Effective Strategies for Learning Solving Well-Structured Problems in STEM Education: A Systematic Literature Review
Year: 2026
Type: review
Abstract: Problem-solving is central to STEM in higher education, yet evidence on effective learning strategies for problem-solving remains fragmented across cognitive psychology and discipline-specific research. This systematic literature review, conducted following the PRISMA framework, identifies, categorizes, and analyzes empirical evidence for the learning of well-structured problems in STEM higher education. The strategies reviewed build on two established frameworks of desirably difficult and generative learning strategies. Across three databases (ERIC, PsycINFO, Web of Science), supplemented by citation tracking and screening with ASReview of over 11,000 records, 34 peer-reviewed empirical studies were retained. This review addresses which strategies improve problem-solving in STEM higher education, and how student characteristics, teacher support, and implementation factors shape their effectiveness; data were extracted thematically across these four dimensions. Results show that distributed practice, drawing, enacting, self-explanation, and testing improved problem-solving across STEM contexts. The evidence for interleaving and concept mapping was less consistent. The size and reliability of effects depend on how each strategy was implemented. Effects were strongest when students actively generated content and when problem-solving was measured on delayed assessments. Particularly for testing, distributed practice, and interleaving, effects were also larger when interventions were sustained over time and structurally embedded in courses, though this pattern was not equally evident across all strategies. The review highlights the need for broader coverage of disciplines and student populations. Evidence for teacher’s support role is not present in any included study.
Source: Education Sciences
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7222417440