalyuk & Dawn M. Kilkenny

Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada

Locke Davenport Huyer

Translational Biology and Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, ON, Canada

Neal I. Callaghan

George Harvey Collegiate Institute, Toronto District School Board, Toronto, ON, Canada

Sara Dicks, Edward Scherer & Margaret Jou

Institute for Studies in Transdisciplinary Engineering Education & Practice, University of Toronto, Toronto, ON, Canada

Dawn M. Kilkenny

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LDH, NIC and DMK conceived the program structure, designed the study, and interpreted the data. LDH and NIC ideated programming, coordinated execution, and performed all data analysis. SD, ES, and MJ designed and assessed student deliverables, collected data, and anonymized data for assessment. SD assisted in data interpretation. AIS assisted in programming ideation and design. All authors provided feedback and approved the manuscript that was written by LDH, NIC and DMK.

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Correspondence to Dawn M. Kilkenny .

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Davenport Huyer, L., Callaghan, N.I., Dicks, S. et al. Enhancing senior high school student engagement and academic performance using an inclusive and scalable inquiry-based program. npj Sci. Learn. 5 , 17 (2020). https://doi.org/10.1038/s41539-020-00076-2

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Published: 16 May 2018

A study of the correlation between STEM career knowledge, mathematics self-efficacy, career interests, and career activities on the likelihood of pursuing a STEM career among middle school students

Karen A. Blotnicky 1 ,
Tamara Franz-Odendaal 2 ,
Frederick French 3 &
Phillip Joy 4

International Journal of STEM Education volume 5 , Article number: 22 ( 2018 ) Cite this article

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A sample of 1448 students in grades 7 and 9 was drawn from public schools in Atlantic Canada to explore students’ knowledge of science and mathematics requirements for science, technology, engineering, and mathematics (STEM) careers. Also explored were their mathematics self-efficacy (MSE), their future career interests, their preferences for particular career activities, and their likelihood to pursue a STEM career.

Analysis revealed that while older students had more knowledge about mathematics/science requirements for STEM careers, this knowledge was lacking overall. Also, students with higher MSE were more knowledgeable about STEM career requirements. Furthermore, students with higher MSE and STEM career knowledge were more likely to choose a STEM career. Students with greater interest in technical and scientific skills were also more likely to consider a STEM career than those who preferred career activities that involved practical, productive, and concrete activities.

Conclusions

The results of this study show that students in middle school have a limited STEM career knowledge with respect to subject requirements and with respect to what sort of activities these careers involve. Furthermore, students with low MSE have a declining interest in STEM careers. Our data thus support the need to improve access to knowledge to facilitate students’ understanding of STEM careers and the nature of STEM work. Exposure of students to STEM careers can enhance their interest in pursuing careers involving science, technology, engineering, and mathematics.

Globally, youth vary considerably in their level of science, technology, engineering, and mathematics (STEM) career knowledge, their career interests, and their intentions of pursuing a STEM career. STEM career knowledge, defined as a student’s familiarity with a particular STEM career, varies considerably based on the school’s STEM career guidance. The level of STEM career knowledge an individual has will directly affect one’s intentions of pursuing a STEM career in the future (Compeau 2016 ; Nugent et al. 2015 ; Zhang and Barnett 2015 ). Without adequate knowledge, there is a risk that students will dismiss a STEM-based career path as a potential option for their future. Consequently, student interest in a particular STEM career will wane, which will negatively influence their desire to participate in activities that serve to increase STEM career knowledge and awareness. Indeed, interventions have shown that equipping students with STEM career knowledge early increases their motivation to take more science and mathematics courses in high school (Harackiewicz et al. 2012 ).

Students’ career interest and their preferred future career activities will also affect their intention of pursuing a STEM career. A key predictor of STEM career interest at the end of high school is interest at the start of high school (Sadler et al. 2012 ). However, the positive attitudes towards science identified in youth age 10 sharply declines by age 14 (Murphy and Beggs 2005 ; Tai et al. 2006 ); the junior high school years are typically ages 12–14 years. An extensive study in 2015, surveying 24,000 students, showed that occupational intentions change dramatically between the 9th and 11th grade and that the relationship between STEM intention and motivation is highly time-sensitive (Mangu et al. 2015 ).

Both STEM career knowledge and career interests are also influenced by society at large. These society influencers include role models that students are exposed to either in person or through the media, the individual students interact with on a daily basis such as teachers, family members, and peers, as well as students’ extracurricular experiences (Dabney et al. 2012 ; Harackiewicz et al. 2012 ; Nugent et al. 2015 ; Sahin et al. 2014 ; Sahin et al. 2015 ; Schumacher et al. 2009 ; Sjaastad 2012 ; Steinke et al. 2009 ; Zhang and Barnett 2015 ). Collectively, these influencing factors predict the self-efficacy (i.e., one’s belief in one’s ability) youth hold about their career options as well as their outcome expectancies (Mangu et al. 2015 ). Self-efficacy is considered a major predictor guiding the selection of majors during high school and post-secondary education (Heilbronner 2009 ; Kelly et al. 2013 ).

The grades 7 through 9 years (12–15-year-olds) are the key time period for influencing STEM career interest and for building this self-efficacy with respect to mathematics and science. Thus, it is during the junior high (middle) school age that a student’s beliefs about competency and interests begin to solidify (Simpkins et al. 2006 ). It is at this time that student engagement activities and career knowledge should be at its highest. Social cognitive career theory (Lent 2005 ) acknowledges and hypothesizes that career interests, choice, and personal goals form a complex human agency process that includes performance, self-efficacy, and outcome expectations. For example, self-efficacy is positively related to student academic performance and science self-efficacy has been shown to impact student selection of science-related activities, which impacts their ultimate success and helps maintain interests (Britner and Pajares 2006 ; Parker et al. 2014 ; Richardson et al. 2012 ).

Early interest in STEM topics is an excellent predictor for later learning and eventual career interests and choice (DeBacker and Nelson 1999 ). Contextual and individual variables influence these social cognitive variables including factors such as parental, teacher, and peer cultural expectations (Lent et al. 1994 ). Nugent et al. ( 2015 ) found support for the social cognitive career theory (Lent et al. 1994 ) as a framework for examining STEM learning and career orientation outcomes by providing a way in which to view the socio-contextual, motivational, and instructional factors that can impact youth STEM interests.

Although 88% of parents believe they can help guide their children’s learning, less than 28% actually discuss the value of a STEM education with their children (“Let’s Talk Science Canada Annual Report,” 2015 ). Recent studies have also indicated that junior high students have an unclear view about engineering (Compeau 2016 ; Karatas et al. 2011 ) and science (Masnick et al. 2010 ) yet these are critical years in which to build STEM interest. The present paper builds on our previous study (Franz-Odendaal et al. 2016 ) and explores students’ knowledge of STEM career mathematics/science requirements and their mathematics self-efficacy (MSE) and how these shape students’ career interests and preferred career activities. Differences among grade 7 and 9 students with respect to career interests and activities, and the likelihood of pursuing a STEM career will be examined. While gender differences are important because STEM stereotypes are heavily biased towards males, these differences are beyond the scope of the current study. This study will examine who, what, and how youth are influenced in STEM career choice.

This study captured five main areas of interest: student knowledge of mathematics and science requirements that lead to STEM careers, MSE, career interests, career activity preferences, and their correlation with the likelihood to consider pursuing a STEM career among youth. Based on the literature, the following research questions were developed to guide this research.

RQ1: What is the correlation between grade level and students’ knowledge of high school requirements for STEM careers?

RQ2: What is the correlation between MSE and students’ knowledge of high school requirements for STEM careers?

RQ3: What is the correlation between MSE and students’ career interests and/or their preference for particular career activities?

RQ4: What is the association between student preferences for career interests and preferred career activities with grade level?

RQ5: What are the relationships between the following factors and the likelihood that students will choose a STEM career: grade level, MSE, student knowledge of mathematics/science requirements for post-secondary study for STEM careers, career interests and preferred career activities?

These research questions have not been explored in the context of Atlantic Canada, thus making this study relevant to the education system within Canada and globally.

Grade 7 and 9 students in the four Canadian Atlantic provinces (New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland) completed an online survey during their school hours. This research was approved by the university research ethics board. Permission to collect data in the schools was obtained from school board superintendents and parents. Schools were purposefully chosen from school families in geographic areas across Atlantic Canada. English and French language schools were included in the study. Data were weighted to ensure that the sample was representative by grade level, from each of the four Atlantic provinces. A total sample size of 1448 students was obtained across all four provinces in Atlantic Canada: New Brunswick (33%), Nova Scotia (38.4%), Prince Edward Island (6.5%), and Newfoundland-Labrador (22.1%). The sample was split almost evenly between grade 7 (48%) and grade 9 (52%). The sample was balanced with respect to gender (58% female to 42% male). Students ranged in age from 11 to 20 years with an average age of 13.5 years and a median age of 14 years (SD = 1.1). Grade 7 students had an average age of 12.6 years (SD = .6) with a median age of 13 years. Grade 9 students had an average age of 14.5 years (SD = .6) with a median age of 14 years.

Five different measures were used in this study. These included measures of STEM career knowledge, MSE, career activity preferences, career interests, and likelihood to choose to pursue a STEM career. These measures were incorporated into the study based on earlier reviews that found that studies’ examining factors influencing career choice have been criticized for failing to account for the complexity of career choices and career decision-making (Patton and McMahon 2006 ) and for being too static in their view of career development (Hirshi 2011 ).

STEM career knowledge score

A STEM career knowledge (SCK) score was created to capture students’ knowledge about the requirements for high school mathematics and science in STEM careers. Students were presented with 12 STEM careers and asked to indicate whether they believed that the training for each of the careers required having taken mathematics or science in high school. Students could respond “yes” if they believed the career required high school mathematics or science based on their knowledge of the entrance requirements for Canadian colleges and universities. They could respond “no” if they believed that the career did not require high school mathematics or science, or they could choose “uncertain” if they were not sure that high school mathematics and/or science were required for that career. The list included careers students are commonly exposed to (such as veterinarian, pharmacist, and oral hygienist) as well as careers that are likely less familiar to them (such as mechanical engineer, geologist, and land surveyor). The list included mechanical engineer, computer hardware designer, pharmacist, medical technologist, geologist, veterinarian, oil industry engineer, physiotherapist, oral hygienist, nutritionist, land surveyor, and ophthalmologist. The list was provided to students in no particular order.

A score was calculated to capture students’ knowledge based on these responses. “Yes” responses were scored as “1,” “uncertain” responses as “0,” and “no” scored as “− 1.” The responses were then summed to obtain a basic SCK score per student. The SCK score was calculated only for students who had rated at least one third of the careers in the list. The SCK was validated using confirmatory factor analysis (CFA) and reliability analysis.

Mathematics Self-Efficacy Scale

In an attempt to offer a more complete perspective on the process of career decision-making, Hackett and Bertz (Hackett and Betz 1981 ) drew on the work of Bandura ( 1977 ) to introduce the concept of self-efficacy to the career development literature noting its potential to help understand the complexity of career decision-making such as the underrepresentation of women in traditional male-dominated career fields. Self-efficacy referred to the belief that a person had in their own ability to successfully perform a particular behavior based on their perception of their capability and the likelihood of their achieving success in that activity.

The second measure used in this analysis was a MSE scale. Students were asked to describe their experiences in mathematics by rating each of the following statements on a scale ranging from (1) Strongly disagree to (5) Strongly agree: I get good grades in mathematics; I learn quickly in mathematics; I look forward to my mathematics class; I feel tense doing mathematics problems; I feel helpless doing mathematics problems. Negatively phrased items were reverse-coded to maintain consistency in the MSE scale. Students’ responses to these five statements about their experiences in mathematics were then coded into dichotomies to create five separate measures. Dichotomies were created by assigning a value of “1” to those who were most comfortable with Likert scale ratings of 4 or 5 to the statements and assigning a value of 0 to those who were less confident and comfortable with Likert scale ratings of 1 through 3. These five measures were then summed to get a single MSE scale that would reflect higher measures for those who were the most confident and comfortable with mathematics. The MSE scale was validated using confirmatory factor analysis (CFA) and reliability analysis. The resulting MSE score ranged from 0 to 5. The MSE scale was then divided into two subgroups to create an MSE score for further analysis. The MSE score consisted of those with low MSE (scores of 0 through 3) and high MSE (scores of 4 or 5). The goal in using this breakout was to identify students who were the most comfortable and confident in their mathematics experiences.

Career activities and career interests

Social Cognitive Career Theory (SCCT) (Lent et al. 2010 ) has continued to evolve to include person and environmental and socio-demographic variables as well as interest and career choice models. The SCCT argues that people develop interests (actively likes and dislikes) largely on the basis of their beliefs about their self-efficacy and the outcomes their efforts could achieve. Ultimately, people become interested in activities they believe they can perform well. Therefore, people develop goals to pursue academic and career activities that are consistent with their interests as well as with their self-efficacy and outcome expectations (Sheu et al. 2010 ). Thus, career activities and career interests are highly correlated. For these reasons, student ratings of self-perceptions of their career interests, and also their career activities, were included.

The SCCT has been found to support self-efficacy and outcome expectations as significant predictors of interest, that interests partially mediate the relation of self-efficacy and outcome expectations, and that self-efficacy relates to outcome expectations across Holland’s (1997) broad occupational themes as utilized in the current study (Sheu et al. 2010 ). This alignment was felt to provide a rationale for the use of student ratings of interests, activities, and Holland’s broad occupational themes as a comprehensive way of gaining insight into the complexity of career decision-making of junior high students.

Hollands’ Theory of Career Choice and Development (Holland 1973 ) focused on six basic personality types: realistic (practical); investigative (analytical, curious); artistic (expressive, original); social (working/helping others); enterprising (goal oriented); and conventional (ordered). Individuals are not limited to one personality type and many exhibit characteristics on more than one type. Holland ( 1973 ) argued that everyone has career decisions to make at various stages of their lives. As well, he argued that everyone can serve as both a coach and/or a player in those decisions depending on their role, situation, and knowledge. Reflecting on the life stage, the environment, and the knowledge one has of their own particular type of preferred approach to life plus knowledge of the interaction among a variety of factors such as the cultural, social, academic, and family influences on the decisions that each individual makes about their life career. These are not perfect, single, nor static events and depend on self and other perceptions of a wide range of factors. However, at a point in time, they represent what each person conceptualizes as a satisfying career for them. Holland ( 1973 ) argued that his theory of careers was really intended to help practitioners, researchers, and students in education and social science to address a fuller understanding of vocational choice and to be helpful in professional counseling. Miller ( 1998 ) stated that Holland’s theory can be used to help individuals explore career choices. More recently, Olitsky ( 2014 ) used Holland’s theory of career and educational choice when researching the earnings of STEM majors, indicating that the underlying theory is still relevant. Since career interests and career activities are highly correlated, they were measured separately.

The third measure used in this analysis was a ranking of the preferred career activities using Holland’s Theory of Career Choice and Development (1973). Students were asked to rank six different career activities from (1) Most favorite to (6) Least favorite. Each of the career activities was then analyzed based on the percentage of students who rated it in their top 2 favorites. The career activities studied in this research included the following: (1) artistic, unusual, and creative activities; (2) working on practical, productive, and concrete activities; (3) taking responsibility, providing leadership, and convincing others; (4) things being organized into routines and having an order; (5) learning by reading, study, analysis, or investigation; (6) helping others and being concerned for the welfare of others.

The fourth measure used in this analysis was a ranking of career interests also based on Holland’s Theory of Career Choice and Development (Holland 1973 ). Students were asked to rank six different career interests from (1) Most favorite to (6) Least favorite. Each of the career interests was then analyzed based on the percentage of students who rated it in their top 2 favorites. These interests were (1) working with people; (2) creative skills and expression; (3) technical and scientific skills; (4) manual and mechanical skills; (5) leading, persuading, and directing others; and (6) routines and adhering to standards of performance.

Likelihood to pursue a STEM career

The final measure used in this analysis was the likelihood that students would consider choosing a STEM career in their future. Students were asked how likely they would be to choose a career that is science-related (including science, engineering, health, or technology). Likelihood was measured using the following Likert scale: (1) Very unlikely, (2) Somewhat unlikely, (3) Somewhat likely, and (4) Very likely. This scale was recoded into a dichotomous variable for use in bivariate logistic regression: Students who were somewhat likely or very likely to choose a STEM career were coded as “1,” and those who were somewhat unlikely or very unlikely to choose a STEM career were coded as “0.”

Data analysis

Data were analyzed using the SPSS software (IBM Corp 2013 ). Descriptive statistics were used to provide an overall analysis of the data. Various statistical tests were selected based on the level of data measurement and data distributions (McDaniel et al. 2014 ; Hair Jr. et al. 2010 ). t tests were used to explore differences in average ratings between groups. Chi-square was used to analyze associations between nominal and ordinal variables. Analysis of variance (ANOVA) was used to evaluate significant differences between average ratings and measures involving categorical variables with more than two response levels (McDaniel et al. 2014 ). Logistic regression was used to explore research questions involving interval and ratio-scaled variables (Hair Jr. et al. 2010 ). Brown-Forsyth exact tests were used with ANOVA to compensate for violations of homogeneity of variance (IBM Corp 2013 ). Bonferroni post hoc tests were used to detect significant differences between groups for significant ANOVA results (IBM Corp 2013 ; Hair Jr. et al. 2010 ). Data were weighted to reflect the population of students by grade level and province across Atlantic Canada.

Bivariate logistic regression was conducted to explore the relative contribution of the following factors on the likelihood that students would choose a STEM career: SCK score, MSE score, grade level, career interests, and career activities. Grade level, career interest, and career activities were coded as dichotomies for the regression analysis as follows: grade level (grade 9 = 1, grade 7 = 0), career interests (rated in top 2 favorites = 1, not rated in top 2 favorites = 0), career activities (rated in top 2 favorites = 1, not rated in top 2 favorites = 0).

Three regressions were created to explore the research questions. The first analysis regressed grade level, SCK score, and MSE score against the likelihood to pursue a STEM career. Two more regressions were conducted: one to regress career activities and a second to regress career interests against the likelihood to pursue a STEM career as a dependent variable. Measures for career activity and career interests showed a high level of multicollinearity between the two sets of variables. Separating these predictors into two different regressions eliminated problems with multicollinearity.

We first describe the results for each of the measures used in this study and then answer our research questions (RQ1–5).

Student knowledge of mathematics and science requirements for STEM careers

We assessed student’s knowledge of high school requirements for STEM careers, by asking students to indicate whether a career required mathematics and/or science (Table 1 ). Mechanical engineer was noted by 71.4% of students as having a high school mathematics/science requirement. Two careers (land surveyor and ophthalmologist) were noted by less than half of the students as requiring high school mathematics or science. Five careers were classified as requiring mathematics and science by 65.6 to 68.2% (veterinarian, geologist, medical technologist, pharmacist, computer hardware engineer). Four of the careers were listed as requiring mathematics and science by 51.8 to 58.6% of the students (nutritionist, oral hygienist, physiotherapist, oil industry engineer). What is notable in the students’ responses are that most students seemed confident of their career classification in that they answered “yes” or “no” and not the option of “uncertain,” indicating that they were confident in their choice. The percentage of students saying that they were uncertain if a career required mathematics or science for post-secondary study was low and ranged from 12.5 to 32.6% across all of the careers with half of the uncertain responses ranging from 12.5 to 13% of students. Table 1 shows the results of high school mathematics/science requirements for STEM careers.

Students’ responses were summed to obtain an overall SCK score. A factor analysis of the career ratings was used to ensure it was unidimensional, and reliability of the score was measured using Cronbach’s alpha. The confirmatory factor analysis was statistically significant (KMO = .961, p < .01). Cronbach’s alpha was .95 which meets the criterion for reliability.

The SCK score ranged from − 12 to + 12, with an average score of 4.6 (SD = 7.6; Fig. 1 ). The average SCK score was low, indicating a lack of familiarity with the mathematics and/or science requirements of STEM careers. Approximately 8% of students did not correctly classify any of the careers as having a high school mathematics and/or science requirement. Only 36.4% of students had high SCK scores having correctly classified 10 to 12 careers. The top quartile of students scored 11 or better while the bottom quartile scored 0 or less than 0 out of the 12-point score. A summary of the SCK score is in Fig. 1 .

STEM career knowledge (SCK) score

Overall, these results suggest that STEM career knowledge is limited among middle school students. Results also reveal that students seem to be unaware of their limited knowledge regarding STEM career preparation.

Students’ mathematics self-efficacy

In order to determine whether MSE was correlated with students STEM career knowledge (RQ2) and/or between MSE and career interests and/or preferred career activities (RQ3), we first determined the MSE scale for the cohort. The MSE scale ranged from 0 (No self-efficacy) to 5 (High self-efficacy). The distribution of the Math Self-Efficacy Scale is shown in Fig. 2 .

Mathematics Self-Efficacy (MSE) Scale

A confirmatory factor analysis of the measures in the MSE scale indicated that it was unidimensional and reliable. The factor analysis was statistically significant (KMO = .698, p < .01). Cronbach’s alpha was .72 which is acceptable for a scale analysis. These results suggest that over half of the students had a relatively high MSE and about one third of students had low MSE.

Career activities and interests

In order to assess students’ preferred career activities and their career interests, students were asked to select their favorites. Students were presented with a list of six career activities and asked to indicate which activities were in their top 2 favorites. These measures were recoded into dichotomies for further analysis. There was a very even spread of students rating career activities in their top 2 favorites, ranging from 32.1 to 45.9%. Most of the activities were listed in their top 2 favorites by about one third of the students. The results revealed that artistic, unusual, and creative activities were most commonly listed in the top 2 favorite career activities. The career activity with the lowest rating was helping others and being concerned for their welfare. The results are summarized in Table 2 .

Students were also presented with a list of six career interests and asked to indicate which career interests were in their top 2 favorites. These measures were recoded into dichotomies for further analysis. The percentage of students rating career interests as their top 2 favorites ranged from 21.9% for routines and adhering to standards to working with people at 49.8%. The results are summarized in Table 3 .

Likelihood of choosing a STEM career

Next, we assessed whether students were interested in pursuing a STEM career. Nearly 70% percent of students surveyed revealed that they were either somewhat likely or very likely to pursue a STEM career. On a scale of (1) Very unlikely to (4) Very likely, an average rating of 2.9/4 indicated that students were somewhat likely to pursue a STEM career. The results appear in Table 4 .

The association of grade level and STEM career knowledge

The first research question (RQ1) explored the correlation of grade level and STEM career knowledge. There was a statistically significant difference in the average SCK score by grade, with grade 9 students scoring higher than grade 7 students (5.7 vs 3.3, t = − 5.69, df = 1209.7, p < .01). While it is good to see that students appear to acquire more knowledge of STEM career requirements in middle school grades, it is concerning that students in grade 9 still had a low average SCK score since this is the year in which students begin to choose subject classes in Atlantic Canada. This indicates that more work is needed to ensure students have the correct information about STEM career requirements in time for them to make informed decisions about high school course selection.

Correlation between mathematics self-efficacy and the STEM career knowledge

The second research question (RQ2) focused on whether there is a correlation between students with higher MSE and knowledge of STEM career requirements. An analysis of variance revealed that students with high self-efficacy (MSE scale = 4 and 5) had significantly higher SCK scores than students who did not score as highly in the MSE scale (BF = 8.7, df = 5, p < .01). Students with high MSE had a SCK score of 6.6 out of 12, while students with lower MSE scores had average SCK scores ranging from 2.8 to 4.8. The results are shown in Table 5 .

These results for RQ2 show that students who report more confidence and comfort in mathematics tend to be more knowledgeable about mathematics/science requirements for STEM careers. This is a correlation only and cannot be interpreted as a causal relationship since survey data cannot be used to measure causality.

Correlation between mathematics self-efficacy and students’ preferred career activities and their career interests

Our third research question (RQ3) explored whether there was a correlation between MSE and students’ career interests and preferred career activity. There were statistically significant differences by students’ preferred career activities for the MSE scale. MSE scale totals were sorted into two groups to create an MSE score for further analysis. Those with low MSE scale totals (0 through 3) were assigned an MSE score of 0, and those with high MSE scale totals (4 and 5) were assigned an MSE score of 1. A chi-square analysis revealed that only one career activity differed significantly based on students’ MSE scores. Reading, study, analysis, and investigation was listed in the top 2 favorites for career activities by 36.5% of students who had high MSE scores (between 4 and 5) when compared to 28.4% of students with low MSE (0 through 3) ( χ 2 = 7.979, df = 1, p < .01). The remaining career activities did not differ significantly based on students’ MSE. The results are summarized in Table 6 .

These results show that most of the preferred career activities had no correlation at all with MSE scores. However, reading, study, analysis, and investigation are the hallmarks of a mathematics-, science-, or technology-based activity. Therefore, it is reasonable that students who are confident and comfortable with mathematics would also enjoy reading, study, analysis, and investigation.

In order to explore whether there is a correlation between MSE and student’s career interests, a chi-square analysis was conducted. The chi-square analysis revealed that only one career interest differed significantly based on students’ MSE score. This career interest was technical and scientific skills. This career interest was listed in the top 2 favorites for career activities by 43.8% of students with high MSE score (between 4 and 5) compared to 36.0% of students with low MSE score (0 through 3) ( χ 2 = 6.558, df = 1, p = .01). The remaining career interests did not differ significantly based on students’ MSE scores. As with the results for career activities, these results show that most of the career interests were not significantly correlated with MSE and all of the career interests were rated in the top 2 favorites by less than half of the students. It is reasonable that students who are confident and comfortable with mathematics would also be interested in careers involving technical and scientific skills. The results are summarized in Table 7 .

The correlation between grade level and students’ career interests and preferred career activities

The fourth research question (RQ4) addressed whether grade level was associated with student preferences for career interests and activities. There were statistically significant differences by grade regarding some of the career interests, thereby satisfying the first part of the fourth research question. More grade 7 than grade 9 students listed manual and mechanical skills in their top 2 favorites (36.5 vs 29.4%, χ 2 = 6.84, df = 1, p < .01), as well as creative skills and expression (45.4 vs 37.0%, χ 2 = 8.73, df = 1, p < .01). More grade 9 than grade 7 students ranked “working with people” in their top 2 favorites (52.8 vs 46.8%, χ 2 = 4.21, df = 1, p < .05). These results are summarized in Table 8 .

More grade 7 than grade 9 students listed practical, productive, and concrete activities in their top 2 favorites (42.1 vs 34.7%; χ 2 = 6.9, df = 1, p < .01). More grade 9 than grade 7 students rated helping others and being concerned for their welfare in their top 2 favorite career activities (34.9 vs 28.6%; χ 2 = 5.4, df = 1, p < .05) as well as having things organized into routines and having order (39.4 vs 29.4%; χ 2 = 13.2, df = 1, p < .01). There were no statistically significant differences by grade level for the other career activities studied.

This trend is similar to that emerging in the analysis of career interests. In general, students in the higher grade focused more on activities involving helping others and being less attracted to careers that involved practical applications or routines. The results are summarized in Table 9 .

The correlations between students’ STEM career knowledge, mathematics self-efficacy, and grade level on their likelihood to choose a STEM career

The fifth research question (RQ5) focused on how several aspects might relate to students’ likelihood of choosing a STEM career. These areas included grade level, MSE, knowledge of STEM careers, and preferences for various career interests and activities.

First, a logistic regression was conducted to determine whether or not grade level, STEM knowledge, and MSE score were associated with students’ likelihood to pursue a STEM career. The hypothesized regression model was likelihood of choosing a STEM career (ODDS) = f (grade level, STEM knowledge score, MSE score). A test of the full regression model against an intercept-only model was statistically significant ( χ 2 = 76.85, df = 3, p < .01). The regression was strong with a McFadden’s R 2 = .85.

The regression analysis correctly classified 70.6% of all cases and 95.3% of those who were likely to choose a STEM career. The regression revealed that students with stronger SCK scores were marginally more likely to pursue a STEM career than were students with weaker SCK scores (odds ratio = 1.04, probability = .51). However, students with high MSE scores were 1.3 times more likely to pursue a STEM career than were those who had lower MSE scores (probability = .56). Grade level was not a statistically significant predictor of the likelihood of pursuing a STEM career.

These results showed that students’ knowledge of STEM careers and their self-efficacy in mathematics were statistically significant factors in the likelihood that they would pursue a STEM career, while STEM career knowledge was a modest contributor. Also, students in grade 9 were not more likely to pursue a STEM career than were students in Grade 7. However, research has shown that occupational intentions change dramatically between 9th and 11th grades and the relationship between STEM intention and motivation is very time-sensitive (Mangu et al. 2015 , p.55). The results are summarized in Table 10 .

These regression results reveal that individual student characteristics, MSE, and SCK are better predictors of the likelihood to pursue STEM careers than student grade level. Individual strengths and weaknesses, as well as students’ knowledge and competency, are better indicators of future career paths than grade level.

The correlation between students’ career interests and their likelihood to pursue a STEM career

A second logistic regression was conducted to explore whether or not students’ preferred career interests was correlated with their likelihood to pursue a STEM career. Six career interests were explored in the analysis. The hypothesized regression model was likelihood of choosing a STEM career (ODDS) = f (manual and mechanical skills; technical and scientific skills; creative skills and expression; working with people; leading, persuading, and directing others; routines and adhering to standards). A test of the full regression model against an intercept-only model was statistically significant ( χ 2 = 119.94, df = 6, p < .01). The regression was reasonably strong with a McFadden’s R 2 = .73. The regression analysis correctly classified 72% of all cases and 96.6% of those who were likely to choose a STEM career.

The regression revealed that students who rated technical and scientific skills in their top 2 favorite career interests were 5.4 times more likely to pursue a STEM career (probability = .84). Students who rated working with people in their top 2 favorites were 1.5 times more likely to pursue a STEM career (probability = .61). Students who rated creative and expressive skills in their top 2 favorite career interests were less likely to pursue a STEM career than those who rated creative and expressive skills highly. Their odds of pursuing a STEM career were only .70 of those who did not rate creativity and expressiveness among their favorite career interests. Their probability of pursuing a STEM career was .41. The remaining career interests were not statistically significant predictors of the likelihood of pursuing a STEM career (manual or mechanical skills; leading, persuading, or directing others; routines and adhering to standards). These results provide evidence for the fifth research question in that three out of the six career interests measured did have a statistically significant correlation with the likelihood that a student would consider pursuing a STEM career. The results are summarized in Table 11 .

These results indicate that student preference for technical and scientific skills and careers involving working with people enhance the likelihood of pursuing a STEM career, while students who prefer careers involving creative skills and expression are less likely to do so. While a focus group could better explore the students’ preferences for creativity and creative careers, this level of detail is not possible in large sample survey-based research and is outside of the scope of this study. Other career interests that focus on mechanical, manual, or routine activities, or those involving leadership, do not predict the likelihood of students pursuing a STEM career and are not significantly correlated with STEM career choice.

A third logistic regression analysis was conducted to determine whether students’ career activity preferences were correlated whether or not they were likely to pursue a STEM career. Six career activities were explored in the analysis. The hypothesized regression model was likelihood of choosing a STEM career (ODDS) = f (practical, productive, concrete activities; reading, study, analysis, and investigation; artistic, unusual, and creative activities; taking responsibility, providing leadership, and convincing; and helping others and being concerned for their welfare). A test of the full regression model against an intercept-only model was statistically significant ( χ 2 = 32.883, df = 6, p < .01). The regression was reasonably strong with a McFadden’s R 2 = .78. The regression analysis correctly classified 72% of all cases, and 100% of those who were likely to choose a STEM career.

The regression revealed that students who preferred career activities involving reading, study, analysis, and investigation were 1.8 times more likely to pursue a STEM career (probability = .65) than those who did not prefer such activities. Students’ rating career activities involving routines and having an order were 1.5 times more likely (probability = .60) to pursue a STEM career than those who did not prefer such activities, while students with preferences for practical, productive, and concrete career activities were 1.5 times more likely to pursue a STEM career (probability = .60) compared to those who did not prefer such activities. The remaining career activities were not statistically significant predictors of the likelihood to pursue a STEM career (artistic, unusual, and creative activities; taking responsibility, providing leadership, and convincing others; helping others and being concerned for their welfare). These results revealed three out of the six career activities measured did have a statistically significant correlation with the likelihood that a student would consider pursuing a STEM career. The results are summarized in Table 12 .

These results stand in contrast to those for students’ career interests and the likelihood of pursuing a STEM career. Unlike the career interest analysis, students seeking routine career activities are more likely to pursue a STEM career. Also, students who ranked career interests involving helping others were more likely to pursue STEM careers, but this analysis showed that student preference for career activities involving helping others and being concerned for their welfare was not a statistically significant indicator of their likelihood to pursue a STEM career. Further, these results differ somewhat in terms of students’ preferences for practical activities. While career interests involving manual or mechanical skills were not statistical indicators of the likelihood of pursuing a STEM career, career activities involving practical, productive, and concrete activities were statistically significant. The career activity involving reading, study, analysis, and investigation was also statistically linked to students’ likelihood to pursue a STEM career, which seems reasonable given that such activities are at the heart of many STEM careers.

Youth vary widely in their career knowledge, interest, and intentions. Factors investigated in the present study examined STEM career knowledge, MSE, career activities, career interests, and the likelihood of students to pursue a STEM career.

Knowledge and self-efficacy

Results of the present study align with recent findings by Compeau et al. ( 2016 ), Nugent et al. ( 2015 ), and Zhang and Barnett ( 2015 ) show that self-efficacy along with knowledge of STEM careers are significant factors in whether or not adolescents pursue STEM careers. Findings also indicated that career knowledge is limited among middle school students and students seem to be unaware of their limited knowledge regarding STEM preparation. While approximately 70% of students reported that mathematics was an important requirement for a career in mechanical engineering, computer hardware design, and pharmacy, 50% or less were aware that it was also important in careers for ophthalmology, land surveyor, nutrition, and oral hygienist.

The issue of self-efficacy takes on particular significance as students progress through high school. Previous research by Murphy and Beggs ( 2005 ), Heilbronner ( 2009 ), and Mangu et al. ( 2015 ) have noted how young women have a lower self-efficacy in STEM during high school years. Previous research has also shown that interest in STEM and motivation to pursue STEM activities tends to wane over time for all high school students. The results of the current study agree with earlier findings that lower levels of MSE exist; we found approximately 34% of participants had low MSE scale totals. These findings raise concerns about the combined effects of students’ low MSE and their declining interest in STEM from early through to later grades and on the numbers of graduating high school students who will be inclined to choose a STEM career.

Results of the current study demonstrated that students in grades 7 and 9 had a broad range of favorite career activities with the majority (approximately 46%) stating that their strongest preference was for artistic and creative types of activities. Also, all of the possible activities were selected by at least one third of the group. Interestingly, approximately 50% of participants selected their career interest as being “working with people,” but relative to career activities, only one third of participants selected “helping people.” However, this is not surprising given that one can have an interest but may not want to have a career working in that activity. For example, one may be interested in art, but have no interest, or lack sufficient talent, to pursue a career in the field (Holland 1973 ). Also, middle school students may not be able to discriminate between the nuances between career activities and interests in the way that older students and young adults would. Although a focus group study may be able to further elucidate this issue, this is beyond the scope of the current study.

Working with others and participating in creative types of activities are important findings that relate to current issues in education in Canada. A recent study (Ayar and Yalvac 2016 ) found that many STEM careers are team-based, creative, and require technical, scientific, and problem solving skills. However, in Canada, many post-secondary programs continue to focus more on memorizing and replicating science content knowledge. Further study of this possible implication would be worthwhile.

While approximately 70% of participants stated they were likely to choose a STEM career, 30% were less likely to do so. Not all students have the financial means to pursue a career interest. In addition, career interest and motivation are highly time sensitive (Mangu et al. 2015 , p.55). Considering findings from studies such as Ayar and Yalvac ( 2016 ), as well as what we know about the decline in interest in STEM careers as students mature, these results suggest that there is room for increasing awareness, STEM career interest, and providing for better knowledge acquisition in the area of STEM careers. As well, our data suggests that alternative ways of teaching and evaluating STEM courses should be considered. Perhaps a greater emphasis on authentic means of teaching and evaluating STEM content that involves collaboration, problem solving, and application of STEM knowledge might serve to engage learners in more meaningful ways, thereby enabling continued motivation and interest in STEM careers as students progress through secondary and post-secondary education. Does a higher MSE lead students to consider pursuing STEM careers and lead them to becoming more informed about the career requirements or do students who have higher knowledge of STEM careers become more competent in mathematics? Are these factors simply correlational and reflect students who are high in both measures or low in both measures? While these questions cannot be answered in this research, it is interesting to note that MSE may play a role, or be a leading indicator, for STEM career knowledge.

Influence of mathematics self-efficacy on career knowledge, interest and activities

Our results indicate that while there is a relationship between career knowledge and MSE, we did not find a relationship among career interests or activities with MSE. The assumption that having a positive sense of mathematics skill would correlate with STEM career interests and activities was not supported. Follow-up research involving interviews with participants about their understanding of career interests, activities, and MSE, would provide more an in-depth understanding. Based on findings by Simpkins et al. ( 2006 ) it was expected that there would be a relationship among interests, activities and MSE as their findings indicated that in junior high beliefs about competency and interests begin to solidify. Further research may help to uncover reasons for not seeing such a correlation in this analysis.

Influence of grade level on STEM career knowledge, interest, and activities

Results indicate that there were significant differences between grade 7 and grade 9 students in the present study relative to STEM career knowledge. Overall, students in grade 9 were more knowledgeable than grade 7 students about STEM careers. The differences formed an interesting and consistent pattern that more grade 7 students expressed interest in manual and mechanical skills than grade 9 students who tended to have more interest in working with people. Further, more grade 7 students expressed interests in practical and concrete types of activities while more grade 9 students expressed interests in helping people and being concerned for their welfare. Reasons for this shift are not clearly understood. As noted by Lent ( 2005 ), career interest, choice, and personal goals form a complex chain involving performance, self-efficacy, and outcome expectations. As well, socio-cultural factors also need to be considered along with opportunity for exposure (Fouad and Smith 1996 ; Kuncel et al. 2005 ; Lent et al. 1994 ). As with the relationship among self-efficacy, knowledge, interests, and activities, in-depth research involving student interviews may result in greater understanding of the reasons for these shifts and their impact on later careers.

Factors influencing positive statements involving the likelihood of choosing a STEM career

Regression analyses revealed that participants with stronger STEM career knowledge were slightly more likely to pursue a STEM career and that students with higher MSE scores were also slightly more likely to choose a STEM career. Also, grade level was not a differentiating factor, which was anticipated given the small distance between the experiences of grade 7 versus grade 9 students. As noted in many previous studies (Lent et al. 1994 ; Kuncel et al. 2005 ), knowledge of STEM careers and self-efficacy in mathematics are statistically significant factors in the likelihood that participants will pursue STEM careers.

Interest in technical and scientific skills is a strong predictor of the likelihood of pursuing a STEM career with those who indicated a preference for technical and scientific skills being 5.4 times more likely to indicate the likelihood of choosing a STEM career compared to those who rated working with people as their stronger interest. Indeed, preferences for practical, productive, and concrete activities also indicated a stronger likelihood of pursuing STEM careers than those who do not prefer such activities. Implications of these findings point to improving methods for providing information on the skills and nature of the work in STEM careers particularly in fields such as engineering and technology (which have an important focus on team work, problem solving, and creativity) as well as on technical and scientific skills.

Overall, results of the present study show that career knowledge is limited among middle school students and that they have a declining interest in STEM and have low MSE scores. Students are interested in careers that involve a wide variety of activities but do not appear to relate these activities to STEM careers. Our results point to the importance of finding and expanding on ways to increase authentic learning opportunities in secondary school in Atlantic Canada such that students are better able to participate in collaboration, problem solving, and the application of scientific knowledge in their classes. Such learning opportunities would ensure that students have access to more information on the actual nature of work in the STEM field and what is required to pursue these careers. This strategy would also serve as a motivator to those who are not aware that STEM careers involve people skills, creativity, and problem solving.

Abbreviations

Confirmatory factor analysis

Mathematics self-efficacy

Science, technology, engineering, and mathematics

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Acknowledgements

Funding was provided to author by a grant from the Natural Sciences and Engineering Research Council of Canada. We are grateful to the individuals who helped our team obtain parental consent forms and parents for providing consent for the children to participate in this study.

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Blotnicky, K.A., Franz-Odendaal, T., French, F. et al. A study of the correlation between STEM career knowledge, mathematics self-efficacy, career interests, and career activities on the likelihood of pursuing a STEM career among middle school students. IJ STEM Ed 5 , 22 (2018). https://doi.org/10.1186/s40594-018-0118-3

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23+ Quantitative Research Topics For STEM Students In The Philippines

Post author By Ankit
February 6, 2024

“Did you know only 28% of college graduates in the Philippines get degrees in STEM fields? Finding good research topics is vital to getting more Filipino students curious about quantitative studies.

With limited research money and resources, it can be hard for STEM students to find quantitative projects that are possible, new, and impactful. Often, researchers end up feeling apart from local issues and communities.

This blog post offers a unique collection of quantitative research topics for STEM students in the Philippines. Thus, drawing from current events, social issues, and the country’s needs, these project ideas will feel relevant and help students do research that creates positive change.

Philippines students can find inspiration for quantitative studies that make a difference at home through many examples across science, technology, engineering, and math.

Read Our Blog: 120+ Best Quantitative Research Topics for Nursing Students (2024 Edition)

Table of Contents

30 Great Quantitative Research Topics for STEM Students in The Philippines

Here are the top quantitative research topics for STEM students in the Philippines in 2024

1. Impact of Climate Change on Farming

Analyze how changing weather affects the growth of crops like rice and corn in different parts of the Philippines. Use numbers to find ways and suggest ways farmers can adapt.

2. Using Drones to Watch Nature

See how well drones with special sensors can watch over forests and coasts in the Philippines. Look at the data they gather to figure out how to save these places.

3. Making Solar Panels Work Better

Experiment with various ways to make more power with solar panels in sunny, humid places like the Philippines. Utilize math to guess how well they’ll work.

4. Checking How Pollution Hurts Coral Reefs

Count how much damage pollution does to coral reefs in the Philippines. Try to predict how bad it’ll get if we don’t stop polluting.

5. Watching Traffic to Fix Roads

Look at how cars move in big cities like Manila. Use math to figure out how to make traffic flow better and help people get around faster.

6. at Air and Sick People

Measure how clean the air is in various parts of the Philippines and see if it affects how many people get sick. Find out which areas need help to stay healthy.

7. Guessing When Earthquakes Might Happen

Look at data from sensors all over the Philippines to see if we can tell when earthquakes might come. Try to guess where they’ll occur next.

8. Making Water Pipes Better

Use math tricks to design cheap pipes that bring clean water to small towns in the Philippines. Think about things like hills and how many people need water.

9. Checking If Planting Trees Helps

Measure if planting trees helps stop the shore from washing away during storms. Use photos from far away and math to see if it’s working.

10. Teaching Computers to Find Sickness

Teach computers to look at pictures and records from hospitals to see if people are sick. Check if they’re good at spotting problems in the Philippines.

11. Finding Better Bags That Break Down

Test different materials like banana leaves to see which ones can be made into bags that don’t hurt the environment. Compare them to regular plastic bags.

12. Making Gardens in the City

See if we can grow vegetables in tall buildings in big cities like Manila. Use numbers to figure out if it’s a good idea.

13. Checking If Bugs Spread Easily in Crowded Places

Use computers to see if diseases spread fast in busy places in the Philippines. Look at how people move around to stop diseases from spreading.

14. Storing Energy for Islands Without Power

Think about ways to save power for small islands without electricity. Try out different ways to save energy and see which one works best.

15. Seeing How Much Storms Hurt Farms

Calculate how much damage storms do to farms in the Philippines. Use numbers to see how much money farmers lose.

16. Testing Ways to Stop Dirt from Washing Away

Try out different ways to stop dirt from being washed away when it rains. Use math to see which way works best on hills in the Philippines.

17. Checking How Healthy Local Food Is

Look at the vitamins and minerals in local foods like sweet potatoes and moringa leaves. See if eating them is good for people in the Philippines.

18. Making Cheap Water Cleaners

Build simple machines that clean dirty water in small towns. Notice if they work better than expensive ones.

19. Seeing How Hot Cities Get

Use satellites to see how hot cities like Manila get compared to places with more trees. Think about how this affects people.

20. Thinking About Trash in Cities

Look at how much trash cities in the Philippines make and find ways to deal with it. Consider what people can do to make less trash.

21. Checking If We Can Use Hot Rocks for Power

Look at rocks under the ground to see if we can get power from them. Consider whether it is beneficial for the environment.

22. Counting Animals in the Forest

Use cameras to count how many animals are in forests in the Philippines. Notice which places need the most help to keep animals safe.

23. Making Fishing Fair

Look at how many fish are caught in the Philippines and see if it’s fair. Think about ways to make sure there will always be enough fish to catch.

24. Making Power Lines Smarter

Design power lines that can change how much power they use. Try to make sure power goes where it’s needed most.

25. Looking at Dirty Water

Find out if chopping down trees and building things by rivers makes the water dirty. Think about what this means for people and animals.

26. Thinking About Big Waves

Use computers to see if big waves could hit the Philippines and what might happen. Think about how to keep people safe.

27. Seeing If Parks Help Cities

Ask people if they like having parks in their city and see what animals live there. Think about if parks make cities better.

28. Making Houses That Don’t Break in Storms

Make houses that don’t fall when there are big storms. Try to make them cheap so more people can have them.

29. Stopping Food from Going Bad

Look at how food gets from farms to people’s houses and see if we can stop it from going bad. Think about how to make sure people have enough to eat.

30. Seeing How Hot Cities Get

Put machines around cities to see how hot they get. Consider how this affects people and what we can do to help.

These topics will help you to make a good project that assists you in getting better scores.

Importance Of Quantitative Research For STEM Students

Read why quantitative research matters to Filipino students.

Helps us understand problems more clearly by revealing trends, patterns, and connections in the data
Provides an accurate picture by removing personal biases and opinions
Allows quantitative comparison of results if studies use the same methods
Enables testing hypotheses and theories through experiments that can prove/disprove predictions
Allows replication and verification as other researchers can redo experiments and study methods
Numbers give a more precise, factual understanding compared to qualitative data.
Removes subjectivity through quantitative data rather than opinions
A key part of the scientific process is that data helps confirm or reject proposed explanations.
Overall, collecting and analyzing quantitative data is crucial for gaining insights, testing ideas, ensuring consistency, and reducing bias

It’s time to see what challenges students face with their quantitative research.

Challenge Philippines Students Face With Their Quantitative Research

Here are the common challenges that students face with their quantitative research topics:

Lack of resources and funding

Doing quantitative research needs access to equipment, software , datasets etc, which can be costly. Many students lack funding and access to these resources.

Lack of background in mathematics and statistics

Quantitative research relies heavily on math and statistical skills. However, many students haven’t developed strong enough skills in these areas yet.

Difficulty accessing scholarly databases

Students need access to academic journals and databases for literature reviews. However, these can be costly for people to access.

Language barriers

Many of the academic literature is in English. This can make reading and learning complex statistical concepts more difficult.

Lack of mentorship

Having an experienced mentor to provide guidance is invaluable. However, not all students have access to mentorship in quantitative research.

Managing large datasets

Collecting, cleaning and analyzing large datasets requires advanced technical skills. Students may struggle without proper guidance.

Presenting results clearly

Learning how to visualize and communicate statistical findings effectively is an important skill that takes practice.

Ethical challenges

Ensuring quantitative studies are designed ethically can be difficult for novice researchers.

Writing scientifically

Adopting the formal, precise writing style required in quantitative research is challenging initially.

Maintaining motivation

Quantitative research is complex and time-consuming. Students may lose motivation without a strong support network.

While quantitative research presents many challenges, Philippines STEM students can overcome these through access to proper resources and support. With hard work, mentorship and collaborative opportunities, students can build essential skills and contribute to the quantitative research landscape.

Tips For Conducting Quantitative Research In The Philippines

When conducting research in a new cultural context like the Philippines, it is vital to take time to understand local norms and build trust. Approaching research openly and collaboratively will lead to more meaningful insights.

1. Get Required Approvals

Be sure to get any necessary ethics reviews or approvals from local governing boards before conducting the analysis. It is wise to follow proper protocols and permissions.

2. Hire Local Assistants

Hire local research helpers to help navigate logistics, translation, and cultural sensitivities. This provides jobs and insider insights.

3. Use Multiple Research Methods

Triangulate findings using interviews, focus groups, surveys, participant observation, etc. Multiple methods provide more potent and well-rounded results.

4. Verify Information

Politely verify information collected from interviews before publication. Follow up to ensure accurate representation and context.

5. Share Results

Report back to participants and communities on research findings and next steps. This shows respect and accountability for their contributions.

6. Acknowledge Limitations

Openly acknowledge the limitations of perspective and methods as an outside researcher. Remain humble and keep improving approaches.

Keep in mind, when entering a new community to conduct research, taking an open, patient, and collaborative approach leads to more ethical and meaningful results. Thus, making the effort to understand and work within cultural norms demonstrates respect.

STEM students in the Philippines have many possible research topics using numbers. They could look at renewable energy, sustainability, pollution, environment, disease prevention, farming improvements, preparing for natural disasters, building projects, transportation, and technology access.

By carefully analyzing statistics and creating mathematical models, young Filipino researchers can provide key ideas to guide future policies and programs. Quantitative research allows real observations and suggestions based on evidence to make the country better now and later.

Number-based methods help young researchers in the Philippines give tangible recommendations to improve their communities.

How can I limit my choices and pick the right research topic?

Think about what you enjoy and what you’re skilled at. Consider if your topic is meaningful and if you have the resources to study it. Get advice from teachers or friends to help you decide.

What are some common problems in doing math research in science, technology, engineering, and math?

Problems might include: 1. Finding data. 2. Make sure your measurements are correct. 3. Following rules about ethics. 4. Handling big sets of data.

How can I make sure my research is done well?

Plan your study carefully, use the correct methods and tools, write down everything you do, and think about the strengths and weaknesses of your work.

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60+ Innovative Qualitative Research Topics for STEM Students

Explore compelling qualitative research topics for STEM students, delving into personal narratives, ethical dilemmas, and educational impacts across science, technology, engineering, and mathematics.

STEM disciplines traditionally focus on equations, experiments, and empirical evidence. Yet, the human dimension of these fields profoundly shapes their evolution and application.

Qualitative research thus becomes pivotal in unraveling the motivations behind scientific breakthroughs, examining personal stories, perspectives, and educational influences that mold STEM practices.

From uncovering the inspirations driving young scientists to navigating ethical quandaries in technological advancements, and exploring how science education impacts diverse communities, qualitative research offers a rich tapestry of insights into STEM’s human aspect.

This guide curates intriguing qualitative research topics that illuminate the personal narratives within science, technology, engineering, and mathematics. Embark on a journey to uncover the human stories behind STEM!

Table of Contents

Qualitative Research Topics for STEM Students PDF

Qualitative research topics for stem students.

Check out qualitative research topics for STEM students:-

City Birds: How Urban Growth Affects Wildlife

Project Idea: Explore how cities impact bird populations. Use birdwatching and community surveys to track species diversity in urban areas versus natural habitats.

Innovative Approach: Create a citizen science project where local residents and schools monitor bird populations in their neighborhoods using a mobile app.

Ethics in Animal Research: Balancing Science and Welfare

Project Idea: Investigate ethical dilemmas in animal research. Interview scientists and activists to understand perspectives on humane treatment and alternative methods.

Innovative Approach: Organize a debate or public forum where students role-play different stakeholders (scientists, ethicists, animal rights advocates) to discuss and propose solutions.

Healing Plants: Traditional Knowledge Meets Modern Science

Project Idea: Document traditional uses of medicinal plants. Interview healers and botanists to explore their knowledge and conservation efforts.

Innovative Approach: Create an interactive digital archive showcasing interviews, plant specimens , and stories from local communities about medicinal plants.

Community Conservation: Engaging Locals in Wildlife Protection

Project Idea: Study community perceptions of conservation efforts. Interview residents and conservationists to assess awareness and participation in local wildlife conservation projects.

Innovative Approach: Host a virtual reality (VR) experience where participants explore a simulated wildlife reserve, learning about conservation challenges and solutions.

Save the Bees: Investigating Pollinator Decline

Project Idea: Research factors contributing to pollinator decline. Analyze pesticide use and habitat loss impacts on bee populations through fieldwork and stakeholder interviews.

Innovative Approach: Develop an educational board game where players must balance farming practices with bee conservation efforts, raising awareness through interactive gameplay.

Chemical Safety at Home: What’s in Your Products?

Project Idea: Survey public attitudes towards chemical safety in household products. Analyze labeling and consumer preferences for eco-friendly alternatives.

Innovative Approach: Create an Instagram campaign where participants share photos and reviews of safe household products, promoting awareness and informed consumer choices.

Green Innovations: Sustainable Solutions for Small Businesses

Project Idea: Interview small business owners adopting green chemistry practices. Analyze case studies of successful eco-friendly startups and their impact on sustainability.

Innovative Approach: Organize a “Green Shark Tank” event where students pitch eco-friendly product ideas to local entrepreneurs and sustainability experts for feedback and support.

Waste Not, Want Not: Recycling Chemicals for a Cleaner Future

Project Idea: Investigate chemical waste recycling practices. Interview engineers and policymakers to understand challenges and innovations in recycling technologies.

Innovative Approach: Design a virtual reality experience where users explore a recycling plant, learning about chemical waste processes and environmental benefits.

Fun with Chemistry: Enhancing Education through Interactive Learning

Project Idea: Evaluate educational resources in chemistry. Interview teachers and students to assess the effectiveness of hands-on experiments and digital simulations.

Innovative Approach: Develop a mobile app featuring interactive chemistry tutorials and virtual labs, making learning engaging and accessible outside the classroom.

Ethical Chemistry: Ensuring Safety and Responsibility in Research

Project Idea: Explore ethical considerations in chemical research. Interview researchers to discuss issues like data integrity and public trust in scientific studies.

Innovative Approach: Host a podcast series where scientists share personal stories and ethical dilemmas encountered in their research, encouraging open dialogue and critical thinking.

Space Explorers Unite: Inspiring the Next Generation of Astronauts

Project Idea: Engage the public in space exploration. Host workshops where participants design and build model Mars rovers, learning about planetary exploration challenges.

Innovative Approach: Collaborate with local artists to create a mural depicting humanity’s journey into space, sparking curiosity and wonder in the community.

Power of the Future: Exploring Renewable Energy Solutions

Project Idea: Investigate public perceptions of renewable energy. Interview engineers and policymakers about solar and wind power adoption and community benefits.

Innovative Approach: Organize a renewable energy fair where students showcase DIY solar panel projects and energy-efficient designs, promoting sustainable practices in everyday life.

Quantum Quest: Discovering the World of Quantum Mechanics

Project Idea: Explore applications of quantum mechanics. Interview physicists and tech innovators to understand quantum computing and cryptography advancements.

Innovative Approach: Create an augmented reality (AR) experience where users interact with quantum particles, learning about their unique properties and potential applications.

Particle Pioneers: Ethical Challenges in Accelerating Discoveries

Project Idea: Discuss ethical dilemmas in particle physics. Host a mock UN summit where students negotiate international agreements on particle accelerator safety and collaboration.

Innovative Approach: Organize a live-streamed virtual tour of CERN, featuring interviews with physicists and behind-the-scenes footage of particle research, engaging global audiences in scientific exploration.

Physics in Everyday Life: From Curiosity to Practical Applications

Project Idea: Explore public understanding of scientific methods. Survey community attitudes towards physics concepts like energy conservation and climate change solutions.

Innovative Approach: Create a YouTube channel featuring physics experiments and explanations in everyday contexts, making complex concepts accessible and engaging for viewers of all ages.

Engineering

Virtual ventures: designing immersive experiences in virtual reality.

Project Idea: Develop user-friendly VR applications. Conduct user tests to improve interface design and user engagement in virtual environments for education and entertainment.

Innovative Approach: Collaborate with local museums to create VR exhibits where visitors explore historical landmarks or futuristic cities, blending technology with cultural heritage.

Green Cities, Bright Future: Innovating Sustainable Urban Solutions

Project Idea: Interview urban planners and engineers about green infrastructure. Analyze case studies of eco-friendly buildings and transportation projects promoting urban sustainability.

Innovative Approach: Partner with city officials to host a hackathon where students propose green tech solutions like smart traffic lights and energy-efficient public spaces, fostering creativity and civic engagement.

Medical Marvels: Exploring Ethics in Biomedical Engineering

Project Idea: Investigate ethical issues in medical technology. Interview biomedical engineers and healthcare professionals about patient privacy and ethical treatment in device development.

Innovative Approach: Create a podcast series featuring interviews with medical innovators discussing breakthroughs in prosthetics and bioengineering, inspiring ethical considerations in healthcare.

Engineering Education Evolution: Enhancing Learning through Innovation

Project Idea: Evaluate engineering education programs. Survey students and educators to identify effective teaching methods and technological tools for promoting hands-on learning and career readiness.

Innovative Approach: Launch a student-led engineering club focused on building sustainable solutions for local challenges, fostering collaboration and real-world problem-solving skills.

Robotics Revolution: From Factory Floors to Everyday Life

Project Idea: Interview robotics experts about automation’s impact. Analyze case studies of robotic applications in industries like manufacturing and healthcare, exploring technological advancements and societal benefits.

Innovative Approach: Organize a robotics expo where students showcase DIY robots and automation projects, demonstrating their practical applications in improving daily tasks and industry efficiency.

Computer Science

Cyber sleuths: protecting online privacy and security.

Project Idea: Investigate cybersecurity awareness. Conduct workshops and simulations to educate users about phishing scams and data protection strategies in digital environments.

Innovative Approach: Design an interactive mobile app where players solve cybersecurity puzzles and learn encryption techniques, promoting digital literacy and safe online practices.

Mobile Magic: Designing User-Centric Apps for Everyday Needs

Project Idea: Develop intuitive mobile applications. Conduct usability tests and gather feedback from diverse users to enhance app functionality and user experience.

Innovative Approach: Collaborate with local businesses to create an app promoting sustainable shopping habits or community events, integrating social features and real-time updates for users.

AI Ethics: Navigating the Moral Landscape of Machine Learning

Project Idea: Discuss ethical dilemmas in AI research. Host a virtual panel discussion where experts debate topics like bias in algorithms and AI’s impact on job markets and privacy.

Innovative Approach: Create an AI chatbot that educates users about ethical AI practices and prompts discussions on social media platforms, encouraging ethical considerations in technology use.

Digital Classrooms: Enhancing Learning through Online Platforms

Project Idea: Evaluate online learning tools. Survey students and educators to assess the effectiveness of digital resources like interactive tutorials and virtual labs in enhancing STEM education.

Innovative Approach: Develop a gamified learning platform where students collaborate on coding challenges and STEM projects, earning points and badges for problem-solving skills and teamwork.

Tech Transformation: Embracing Digital Disruption for a Better Tomorrow

Project Idea: Investigate public perceptions of digital transformation. Interview tech leaders and policymakers about innovations like smart cities and digital healthcare solutions.

Innovative Approach: Host a virtual reality tour of a futuristic city, showcasing sustainable technologies and smart infrastructure designs, inspiring communities to embrace digital advancements for a more connected future.

Mathematics

Math masters: exploring creative problem-solving techniques.

Project Idea: Engage students in math challenges. Organize math competitions or puzzle-solving events to promote teamwork and critical thinking in solving real-world problems.

Innovative Approach: Create an online platform where users solve daily math puzzles and earn rewards, fostering a community of math enthusiasts and lifelong learners.

Beauty in Numbers: Appreciating the Artistry of Mathematics

Project Idea: Explore mathematical beauty. Host an art exhibit featuring mathematical patterns and sculptures, educating visitors about symmetry and fractals in nature and art.

Innovative Approach: Develop an augmented reality app where users interact with virtual mathematical sculptures, exploring their aesthetic qualities and historical significance.

Math Ethics: Ensuring Integrity and Responsibility in Research

Project Idea: Discuss ethical issues in mathematical research. Host a webinar series where mathematicians and scholars debate topics like data privacy and intellectual property rights in mathematical discoveries.

Innovative Approach: Create a podcast series featuring interviews with mathematicians sharing stories of ethical challenges and breakthroughs in their research, promoting ethical awareness and academic integrity.

Learning Beyond Limits: Enhancing Mathematics Education

Project Idea: Evaluate math education strategies. Survey teachers and students about effective learning methods like interactive lessons and peer tutoring in improving math comprehension and engagement.

Innovative Approach: Develop a virtual classroom platform where students attend math workshops and practice sessions with AI tutors, receiving personalized feedback and progress reports.

Mathematics in Action: Solving Real-World Challenges with Numbers

Project Idea: Investigate math applications. Collaborate with engineers and scientists to analyze case studies of mathematical modeling in fields like climate science and financial forecasting.

Innovative Approach: Launch a YouTube channel featuring animated videos explaining mathematical concepts and their practical applications in everyday life and global issues.

Importance of qualitative research in STEM fields

STEM fields (Science, Technology, Engineering, and Mathematics) focus on data and experiments, but qualitative research adds crucial depth by:

Revealing Human Experiences

It explores motivations and challenges within STEM, like why students lose interest in science or how ethics affect engineering decisions.

Informing Inclusive Practices

By understanding diverse perspectives, it helps create inclusive environments in STEM, addressing issues like gender disparities in engineering.

Examining Societal Impacts

It studies public perceptions of technologies (e.g., AI) and ethical concerns in STEM, guiding responsible development.

Understanding the “Why” Behind Data

It complements quantitative research by explaining reasons behind trends, like declining interest in science among students.

Fueling Innovation

By focusing on human insights, it inspires new technologies and strategies to support future STEM leaders.

In conclusion, qualitative research in STEM enriches understanding by exploring human dimensions, ensuring advancements are ethical, inclusive, and impactful.

Benefits of conducting qualitative studies alongside quantitative approaches

There are several benefits to integrating qualitative studies with quantitative approaches in research, especially in STEM fields. Here are the key advantages:

Richer Understanding

Quantitative data provides “what” and “how much,” while qualitative research (interviews, focus groups) uncovers “why” and “how.” This combination offers a holistic view, revealing underlying factors behind trends like declining student interest in science.

Uncovering Hidden Meanings

Quantitative data can lack context. Qualitative research adds meaning by exploring motivations, challenges, and perspectives, helping to interpret quantitative results accurately.

Generating New Questions and Hypotheses

Quantitative studies identify trends, and qualitative research refines findings and generates new research questions. For instance, survey data on poor science teaching can be explored qualitatively to identify effective teaching methods.

Informing Intervention Strategies

Quantitative research quantifies problems, while qualitative insights guide interventions. For example, understanding student interests can shape more relevant science curricula.

Enhancing Generalizability

Quantitative findings are statistically generalizable, and qualitative research provides insights into specific experiences, helping to assess applicability across different contexts.

Understanding Qualitative Research in STEM

Qualitative research offers a unique perspective on the human aspects of STEM fields (Science, Technology, Engineering, and Mathematics), focusing on experiences and meanings rather than numbers. Here’s a simplified breakdown:

Characteristics

Methods: Uses interviews, focus groups, and observations.

Focus: Explores experiences, perceptions, and contexts.

Approach: Inductive, allowing themes to emerge naturally.

Comparison with Quantitative Research

Quantitative: Deals with measurable data and hypotheses.

Qualitative: Focuses on meanings and narratives.

Applications in STEM

Understanding Motivations: Explores why people choose STEM careers or public perceptions of new technologies.

Social Context: Examines how culture, gender, and background affect STEM participation.

Ethical Analysis: Studies societal impacts of technologies like AI and genetic engineering.

Educational Insights: Evaluates teaching methods and student experiences in STEM.

In summary, qualitative research complements quantitative methods by providing insights into the human dimensions of science and technology.

Choosing a Qualitative Research Topic for STEM Students

STEM students excel in numbers and equations, yet science is deeply intertwined with human experience. Qualitative research explores this intersection, uncovering the “why” and “how” behind scientific phenomena. Here’s how to choose an engaging topic:

Find Your Passion: Identify what excites you in STEM, like robotics or clean energy.

Explore Human Impact: Consider how your STEM interest connects with human behavior or societal impacts. For instance, public views on robots in healthcare.

Identify Gaps: Look for unexplored areas in STEM education or technology development.

Topic Ideas

Motivations and challenges.

What drives students into STEM careers?

Challenges faced by minorities in STEM fields.

Learning and Engagement

Impact of teaching methods on student interest.

Strategies for scientific literacy in diverse communities.

Societal Impact

Ethical influences on AI development.

Bridging the digital divide for equitable technology access.

Focus & Feasibility: Choose a specific yet manageable topic.

Data Collection: Plan interviews, focus groups, or observations.

Ethics: Respect participant privacy and consent.

Selecting a compelling topic in qualitative research can offer valuable insights into STEM’s evolving landscape.

Qualitative research helps STEM students explore how science, technology, engineering, and mathematics connect with people’s lives and society.

By studying experiences and stories, students gain a deeper understanding of ethics and societal impacts. This not only boosts their research skills but also prepares them to innovate thoughtfully for a more inclusive future in STEM.

Remember, choose a topic that truly interests you and sparks curiosity—something that can uncover meaningful insights!

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quantitative research title for grade 12 stem

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Best 151+ Quantitative Research Topics for STEM Students

What is Quantitative Research in STEM?

Importance of Quantitative Research Topics for STEM Students

1. Empirical Evidence

2. Data-Driven Decision-Making

3. Innovation

4. Problem Solving

5. Interdisciplinary Applications

Quantitative Research Topics for STEM Students

Biology and Life Sciences

Engineering

Environmental Science

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Mathematics

Earth Sciences

Health Sciences and Medicine

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What Is STEM?

Why STEM Research Is Important?

How to Choose a Topic for STEM Research Paper

Step 1: Identify Your Interests

Step 2: Research Existing Topics

Step 3: Consider Real-World Problems

Step 4: Talk to Teachers and Mentors

Step 5: Narrow Down Your Topic

Qualitative Research Topics for STEM Students:

Easy Experimental Research Topics for STEM Students:

Research Topics for STEM Students in the Philippines:

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Good Research Topics for STEM Students:

Unique Research Topics for STEM Students:

Experimental Research Topics for STEM Students in the Philippines

Capstone Research Topics for STEM Students in the Philippines:

Experimental Quantitative Research Topics for STEM Students:

Descriptive Research Topics for STEM Students

Research Topics for STEM Students in the Pandemic:

Research Topics for STEM Students Middle School

Technology Research Topics for STEM Students

Scientific Research Topics for STEM Students

Interesting Research Topics for STEM Students:

Practical Research Topics for STEM Students

Experimental Research Topics for STEM Students About Plants

Qualitative Research Topics for STEM Students in the Philippines

Science Research Topics for STEM Students

Correlational Research Topics for STEM Students

Quantitative Research Topics for STEM Students in the Philippines

Things That Must Keep In Mind While Writing Quantitative Research Title

1. Be Clear and Precise

2. Use Important Words

3. Avoid Confusing Words

4. Show Your Research Approach

5. Match Your Title with Your Research Questions

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189+ Good Quantitative Research Topics For STEM Students

What Is Quantitative Research

How To Choose Quantitative Research Topics For STEM

Step 1:- Identify Your Interests and Passions

Step 2:- Review Coursework and Textbooks

Step 3:- Consult with Professors and Advisors

Step 4:- Read Recent Literature

Step 5:- Narrow Down Your Focus

Step 6:- Consider Resources and Access

Step 7:- Think About Practicality

Step 8:- Define Your Research Question

Step 9:- Conduct a Literature Review

Step 10:- Consider the Impact

Step 11:- Brainstorm Research Methods

Step 12:- Seek Feedback

Step 13:- Assess Ethical Considerations

Step 14:- Finalize Your Research Topic

Step 15:- Stay Open to Adjustments

Quantitative Research Topics In Physics and Astronomy

Quantitative Research Topics In Biology and Life Sciences

Engineering and Technology Quantitative Research Topics

Quantitative Research Topics In Mathematics and Statistics

Experimental Quantitative Research Topics In Science and Earth Sciences