Why active learning works in STEM
Teaching courses in Science, Technology, Engineering and Mathematics subjects is demanding work. Concepts are abstract, problems are complex, and students quickly hit the limits of their understanding. Lecturers often note that in difficult courses, students are studying mainly to pass the exam, rather than developing a scientific way of thinking. Active learning helps you and your students tackle that challenge.
Below, you can learn more about how students process information, how active learning fits that process, and what the research shows about impact in STEM teaching.
Evidence shows that active learning works, but why does it work so well, also in STEM?
In a nutshell: When you let students think, discuss and solve problems during class, you utilize how the brain actually processes information. Research shows that active learning leads to better long-term retention and deeper understanding of the study material, stronger transfer of knowledge to new contexts, improved self-regulated learning, and greater engagement and collaboration.
In the video on the left, a quick overview is given of how active learning utilizes cognitive load theory in STEM teaching.
Further below, we explain how the brain processes information during learning, how active learning fits that process, and what the evidence tells about active learning in STEM teaching.
How students process information during learning in STEM
When students learn, their brains are constantly processing new information, prior knowledge, and the demands of the task in front of them. The first video, on active learning and how the brain processes information, explains this in more detail.
The key point for STEM is that students have limited “mental bandwidth” at any given moment. When a task is too complex or an explanation too dense, that bandwidth is quickly overloaded. Cognitive load theory explains how that works in the brain. It distinguishes between intrinsic load (the inherent complexity of the content), extraneous load (the load added by confusing explanations or materials, or distractions) and germane load (the effort needed to make sense of ideas and building mental models). Each type of load is explained in the video on the right.
STEM subjects are naturally high in intrinsic load
STEM subjects ask students to coordinate several ideas at once, requiring high working memory activity. If, on top of that, the teaching materials are unclear or disorganised, extraneous load rises and students’ working memory gets overloaded. Research in STEM education shows that higher levels of extraneous load are associated with lower grades, while effort invested in germane load is related to academic success.
Students who use deeper learning strategies such as metacognition (monitoring their own understanding) and elaboration (connecting ideas), achieve better results than those relying mainly on repetition. In other words, learning in STEM is not just about “trying harder”; it is about productively managing mental resources.
How active learning fits the way students learn
Active learning refers to any approach where students are actively doing something with the material during class. And that aligns well with how students process information.
Cognitive load
To start, well-designed active learning reduces extraneous load and increases germane load. Instead of following a long, uninterrupted explanation, students are guided through smaller steps like applying a concept, checking their understanding, or comparing reasoning with a neighbor. Such structure helps them focus and spend their mental energy on sense-making rather than on decoding slides or trying to keep track. Students who engage in metacognitive and elaborative activities report lower extraneous load and higher germane load, and they tend to perform better.
Strengthen students’ learning processes and outcomes
Active learning also naturally builds in effective learning strategies for students. When they answer a conceptual question, explain a solution to a peer, or reflect on an error, they are practicing retrieval, elaboration and metacognitive monitoring. These are linked to higher achievement and deeper learning. Instead of hoping students will do this on their own after class, active learning methods directly take it into class, with a lecturer to guide them.
Furthermore, active methods are particularly good at surfacing and correcting misconceptions. Hidden misunderstandings quickly become visible and can be addressed when students have to explain their reasoning, compare answers with peers, or tackle conceptual questions. This means students are not just getting better at routine exercises, they also reshape and deepen their underlying understanding of the subject.
Last, not all students learn in the same way. The use of active learning methods offers students multiple, and different opportunities to work with the course content, thereby catering to a broad range of learning preferences.
Scientific example: Active learning in physics at Stanford University
In this video below, Nobel prize winner Carl Wieman explains why and how he set-out to apply active learning in his physics course at Stanford University, using scientific methods, and why the results were so strikingly positive.
You can find other interesting video’s on large physics courses made active under ‘Resources‘.
Research and evidence
The most comprehensive overview of active learning in undergraduate STEM comes from a meta-analysis by Freeman and colleagues, covering 225 studies. They compared traditional lecturing with courses that used active learning in some form, across STEM fields. On average, students in active classes scored 0.47 standard deviations higher on exams and concept inventories; that calculates to a difference of roughly six percentage points.
Even more striking is that students in lecture-only based courses were one and a half (1,5) times more likely to fail than students in courses with some form of active learning. These results were found across disciplines and across class sizes, with the largest gains in classes of 50 students or fewer, but still clear benefits in larger groups.
A separate literature review by Rahman and colleagues looked at 34 studies and found consistent evidence that active methods are associated not only with improved academic performance, understanding and critical thinking, but also with higher motivation, self-confidence and positive learning experiences. Students in active classrooms participate and collaborate more, and develop better communication skills.
One common pattern is that lecturers often start by replacing a small portion of lecture time with targeted activities, and then gradually extend this as they see improvements in student engagement and performance. There is no need to completely redesign and restructure everything; active teaching is a spectrum that can build upon what’s already there, and you can grow as you go.
Aji, C. A., & Khan, M. J. (2019). The Impact of Active Learning on Students’ Academic Performance. Open Journal of Social Sciences, 7(3), 204-211. https://doi.org/10.4236/jss.2019.73017
Apkarian, N., Henderson, C., Stains, M., Raker, J., Johnson, E., & Dancy, M. (2021). What really impacts the use of active learning in undergraduate STEM education? Results from a national survey of chemistry, mathematics, and physics instructors. PLOS ONE, 16(2), e0247544. https://doi.org/10.1371/journal.pone.0247544
Atkinson, R. C. (1968). Human Memory: A Proposed System and its Control Processes. In Psychology of Learning and Motivation (Vol. 2, pp. 89-195). Academic Press. https://doi.org/10.1016/S0079-7421(08)60422-3
Bonwell, C. C., & Eison, J. A. (1991). Active Learning: Creating Excitement in the Classroom. 1991 ASHE-ERIC Higher Education Reports. ERIC Clearinghouse on Higher Education, The George Washington University, One Dupont Circle, Suite 630, Washington, DC 20036-1183 ($17. https://eric.ed.gov/?id=ED336049
Brame, C. J. (Red.). (2019). Science Teaching Essentials. Academic Press. https://doi.org/10.1016/B978-0-12-814702-3.00014-7
Capone, R. (2022). Blended Learning and Student-centered Active Learning Environment: A Case Study with STEM Undergraduate Students. Canadian Journal of Science, Mathematics and Technology Education, 22(1), 210-236. https://doi.org/10.1007/s42330-022-00195-5
de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105-134. https://doi.org/10.1007/s11251-009-9110-0
Deslauriers, L., Schelew, E., & Wieman, C. (2011). Improved Learning in a Large-Enrollment Physics Class. Science, 332(6031), 862-864. https://doi.org/10.1126/science.1201783
Evaluation, C. for E. S. and. (2026, februari 4). Cognitive load theory: Research that teachers really need to understand. NSW Department of Education. https://education.nsw.gov.au/about-us/education-data-and-research/cese/publications/literature-reviews/cognitive-load-theory.html
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
Gierczyk, M., Karwowski, M., Paas, F., & H. Tai, R. (2025). STEM Workshop Learning: Content Load Effects on Cognitive, Interaction, and Emotional Outcomes. The Journal of Experimental Education, 0(0), 1-22. https://doi.org/10.1080/00220973.2025.2513254
Human Memory: A Proposed System and its Control Processes. (1968). In Psychology of Learning and Motivation (Vol. 2, pp. 89-195). Academic Press. https://doi.org/10.1016/S0079-7421(08)60422-3
Paas, F., & Ayres, P. (2014). Cognitive Load Theory: A Broader View on the Role of Memory in Learning and Education. Educational Psychology Review, 26(2), 191-195. https://doi.org/10.1007/s10648-014-9263-5
Rahman, A. A., Sahid, S., & Mohamad Nasri, N. (2022). Literature review on the benefits and challenges of active learning on students’ achievement. Cypriot Journal of Educational Sciences, 17(12). https://doi.org/10.18844/cjes.v17i12.8133
Singh, J. (2025). Correlation between cognitive load, learning strategied, and academis succes in STEM subjects. International Journal for Research Publication and Seminar, 16, 35-44. https://doi.org/10.36676/jrps.v16.i4.319
- Check this practical handout guide how to apply Cognitive Load Theory to your course design by the Medical College of Wisconsin
- This video shows a presentation of the strategy, results, and story how a team of lecturers activated a physics course with very successful results: Assessment to Improve Student Learning: Transforming a Large Physics Course at Harvard
In conclusion
STEM students learn best when teaching recognises the limits of their mental bandwidth, helps them direct effort toward taking the correct thinking steps, and building understanding. They learn best when they are actively involved in making sense of the material. Active learning is not a trick; it is teaching that is aligned with how the brain processes complex information and how learning happens in complex domains.
I want to start using active teaching methods, where do I start?
If the information above has convinced you to start – or experiment – with active learning methods, use this STEM Active Learning Guide to get started! Browse motivating best practice stories from peer lecturers, find ample examples and templates of activities to apply in the classroom in the STEM activity finder, and get practical tips on how to include your peers, students, your TAs, and yourself in active teaching methods that fit your personal teaching style in the ‘Get on board‘ section.
You can also reach out to TLC Science for personalized advice, brainstorming sessions, co-creation sessions, support with lesson- or course plans that include active learning, tailored workshops or inspirations sessions for you and your colleagues, and more. Explore your options in ‘Learn and Co-create‘.