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55 Brilliant Research Topics For STEM Students

Primarily, STEM is an acronym for Science, Technology, Engineering, and Mathematics. It’s a study program that weaves all four disciplines for cross-disciplinary knowledge to solve scientific problems. STEM touches across a broad array of subjects as STEM students are required to gain mastery of four disciplines.

As a project-based discipline, STEM has different stages of learning. The program operates like other disciplines, and as such, STEM students embrace knowledge depending on their level. Since it’s a discipline centered around innovation, students undertake projects regularly. As a STEM student, your project could either be to build or write on a subject. Your first plan of action is choosing a topic if it’s written. After selecting a topic, you’ll need to determine how long a thesis statement should be .

Given that topic is essential to writing any project, this article focuses on research topics for STEM students. So, if you’re writing a STEM research paper or write my research paper , below are some of the best research topics for STEM students.

List of Research Topics For STEM Students

Quantitative research topics for stem students, qualitative research topics for stem students, what are the best experimental research topics for stem students, non-experimental research topics for stem students, capstone research topics for stem students, correlational research topics for stem students, scientific research topics for stem students, simple research topics for stem students, top 10 research topics for stem students, experimental research topics for stem students about plants, research topics for grade 11 stem students, research topics for grade 12 stem students, quantitative research topics for stem high school students, survey research topics for stem students, interesting and informative research topics for senior high school stem students.

Several research topics can be formulated in this field. They cut across STEM science, engineering, technology, and math. Here is a list of good research topics for STEM students.

• The effectiveness of online learning over physical learning
• The rise of metabolic diseases and their relationship to increased consumption
• How immunotherapy can improve prognosis in Covid-19 progression

For your quantitative research in STEM, you’ll need to learn how to cite a thesis MLA for the topic you’re choosing. Below are some of the best quantitative research topics for STEM students.

• A study of the effect of digital technology on millennials
• A futuristic study of a world ruled by robotics
• A critical evaluation of the future demand in artificial intelligence

There are several practical research topics for STEM students. However, if you’re looking for qualitative research topics for STEM students, here are topics to explore.

• An exploration into how microbial factories result in the cause shortage in raw metals
• An experimental study on the possibility of older-aged men passing genetic abnormalities to children
• A critical evaluation of how genetics could be used to help humans live healthier and longer.

Experimental research in STEM is a scientific research methodology that uses two sets of variables. They are dependent and independent variables that are studied under experimental research. Experimental research topics in STEM look into areas of science that use data to derive results.

Below are easy experimental research topics for STEM students.

• A study of nuclear fusion and fission
• An evaluation of the major drawbacks of Biotechnology in the pharmaceutical industry
• A study of single-cell organisms and how they’re capable of becoming an intermediary host for diseases causing bacteria

Unlike experimental research, non-experimental research lacks the interference of an independent variable. Non-experimental research instead measures variables as they naturally occur. Below are some non-experimental quantitative research topics for STEM students.

• Impacts of alcohol addiction on the psychological life of humans
• The popularity of depression and schizophrenia amongst the pediatric population
• The impact of breastfeeding on the child’s health and development

STEM learning and knowledge grow in stages. The older students get, the more stringent requirements are for their STEM research topic. There are several capstone topics for research for STEM students .

Below are some simple quantitative research topics for stem students.

• How population impacts energy-saving strategies
• The application of an Excel table processor capabilities for cost calculation
•  A study of the essence of science as a sphere of human activity

Correlations research is research where the researcher measures two continuous variables. This is done with little or no attempt to control extraneous variables but to assess the relationship. Here are some sample research topics for STEM students to look into bearing in mind how to cite a thesis APA style for your project.

• Can pancreatic gland transplantation cure diabetes?
• A study of improved living conditions and obesity
• An evaluation of the digital currency as a valid form of payment and its impact on banking and economy

There are several science research topics for STEM students. Below are some possible quantitative research topics for STEM students.

• A study of protease inhibitor and how it operates
• A study of how men’s exercise impacts DNA traits passed to children
• A study of the future of commercial space flight

If you’re looking for a simple research topic, below are easy research topics for STEM students.

• How can the problem of Space junk be solved?
• Can meteorites change our view of the universe?
• Can private space flight companies change the future of space exploration?

For your top 10 research topics for STEM students, here are interesting topics for STEM students to consider.

• A comparative study of social media addiction and adverse depression
• The human effect of the illegal use of formalin in milk and food preservation
• An evaluation of the human impact on the biosphere and its results
• A study of how fungus affects plant growth
• A comparative study of antiviral drugs and vaccine
• A study of the ways technology has improved medicine and life science
• The effectiveness of Vitamin D among older adults for disease prevention
• What is the possibility of life on other planets?
• Effects of Hubble Space Telescope on the universe
• A study of important trends in medicinal chemistry research

Below are possible research topics for STEM students about plants:

• How do magnetic fields impact plant growth?
• Do the different colors of light impact the rate of photosynthesis?
• How can fertilizer extend plant life during a drought?

Below are some examples of quantitative research topics for STEM students in grade 11.

• A study of how plants conduct electricity
• How does water salinity affect plant growth?
• A study of soil pH levels on plants

Here are some of the best qualitative research topics for STEM students in grade 12.

• An evaluation of artificial gravity and how it impacts seed germination
• An exploration of the steps taken to develop the Covid-19 vaccine
• Personalized medicine and the wave of the future

Here are topics to consider for your STEM-related research topics for high school students.

• A study of stem cell treatment
• How can molecular biological research of rare genetic disorders help understand cancer?
• How Covid-19 affects people with digestive problems

Below are some survey topics for qualitative research for stem students.

• How does Covid-19 impact immune-compromised people?
• Soil temperature and how it affects root growth
• Burned soil and how it affects seed germination

Here are some descriptive research topics for STEM students in senior high.

• The scientific information concept and its role in conducting scientific research
• The role of mathematical statistics in scientific research
• A study of the natural resources contained in oceans

Final Words About Research Topics For STEM Students

STEM topics cover areas in various scientific fields, mathematics, engineering, and technology. While it can be tasking, reducing the task starts with choosing a favorable topic. If you require external assistance in writing your STEM research, you can seek professional help from our experts.

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A common problem of STEM students

• September 4, 2022
• Computer Science , Engineering , Robotics , STEM
• Classroom Management , Engaging Lessons , STEM Challenges

Fear of failing and not having the right answer is a common problem of STEM students. Especially if your class is their first STEM experience. In most other courses, students are expected to have the “right” answer. If they are paying attention and working hard, they should be able to get a “good” grade on their assessment, but this is not how STEM works. 

Failure is a central part of STEM. The foundation for STEM education is the engineering design process. There are many variations of the process but all true engineering processes should be iterative and focus on developing solutions to a problem and then improving the solution.

In STEM, students are NOT expected to have a perfect solution on the first try. They may never have a perfect solution. But by going through the process they should develop a solution and determine how they could make the solution even better. This is hard for students to grasp until they experience it

Grab this free STEM lesson to introduce your students to engineering and design thinking today!

What does fear of failure in STEM look like?

I’ve had students totally shut down and refuse to continue working on a STEM project because they didn’t know the “right” answer. I’ve had others get angry and frustrated when their first idea doesn’t pan out, and I’m sure you’ve encountered similar situations or if you’re new to STEM, you will soon!

How do we move past this common problem of STEM students?

So… how do we overcome this common problem of STEM students and help our students embrace STEM thinking?

Here are some things that have worked for me:

• Emphasize the process over the product

STEM is about problem solving, and we should focus on helping students become better problem solvers. This means explicitly teaching students strategies for solving problems, assessing students’ problem-solving skills, and giving specific feedback on their attempts at problem-solving. An engineering design process rubric can help you and your students focus on the problem-solving process.

• Practice with smaller low-stakes challenges

Jumping into a big weeks-long STEM project right off the bat is intimidating. Instead try to plan a few small, simple STEM challenges at the beginning of your course ( Get started with this free “What is engineering?” lesson plan and STEM challenge ! ). It doesn’t feel as stressful when a prototype you’ve only been working on for 10 minutes fails. This will also give you an opportunity to see which of your students may struggle with fear of failure and allow you to do some coaching around perseverance. This leads me to tip #3.

• Talk about perseverance

As a class, discuss what perseverance looks like and what it feels like. Call it out when you see students persevering in the classroom. Give students opportunities to shout one another out for persevering. Even students who take to STEM naturally will encounter a project that is challenging. It’s important to have everyone buy into the culture of perseverance.

• Model overcoming failure

As STEM teachers, we are often learning new technologies as we use them with our students. You will make mistakes, and that is OK. Use it as a teaching opportunity and show students how you go about solving the problem. As they say “actions speak louder than words.”

• Don’t give answers but scaffold as needed

When students are really struggling, it’s easy to feel like you should just give them an answer. However, when we do this, our students miss out on this opportunity to practice perseverance and create their own solution. Instead, when students really need a push, you can guide them to identify points of failure and help them identify ways to improve their prototype by asking questions. “What happened when you did X? Why do you think Y happened? What does the project criteria say about Z?” In this way, you can guide their thinking and help them move forward in the process, but your students will still have ownership over their final design.

You are imparting life skills

One of the most challenging and best parts of being a STEM teacher is that you get to cultivate so many important life skills in your students. With intentional planning and lots of opportunities to practice, you’ll help them move past this common problem of STEM students. Then you’ll be ready to dive in and tackle the big problems and watch your students flourish. 

Looking for more support in teaching STEM?

Grab this FREE “What is engineering?” lesson and materials to introduce your students to engineering and STEM.

In this lesson, students will share their background knowledge about engineering. Then they engage in a hands-on engineering experience as they design, build, and test cell phone stands. Afterward, students reflect on their experience, identify misconceptions about engineering, and construct a deeper understanding of what engineers do. Next, students research the definition of engineering and discover a variety of engineering careers. Finally, students reflect on their learning and revise their definitions of engineering from the beginning of class.

Grab this lesson and STEM challenge today and begin helping your students confront their fear of failure in STEM today!

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

• Open access
• Published: 13 December 2019

Students’ reasons for STEM choices and the relationship of mathematics choice to university admission

• Satu Kaleva   ORCID: orcid.org/0000-0002-4847-6513 1 ,
• Jouni Pursiainen 1 ,
• Mirkka Hakola 1 ,
• Jarmo Rusanen 1 &
• Hanni Muukkonen 1  

International Journal of STEM Education volume  6 , Article number:  43 ( 2019 ) Cite this article

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Despite the increasing need for STEM skills, to date, the connection between STEM subject choices and their impact on students’ educational pathways has not been widely studied. Focusing on the mathematics choice (basic/advanced/no mathematics), a large register dataset that covered students admitted to Finnish universities during 2013–2015 ( N = 46,281) was combined with upper-secondary school matriculation examination data ( N = 93,955) to find out how this choice influenced the students’ university admissions. This large dataset was also examined to establish the current gender distributions in different university degree programs from the perspective of mathematics choices. Further, to find out the students’ reasons behind their mathematics choices, a cohort sample of 802 student responses was collected from upper-secondary schools. We also investigated the students’ interests in different fields of study to establish any gender differences in them.

The register data analysis suggested that in Finland, students’ mathematics choices had a strong influence on the university admission outcomes. For instance, only 33% of the upper-secondary school graduates took the advanced mathematics ME test in 2013–2015, yet the number of those admitted to universities who had taken the advanced mathematics ME test was 55%. Most of the university degree programs were female dominated, yet the university students with advanced mathematics were mostly male, and especially the STEM fields in the Finnish universities were male dominated. As for the reasons behind the mathematics choices, students who chose advanced mathematics believed in its usefulness for their future studies and careers. We also found significant gender-based educational differences regarding all the study fields, with STEM careers attracting more males than females.

Advanced mathematics was highly valued in Finnish universities, and many students chose advanced mathematics believing in its usefulness for their future studies or careers. Yet, their further study interests and career plans were segregated by gender. As there is a rising need for STEM skills, we must seek effective ways to deliver the evolving possibilities of STEM fields to students, especially girls, during the earlier years of their educations.

Introduction

Reasons for the gender gap in STEM (science, technology, engineering, and mathematic) fields have been sought in several studies (e.g., Allen & Eisenhart, 2017 ; Chow, Eccles, & Salmela-Aro, 2012 ; Perez, Cromley, & Kaplan, 2014 ). In the USA, Wang and Degol ( 2016 ) found six explanations for women’s underrepresentation in STEM fields: (a) cognitive ability, (b) relative cognitive strengths, (c) occupational interests or preferences, (d) lifestyle values or work/family balance preferences, (e) field-specific ability beliefs, and (f) gender-related stereotypes and biases. As the size and composition of the STEM workforce continuously fails to meet the demand (Jang 2016 ; Wang & Degol, 2016 ), it is important to understand the barriers and factors that influence individual education and career choices (Blotnicky, Franz-Odendaal, French & Joy, 2018 ).

Individual differences in self-efficacy beliefs can impact career choices. Social cognitive career theory (SCCT) suggests that career interest, choice, and personal goals form a complex human agency process that includes performance, self-efficacy, and outcome expectations (Bandura, 1986 ; Lent, Brown, & Hackett, 1994 ). Further, Wang, Eccles, and Kenny ( 2013 ) suggested that the pattern of gender differences in math and verbal ability may result in females having a wider choice of careers in both STEM and non-STEM fields compared with males. Thus, mathematically capable individuals, who also had high verbal skills, were less likely to pursue STEM careers than individuals who had high math skills but moderate verbal skills. Wang et al. ( 2013 ) found that that the group with high math skills and high verbal ability included more females than males. Their study provided evidence that it is not a lack of ability that causes women to pursue non-STEM careers but rather the greater likelihood that females with high math ability also had high verbal ability and thus could consider a wider range of occupations than their male peers with high math ability who were more likely to have moderate verbal ability.

Students with higher mathematics self-efficacy and STEM career knowledge are more likely to choose a STEM career (Blotnicky et al. 2018 ; Wang et al. 2013 ). In addition, students’ own beliefs that success in science depends on exceptional talent can negatively impact their motivation to learn as well as a lack of enjoyment and confidence (Lin-Siegler, Ahn, Chen, Fang, & Luna-Lucero, 2016 ; Wu, Deshler, & Fuller, 2018 ). Without encouragement or adequate knowledge about the educational and career opportunities that STEM skills enhance, there is a risk that students will dismiss a STEM-based career path as a potential option for their future (Blotnicky et al. 2018 ). Although the gender gap in studying STEM subjects (e.g., number of courses taken and performance in those courses) has narrowed in recent decades (Välijärvi & Sulkunen, 2016 ; Wang & Degol, 2013 ), females continue to be less likely to pursue STEM careers than their male counterparts (Ceci & Williams, 2007 ; Hübner, Wille, Cambria, Oschatz, Nagengast, & Trautwein, 2017; Stage & Maple, 1996 ).

This study deals with these internationally recognized challenges to find out how subject choices influence later educational paths and careers and how 16- and 17-year-old students in the Oulu area define their own choices in terms of STEM subjects, study plans, and careers. Finland has an outstanding digital infrastructure, and its ICT sector is bigger than that of its European peers (European Commission, 2019 ). Especially in the Oulu area, the ICT sector has only existed for about 30 years but has grown quickly, providing an increasing number of career opportunities particularly for those with STEM competencies.

In Finland, the importance of upper-secondary school subject choices is currently increasing, as student selection to the universities will be more heavily based on the results of the matriculation examination in the future. By understanding the reasons behind those choices, we can discover the existing gaps in our education system and develop ways for education to assist youngsters in seeing the new, growing STEM opportunities and to meet the demands of the future.

Gender gap in studying STEM narrows, yet remains in STEM work fields

Dasgupta and Stout ( 2014 ) investigated why the shortage of women in STEM careers remains stark. Their research points to different obstacles particularly relating to three developmental periods: (a) childhood and adolescence, (b) emerging adulthood, and (c) young-to-middle adulthood. In their article, Dasgupta and Stout describe how specific learning environments, peer relations, and family characteristics become obstacles to STEM interest, achievement, and persistence in each period. They discovered some key obstacles: (1) in childhood and adolescence, masculine stereotypes about STEM, parents’ expectations of daughters, peer norms, and a lack of fit with personal goals make girls move away from STEM fields; (2) in emerging adulthood, feeling like a misfit in STEM classes, being vastly outnumbered by male peers, and lacking female role models make women avoid STEM majors or leave prematurely; and (3) in early to mid-adulthood, subtle gender bias in hiring and promotion, biased evaluation of scientific work, non-inclusive department climates, juggling work/family responsibilities, and difficulty returning after a family-related pause undermine the retention of women in STEM. To remove these obstacles, Dasgupta and Stout ( 2014 ) recommend evidence-based programs and policies be implemented during each of these developmental periods.

The scale and variability of gender differences in vocational interests have been examined, e.g., by Holland’s ( 1997 ) RIASEC (realistic, investigative, artistic, social, enterprising, and conventional) theory of careers that explain what personal and environmental characteristics lead to satisfying career decisions, what personal and environmental characteristics lead to stability and change in the kind and level of work a person performs over time, and what are the most effective methods for providing assistance to people with career problems. His theory allows us to predict the outcome of person-environment interactions and provides explanations for those previous fundamental questions (Holland, 1997 ). Su, Rounds, and Armstrong ( 2009 ) studied vocational interests and suggest that men prefer working with things and women prefer working with people. Indeed, to be engaged in studying STEM subjects, students need to have high levels of interest, skills, and desire for challenges (Wang & Degol, 2017, Linnansaari et al., 2015 ). Students’ situational interest in science lessons is not as uniform as in other lessons, and Linnansaari et al. ( 2015 ) suggest that girls tended to be interested in life science lessons and uninterested in physical science lessons, and in contrast, boys are highly interested in physical science topics but not life sciences. For example, in previous studies, physics was considered uninteresting because it was considered as difficult, irrelevant, and boring by some students, especially girls (Williams, Stanisstrect, Spall, Boyes, & Dickson, 2003 ).

Science and STEM identity has a complex differential function in supporting students’ optional science choices by gender, and STEM identity may be associated with academic performance and flourishing in undergraduate physics courses at the end of the term, particularly for women (Seyranian et al., 2018 ; Vincent-Ruz & Schunn, 2018 ). In mathematical problem solving, the role of self-efficacy beliefs and the nature of science identity has also been widely investigated (Pajares & Miller, 1994 ; Pajares & Urdan, 2006 ; Vincent-Ruz & Schunn 2018 ; Zeldin & Pajares, 2000 ). In their longitudinal study, Parker, Marsh, Ciarrochi, Marshall, and Abduljabbar ( 2014 ) found (a) a strong relationship between achievement, self-efficacy, and self-concept in mathematics at age 15; (b) both self-concept and self-efficacy being independent and similarly strong predictors of tertiary entrance ranks at the end of high school; (c) math self-efficacy as a significant predictor of university entry but math self-concept was not; and (d) math self-concept as a significant predictor of undertaking post-school studies in science, technology, engineering, or math, but math self-efficacy was not.

The impact of teaching STEM subjects has been studied, e.g., by Bottia, Stearns, Mickelson, Moller, and Valentino ( 2015 ). They suggest that although the proportion of female math and science teachers at school had no impact on male students, it had a powerful effect on female students’ likelihood of declaring and graduating with a STEM degree, and the effects were largest for female students with the highest math skills (Bottia et al., 2015 ).

Factors impacting students’ decisions in subject selection

There are many factors that have an impact on the subject choices that students make. Palmer, Burke, and Aubusson ( 2017 ) used a best-worst scaling (BWS) survey to investigate the relative importance of factors thought to impact students’ subject selection decisions. According to their findings, students ranked enjoyment, interest and ability, and perceived need in their future study or career plans as the most important factors in both choosing and rejecting subjects. They considered advice from teachers, parents, or peers to be relatively less important. According to several studies, enhancing students’ enjoyment, interest, and perceptions of their ability in science, and their attitude towards it, as well as increasing student perceptions of the value of science in a future career may result in more students studying science at school (Osborne, Simon, & Collins, 2003 ; Palmer et al., 2017 ).

Another important issue is the quality of STEM education where the teacher's role is essential. Slavit, Nelson, and Lesseig ( 2016 ) suggest that a teacher’s role is a complex mixture of learner, risk-taker, inquirer, curriculum designer, negotiator, collaborator, and teacher. It is important to understand teachers’ own beliefs and perceptions related to STEM talent development. According to Margot and Kettler ( 2019 ), teachers with increased confidence in teaching STEM would likely be more effective at integrating STEM activities, and increased confidence leads to better performance during instruction, which leads to gains in student learning.

Case Finland

The Program for International Student Assessments (PISA) conducted by the Organization for Economic Co-operation and Development (OECD) has kept Finland among the highest-ranking countries in the world in education since 2001. However, recent PISA scores present an ambivalent message. On the one hand, Finland is still a top-ranking country in education. On the other, a decrease in learning outcomes, observed for more than 10 years, has leveled off in reading literacy and slowed down in mathematical literacy but still remains a concern. These concerns extend to the future of basic education, as the average trend in all three domains has been declining since 2009 (Välijärvi & Sulkunen, 2016 ). The PISA 2015 survey showed that the number of poor performers in science was growing, and the number of top performers was declining in Finland, especially among boys, and that regional equity was deteriorating. The number of Finnish students who performed poorly in science had nearly tripled, and the number of top performers had dropped by nearly one third. Altogether, 65 percent of the students who performed poorly in science also did poorly in mathematics and reading. Of these, two thirds were boys (Ministry of Education and Culture, 2016 ).

According to the new government program in Finland, a national goal is to increase the number of highly educated people in the youth population to reach more than 50% (Finnish Government, 2019 ). Higher STEM identification may be associated with higher academic achievement (Seyranian et al., 2018 ), yet STEM subjects or fields such as ICT (Castaño & Webster 2011 ) are not attracting enough students, and the decreasing number of students in science learning has been recognized as a national problem (Linnansaari, Viljaranta, Lavonen, Schneider, & Salmela-Aro, 2015 ). Finland provides many career opportunities especially for people with STEM competencies. As an example, in 2014, The World Economic Forum in their Global Information Technology Report (GITR) ranked Finland as number one for its outstanding digital infrastructure for the second consecutive year (Bilbao-Osorio, Dutta, & Lanvin, 2014 ). The successes in the digital fields were largely based on STEM competencies, but as in many other countries, Finland is barely getting enough students with sufficient skills in mathematics and science.

Current study

Research questions.

To determine how mathematics choice related to the students’ university admissions, we combined two large national datasets. Based on the combined register dataset, we examined:

How students’ mathematics choices related to the university admissions and to the student distribution in different degree programs?

From the perspective of mathematics choices, what was the gender distribution among bachelor’s degree graduates in different degree programs?

Based on a cohort sample of one city’s first-year upper-secondary school students’ responses, we also investigated:

What were the reasons that students chose basic or advanced mathematics during the first year of their studies?

Which further study fields were students interested in during the first year of their upper-secondary school studies and what gender-based differences were found in the interest?

Setting of the study: education system in Finland

In Finland, there are 5.5 million inhabitants, of which 2.8 million are female. Approximately 2 million are wage and salary earners, and 1.3 million children and youngsters are students. The number of high educational qualifications achieved in 2015 from universities of applied science was 26,175 and from research universities was 32,718 degrees (Statistics Finland, 2017 ). Education is free of charge for all, providing an equal basis for education. The Finnish education system consists of:

Early childhood education and care before compulsory education begins.

Pre-primary education for children in the year preceding the beginning of compulsory education.

Nine-year compulsory basic education (comprehensive school).

Upper-secondary education (general upper-secondary education or vocational education).

Higher education (universities or universities of applied sciences).

Furthermore, adult education is available at all levels. (Ministry of Education and Culture 2017 ; Finnish Ministry of Education and Culture 2017 ).

General upper-secondary education

After the 9-year compulsory basic education, school-leavers opt for general or vocational upper-secondary education. Both forms usually take 3 years and provide eligibility for higher education. More than 90 percent of the relevant age group starts general or vocational upper-secondary studies immediately after basic education. There are no national tests in the basic education stage (ages 7–15), and if students decide to continue their studies in upper-secondary education, a national examination, the Matriculation Examination (ME), takes place at the end of their studies (age 19). The tests are assessed first by the upper-secondary school teachers and then by assessors, who are members or associate members of the Matriculation Examination Board. Every year, approximately 30,000 candidates take the exam, with 6% of the candidates failing the exam. The examination consists of four compulsory tests and additional optional tests. The compulsory tests are the candidate’s mother tongue, together with three other tests selected from four options, which are the second national language (advanced/intermediate level), a foreign language (advanced/basic level), mathematics (advanced/basic level), and one test in the general studies battery of tests, sciences and humanities (Britschgi, 2014 ).

The Finnish National Core Curriculum for Upper Secondary Schools was renewed in 2016, and within the new curriculum, there were some changes regarding mathematics studies. Previously, students had to choose between basic or advanced mathematics before entering upper-secondary school, but now the choice is made during the first year. The purpose of this renewal was to raise students’ interest in advanced mathematics by giving some insights during the first year of their studies about advanced mathematics advantages.

Participants

This study used combined register data , including (1) students who were admitted to Finnish universities during 2013–2015 ( N = 46,281) and (2) data of students who took the upper-secondary school Matriculation Examination (ME) ( N = 93,955) during the same years, 2013–2015. This dataset had 46,281 entries representing 43,639 individual persons, of which 55% were female. Upper-secondary school graduates are usually 19 years old, but university applicants can be older. Their ages were, however, not available in the register data.

In addition, the participants of this study included students who completed the questionnaire. The questionnaire data was collected with an online survey of first-year upper-secondary school students. This data represented a total of 1,539 first-year upper-secondary school students from the Oulu area (age 16). Of them, 802 students responded to the online survey, providing a response rate of 52.1%. The gender distribution of the participants was 40% male and 60% female.

Data collection

The original register data , including all the students admitted to Finnish universities during 2006–2016, was collected from the Finnish universities by CSC, the IT Center for Science Ltd. This study used the data regarding the years 2013–2015 ( N = 46,281). The Matriculation Examination data ( N = 93,955) was collected by the Matriculation Examination Board of Finland.

The questionnaire data ( N = 802) was collected with the Webropol online survey tool, collecting both quantitative and qualitative data about students’ subject choices, future study aspirations, and career plans. The survey was carried out in the spring semester 2017 during school class hours under teacher supervision. In total, the questionnaire included several question points, and this research focused on those questions regarding mathematics choices and study aspirations. These questions were presented in the questionnaire as follows:

Please continue the applicable sentences that concern your own choice of mathematics: (open-ended questions)

I chose advanced mathematics, because…

I did not choose advanced mathematics, because …

I did not choose basic mathematics, because …

I chose basic mathematics, because…

I am interested in the following study fields:

(Likert scale, 1–5, from 1 = not interested to 5 = very interested)

Arts and Culture

Social Sciences

Economics, Administration, Law

Natural Sciences

Information Technology, IT Communication

Agriculture, Forestry

Health and wellbeing

Service Sector

Data analysis

Register data.

Regarding the first and second research questions, the combined register data was examined and the research units concerning students with no corresponding ME results (altogether 8,073 research units) were removed. One part of this missing information stems from upper-secondary school graduates from the years before 2006, when the structure of the examination was different. The data, however, did not contain information on the year when the ME was taken. Students admitted by entrance exam and without completing the ME (e.g., with a background in vocational schools) also belonged to this group.

Altogether 2,563 duplicates (having the same personal ID) were removed from the register data. However, multiple entries on the same student indicating different degree programs were not removed. Since the focus of this study was on student admission, it was important to count every entry to a degree program, regardless of any previous or later choices of the applicant.

Questionnaire data

Regarding the third and fourth research questions, the questionnaire data ( N = 802) was based on a cohort sample of 16-year-old upper-secondary school student responses. We investigated what kinds of reasons students gave for choosing basic or advanced mathematics and if there were gender differences in students’ mathematics choices.

The analysis of students’ reasons began by studying the students’ responses given to the open-ended question. After this, thematic categories were formed, and each response was individually placed into one of these reason categories, following the Palmer et al. ( 2017 ) best-worst Likert scaling (BWS) system. In their study, Palmer et al. used BWS-Choose and BWS-Reject subject selection attribute statement pairs that were grouped as “Advice, Enjoyment and Interest, Logistics, Ability (marks), Subject characteristics, Teaching, and Usefulness.” This grouping was adaptable for our analysis, since the original researchers similarly examined the reasons why school students chose and rejected science. In this study, the themes found among students’ answers to open-ended questions were thematically categorized into five reason categories: (1) Usefulness (2) Enjoyment and Interest (3) Logistics, (4) Self-efficacy, Ability, and Competence, and (5) Advice, Teaching, and Other. The “subject characteristics” was left out, as we focused only on one subject choice. Within one open-ended response, a student often gave multiple reasons behind his or her mathematics choice, therefore, one response had to be divided into multiple units of analysis. The mutually exclusive reason categories are described in Table 1 , along with examples of the students’ reasons for choosing or rejecting advanced or basic mathematics .

To examine inter-coder reliability, two independent raters categorized 10% of the qualitative data. The kappa coefficient of 0.753 (Cohen’s kappa) was statistically different from 0, suggesting that the two independent ratings were largely similar. The reason categories are described in Table 1 .

To examine the differences between females and males in reasons for choosing or rejecting advanced and basic mathematics, we used Fisher’s exact test, which is similar to the chi-squared (χ2) test but performs better for imbalanced distributions and distributions with small expected values. Regarding the question about students’ interest towards the study fields, the Likert-scale responses were analyzed with a t-test to examine similarities of female and male interests.

The results showed that the student admission process of Finnish universities significantly appreciates advanced mathematics. Only 33% of the upper-secondary school graduates took the advanced mathematics test in the ME in 2013–2015. The percentage of all students admitted to the universities who took the advanced mathematics test in the ME was 55%. Furthermore, our data suggests that more than 80% of the upper-secondary school students/university applicants with advanced mathematics gain admission to the universities. In fact, the first-year university students in our data with advanced mathematics (25,738) represented as much as 83% of the upper-secondary school graduates with advanced mathematics (30,905) during the same 3-year period, 2013–2015.

The significance of advanced mathematics in the different university degree programs can be seen in Fig. 1 and Table 2 . Most of the degree programs had higher percentages in advanced mathematics than the overall percentage in the ME (33%), and all were higher than 23%. This reflects a situation where the needs of the Finnish universities can hardly be met by mathematically skilled upper-secondary school graduates. As seen in Table 2 , Medicine, Dentistry, and Veterinary Medicine attracted high numbers of applicants and received high percentages of students with advanced mathematics (90%, 83%, and 83%). However, Technology (7,095) and Natural Sciences (6,324) dominated the student numbers, having also high percentages for advanced mathematics. Either of these numbers was higher than the corresponding numbers for the remaining 18 degree-offering programs. Humanities and Education have relatively high numbers of students with advanced mathematics, even though the percentages were low, 24% and 28%, respectively, compared with their totals.

University students in different degree programs with advanced, basic or no mathematics in ME test

Most of the degree programs were female dominated (Table 2 ) and so also was the matriculation examination itself, with 59% females in 2013–2015. While 56% of all university students were female, university students with advanced mathematics were mostly male with only 44% being female students. Basic (63%) and No mathematics (79%) were clearly female dominated. In the different university degree programs, Technology had only 22% female students, Economic Sciences 41%, and Natural Sciences 45%, whereas most of the other programs were clearly (> 60%) female dominated.

In Fig. 1 , Visual Arts, Theatre, Arts, Musicology, and Dance have been merged into Arts Combined . Of the upper-secondary school graduates, 46% had taken the basic mathematics test in the ME exam, but in the universities, their percentage was as low as 30%. The highest numbers (Fig. 1 ) can be seen in Economic Sciences, Education, and Humanities (2,758, 2,572, and 2,396, respectively), which also had high percentages for Basic Math (in Table 2 , 40–49%). Only in Education and Administrative Sciences (49% and 47%) were the percentages of Basic Math higher than in the ME exam (46%). Technology and Medicine were dominated by Advanced Math, and the Basic Math student numbers were very low. Basic Math was female-dominated (64% female), but in Technology (32% female), Economic Sciences (42% female), Science (47% female), and Physical Education (48% female), the students with Basic Math in the ME exam were in a majority.

About 21% of the upper-secondary school graduates in the ME data had not taken a mathematics test at all. The weight of this No Math group was 15% among the admitted students. This group was 79% female dominated, which was also reflected in different disciplines (Table 2 ). Understandably, there were some degree programs, like Technology (31 out of 7,321), where the number of No Math students was very low. The No Math students were relatively abundant in Education (24%), which was also a highly female-dominated degree program (84%). This may reflect low motivation or even a dislike for mathematics among education students, most of whom become teachers at different school and early childhood education levels. There is no evident reason for high No Math numbers in Political Science (30%), Social Science (29%), and Administrative Science (25%).

The third research question addresses what kinds of reasons students gave for choosing basic or advanced mathematics based on the qualitative data from the survey. In total, 1,601 answers were given to the open-ended questions. Their distribution in the reason categories based on choosing or rejecting advanced or basic mathematics is presented in Table 3 .

According to the students’ responses, the main reason for choosing advanced mathematics was its usefulness ( N = 359). Many students replied that they believed Advanced Math opens more options for their future professions or places of study, although during the first semester of upper-secondary school, many did not have a clear view of their future studies or career plans. Those who had a clear career plan towards fields that demand advanced mathematics skills were clearly aware of the usefulness of the subject. For example, “I assume that by studying it [Advanced Math] I will have a better chance to enter the professions that are better paid. I also know that I need it to enter medical school” and “I don’t know my future profession, so I chose advanced mathematics as I don’t want to rule out any options.” Another reason for choosing advanced math was enjoyment and interest ( N = 119). Those who enjoyed mathematics wanted to practice more. Many students reported that they wanted to challenge themselves and that solving problems was enjoyable. For example, “I enjoy mathematics and want to challenge myself with it” and “I want to learn more mathematics and accept new challenges, and I enjoy solving problems.” The third most named reason for choosing advanced mathematics was self-efficacy, ability, and competence ( N = 54). Many found that they had skills and competences in mathematics, and during their previous studies, they had received good marks in mathematics. For example, “I want to learn mathematics as much as possible, as I am skilled in it” and “I have previously received good marks in mathematics.”

Among the upper-secondary school students in Oulu, only a very few students mentioned advice from parents or peers or teaching style or quality as important factors when choosing advanced mathematics.

Some students described rejecting advanced mathematics ( N = 58) because they did not find the subject necessary or useful for their future field of study or work. Particularly, females ( N = 93) versus males ( N = 45) responded that they rejected advanced mathematics for a lack of interest and competence. The most reported reason for choosing basic mathematics ( N = 111) was self-efficacy, ability, and competence, and many of these respondents reported they did not feel they were “able to make it” in the advanced mathematics course.

Students often explained rejecting basic mathematics ( N = 231) with reasons similar to those for choosing advanced mathematics; they wanted to keep more study and career options open by selecting advanced mathematics.

When we compared female and male responses, there were evident differences in between the two. Females reported more reasons ( N = 127) than males ( N = 4) related to enjoyment and interest for rejecting advanced mathematics and self-efficacy, ability, and competence (females, N = 93; males, N = 45). The same pattern was also evident in the responses for choosing basic mathematics.

Regarding the reasons for upper-secondary school students choosing or rejecting mathematics, we used Fisher’s exact test for finding out if there were gender-related differences. Fisher’s exact test (two-way) indicated that there were no significant gender-related differences in reasons for choosing advanced mathematics ( p = 0.153). However, regarding rejecting advanced mathematics ( p < 0.001), choosing basic mathematics ( p < 0.01), and rejecting basic mathematics ( p < 0.05), the Fisher’s test indicated that there were significant gender-related differences in the reasons students provided for their choices.

Finally, students rated their interest in the provided study fields (see Table 4 ). In the questionnaire, students were asked to rate their interest towards the study fields of higher education on a scale of 1–5. Health and wellbeing, Humanities Service Sector, Education, and Arts and Culture attracted more females than males. Assessing with the t-test, we found statistically significant differences regarding every field of study . Especially, in terms of Information Technology/IT Communication and Technology, females indicated significantly less interest towards these fields compared to males. Vice versa, towards Health and wellbeing and Education study fields, males had significantly less interest.

Regarding the question of how students’ mathematics choice affects their admission to university degree programs, it is evident that the choice of mathematics appears as a significant divider of Finnish students’ educational pathways. Secondary school graduates who completed the advanced mathematics test had very good chances to be admitted to the universities. About 83% of the secondary school graduates who completed the advanced mathematics test were eventually admitted to the universities in 2013–2015. This can be concluded by direct comparison of the numbers of advanced mathematics in the register data (an annual average of 8,926) and the matriculation examination data (an annual average 10,302). Effectively, 83% was very close to all, since our data did not represent all the new students in the universities during those years, and many secondary school graduates were also aiming to study at the universities of applied sciences. All the degree programs appreciated mathematical skills, and some of them had problems with student admissions. These problems were especially related to Technology and Science, where the need for mathematical skills was very high.

Regarding the students’ reasons behind their mathematics choices between basic or advanced, there were some differences between female and male respondents and their given reasons. Compared to males, females often reported lack of self-efficacy, ability, and competence towards mathematics studies as reasons for not selecting advanced mathematics, corresponding to the findings of several prior studies (Ceci & Williams, 2007 ; Dasgupta & Stout, 2014 ; Hübner et al., 2017 ; Stage & Maple, 1996 ). Nonetheless, the majority of both genders acknowledged the value of the subject at the age of 16, during their first year of studies in upper-secondary school. However, many of these students tended to move to basic mathematics studies during the ensuing 2–3 years. Among students taking part in the online survey, as many as 65% selected advanced mathematics during the first years but were already hesitating: “ I want to try it [advanced mathematics] out first” and “It is possible to drop out from advanced and go to basic mathematics.” Indeed, moving from challenging, time-consuming advanced mathematics (14 courses) to basics (9 courses) was more likely than the other way around.

This study shows that gender differences were especially significant in students’ interest towards different fields of study. In the cohort sample, males were interested in Information Technology, IT Communication, and Technology but showed less interest towards Health and Wellbeing and Education than their female counterparts. This result is in line with a previous study (Su et al., 2009 ) that found men prefer working with things and women prefer working with people, also raising questions about STEM identity as studied by Seyranian et al. ( 2018 ).

On the limitations of this study, from the register data, we were able to investigate only the issues regarding students’ gender. The qualitative data might be somewhat influenced by respondents’ gender; males tended to provide shorter responses compared to the females. In future studies, these factors may need to be also considered.

Conclusions

The current study investigated the connection between STEM subject choice, especially the choice of mathematics, conducted in upper-secondary school and their relation to university admissions. Further, we examined the gender distribution in different university degree programs from the perspective of the mathematics choice for finding out in which programs students with advanced, basic, or no mathematics end up within the universities. Next, we analyzed the large dataset to explore what is the gender distribution in different university degree programs covering all the universities in Finland. Finally, for finding out the students’ reasons behind the mathematics choices, we collected a cohort sample of 802 students from upper-secondary schools to investigate the students’ interest in different fields of study to establish the existing gender differences in them.

These results show that advanced mathematics was highly valued in Finnish universities. According to our cohort sample, the majority of students that chose studies in advanced mathematics believed in its usefulness for their future studies or career. Yet, although the Finnish girls were the topmost mathematics performers in the world (Ministry of Education and Culture, 2016 ), we found that their further study interests were significantly segregated by gender, neglecting the vast possibilities of STEM careers. Adding to the STEM identity and gender study findings of Seyranian et al. ( 2018 ), careful attention must be paid to students’ physical and social learning environments which may send cues about who belongs in or may succeed in STEM fields.

The foundation for mathematics and interest towards STEM is built during the early years of education. Blotnicky, Franz-Odendaal, French, and Joy ( 2018 ) have recognized a need to improve access to knowledge which facilitates students’ understanding of STEM careers and the nature of STEM work. According to Cannady et al. ( 2017 ), one-size-fits-all policies for broadening participation in the STEM workforce are unlikely to be successful, but programs that are designed to generate wonder and fascination with STEM content may be successful in attracting more girls (Cannady et al., 2017 ).

Recently, research has focused on identifying the biological and sociocultural factors for the divergence in gender abilities, interests, and career choices. Wang and Degol ( 2016 ) concluded that for reducing the gender gap in STEM, attention should be given to address the contributory cognitive, motivational, and sociocultural factors, primarily by maximizing the number of career options that women perceive as attainable and compatible with their abilities, preferences, and goals. Otherwise, large numbers of mathematically talented females will continue to slip through the cracks when their choices are restricted by cultural barriers, gender stereotypes, or misinformation.

In Finland, students make subject choices that can decisively affect their futures at the age of 16 or even earlier. Therefore, it would be essential to seek new, more effective means and ways to deliver information during their early years about relatively new careers such as ICT (Information and communication technologies). As social cognitive career theory (Lent et al., 1994 ) suggests, career interest, choice, and personal goals form a complex human agency process that includes performance, self-efficacy, and outcome expectations. Further, Seyranian et al. ( 2018 ) studied interventions that strengthen STEM identification for women and suggested that these interventions may signal one promising approach to reduce gender disparities. Currently, in Oulu, new STEM learning environments are evolving in close cooperation with educators and ICT companies. It is important to discover if these types of new learning environments, out-of-school time science activities (Dabney et al. 2012 ), or carefully structured STEM interventions can actually help girls’ STEM identities to flourish and spark boys’ interests towards STEM subjects. We suggest further research to find out if such actions can provide effective ways to motivate youth towards STEM pathways and subjects and also to help them see the constantly evolving possibilities of future STEM careers.

Availability of data and materials

The questionnaire data were collected mostly from minor aged (16–17-year-old) students with consent of confidentiality and therefore cannot be shared. The register datasets were provided to the University of Oulu for research purposes under strict condition of not sharing them without permission from the CSC, the other Finnish Universities involved, as well as the Matriculation Examination Board. For further information about the availability of the register data, please contact the corresponding author.

Abbreviations

Best-worst scaling

Information and communication technology

Matriculation examination

Organization for Economic Co-operation and Development

The Program for International Student Assessment

Science, technology, engineering, and mathematics

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Acknowledgements

The authors thank the students, student counselors, teachers, and principals of Oulu upper secondary schools, also the staff of the Department of Education and culture of Oulu, for giving their time and support for this study. We are grateful to the Matriculation Examination Board and the CSC for opening their student registers for this research. We also owe a debt of gratitude to every Finnish research university that kindly supported our research by giving us their student selection information for research purposes.

The authors are grateful for receiving funding for this research from the University of Oulu, the Adult Education Allowance of Finland, and from the Oulu University project regarding student admissions, funded by the Ministry of Education and Culture .

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Satu Kaleva, Jouni Pursiainen, Mirkka Hakola, Jarmo Rusanen & Hanni Muukkonen

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Satu Kaleva collected the cohort sample, carried out the analysis and drafted the manuscript together with Hanni Muukkonen and Jouni Pursiainen. Jouni Pursiainen and Jarmo Rusanen collected the register datasets, Mirkka Hakola and Jouni Pursiainen analyzed the data and Jouni Pursiainen contributed on writing the data analysis of the quantitative data. Hanni Muukkonen and Jouni Pursiainen provided feedback on the full manuscript and participated in the study’s conceptualization, design, and coordination conducted by Satu Kaleva. All writers have approved the final manuscript.

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Satu Kaleva is a doctoral student at the University of Oulu in the Department of Education. She has worked for several years in the development projects of education, and her research interest is on developing educational pathways for improving gender and socio-economic equity in STEM fields, and in school and work life cooperation for enhancing students’ future skills.

Jouni Pursiainen is professor in Chemistry in Oulu University also leads the STEM center of Oulu University. He is interested in the study paths from upper secondary school to the universities, especially the effect of subject choices in the matriculation examination.

Mirkka Hakola is a full time Release Manager at Empower IM but also a graduate student at the University of Oulu in the Department of Chemistry. Her master theses research concentrated in finding the background factors that are influencing student acceptance process. She has participated in the development projects of education.

Jarmo Rusanen is a professor emeritus, geoinformatics, at the Geography Research Unit, University of Oulu. His research has been focused always on register based data, like matriculation examination in this study.

Hanni Muukkonen is Professor in Educational Psychology at the University of Oulu, Finland. Her research areas include collaborative learning, knowledge creation, learning analytics and methodological development. To study learning processes and pedagogical design, she has been involved in and lead several large scale international educational technology development projects carried out in multidisciplinary collaboration.

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Correspondence to Satu Kaleva .

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This research was approved by the City of Oulu Department of Education and Culture. All students in this study provided consent prior to study participation.

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Kaleva, S., Pursiainen, J., Hakola, M. et al. Students’ reasons for STEM choices and the relationship of mathematics choice to university admission. IJ STEM Ed 6 , 43 (2019). https://doi.org/10.1186/s40594-019-0196-x

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Home » 500+ Quantitative Research Titles and Topics

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

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Diamond glitter: A play of colors with artificial DNA crystals

Using DNA origami, LMU researchers have built a diamond lattice with a periodicity of hundreds of nanometers -- a new approach for manufacturing semiconductors for visible light.

The shimmering of butterfly wings in bright colors does not emerge from pigments. Rather, it is photonic crystals that are responsible for the play of colors. Their periodic nanostructure allows light at certain wavelengths to pass through while reflecting other wavelengths. This causes the wing scales, which are in fact transparent, to appear so magnificently colored. For research teams, the manufacture of artificial photonic crystals for visible light wavelengths has been a major challenge and motivation ever since they were predicted by theorists more than 35 years ago. "Photonic crystals have a versatile range of applications. They have been employed to develop more efficient solar cells, innovative optical waveguides, and materials for quantum communication. However, they have been very laborious to manufacture," explains Dr. Gregor Posnjak. The physicist is a postdoc in the research group of LMU Professor Tim Liedl, whose work is funded by the "e-conversion" Cluster of Excellence and the European Research Council. Using DNA nanotechnology, the team has developed a new approach for the manufacture of photonic crystals. Their results have now been published in the journal Science .

Diamond structure out of strands of DNA

In contrast to lithographic techniques, the LMU team uses a method called DNA origami to design and synthesize building blocks, which then self-assemble into a specific lattice structure. "It's long been known that the diamond lattice theoretically has an optimal geometry for photonic crystals. In diamonds, each carbon atom is bonded to four other carbon atoms. Our challenge consisted in enlarging the structure of a diamond crystal by a factor of 500, so that the spaces between the building blocks correspond with the wavelength of light," explains Tim Liedl. "We increased the periodicity of the lattice to 170 nanometers by replacing the individual atoms with larger building blocks -- in our case, through DNA origami," says Posnjak.

The perfect molecule folding technique

What sounds like magic is actually a specialty of the Liedl group, one of the world's leading research teams in DNA origami and self-assembly. For this purpose, the scientists use a long, ring-shaped DNA strand (consisting of around 8,000 bases) and a set of 200 short DNA staples. "The latter control the folding of the longer DNA strand into virtually any shape at all -- akin to origami masters, who fold pieces of paper into intricate objects. As such, the clamps are a means of determining how the DNA origami objects combine to form the desired diamond lattice," says the LMU postdoctoral researcher. The DNA origami building blocks form crystals of approximately ten micrometers in size, which are deposited on a substrate and then passed on to a cooperating research group from the Walter Schottky Institute at the Technical University of Munich (TUM): The team led by Professor Ian Sharp (also funded by the "e-conversion" Cluster of Excellence) is able to deposit individual atomic layers of titanium dioxide on all surfaces of the DNA origami crystals. "The DNA origami diamond lattice serves as scaffolding for titanium dioxide, which, on account of its high index of refraction, determines the photonic properties of the lattice. After coating, our photonic crystal does not allow UV light with a wavelength of about 300 nanometers to pass through, but rather reflects it," explains Posnjak. The wavelength of the reflected light can be controlled via the thickness of the titanium dioxide layer.

DNA origami could boost photonics

For photonic crystals that work in the infrared range, classic lithographic techniques are suitable but laborious and expensive. In the wavelength range of visible and UV light, lithographic methods have not been successful to date. "Consequently, the comparatively easy manufacturing process using the self-assembly of DNA origami in an aqueous solution offers a powerful alternative for producing structures in the desired size cost-effectively and in larger quantities," says Prof. Tim Liedl. He is convinced that the unique structure with its large pores, which are chemically addressable, will stimulate further research -- for example, in the domain of energy harvesting and storage. In the same issue of Science , a collaboration led by prof. Petr Šulc of Arizona State University and TUM presents a theoretical framework for designing diverse crystalline lattices from patchy colloids, and experimentally demonstrates the method by utilizing DNA origami building blocks to form a pyrochlore lattice, which potentially also could be used for photonic applications.

• Organic Chemistry
• Computational Biology
• Mathematics
• Artificial Intelligence
• Communications
• Ultraviolet
• Electromagnetic spectrum
• Circuit design
• Gross domestic product
• Nanotechnology

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Materials provided by Ludwig-Maximilians-Universität München . Note: Content may be edited for style and length.

Related Multimedia :

• Diamond crystals made from DNA

Journal Reference :

• Gregor Posnjak, Xin Yin, Paul Butler, Oliver Bienek, Mihir Dass, Seungwoo Lee, Ian D. Sharp, Tim Liedl. Diamond-lattice photonic crystals assembled from DNA origami . Science , 2024; 384 (6697): 781 DOI: 10.1126/science.adl2733

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