Exploring the Relationship between Problem Solving and Metacognition in Problem-Based Learning: A Case Study with Secondary School Students
Abstract:
This study investigated the effects of problem-based learning (PBL) on secondary students’ metacognitive regulation and examined its relationship with problem-solving processes. Despite extensive research on PBL, limited empirical evidence exists on how it fosters metacognitive regulation at the secondary level and how these processes interact with the problem-solving stages.
A quasi-experimental pretest–posttest design was conducted with 128 Grade 10 students assigned to a PBL group and a traditional instruction group for comparison. Metacognitive skills were measured using an adapted Metacognitive Awareness Inventory (MAI) questionnaire. Data were analyzed using analysis of covariance (ANCOVA), Pearson correlations, and confirmatory factor analysis (CFA).
The results indicate that PBL significantly improves students’ metacognitive regulation after controlling for baseline differences, with moderate-to-large effect sizes. Correlation and regression analyses revealed consistent associations between metacognitive processes and the corresponding problem-solving stages, particularly planning, monitoring, and evaluation. The CFA results confirmed the validity of the measurement model.
These findings suggest that metacognitive regulation is a key mechanism through which PBL enhances problem-solving performance, highlighting the importance of integrating explicit metacognitive scaffolding into secondary science education.
KeyWords:
problem-based learning; metacognition; problem solving; secondary education; CFA
References:
- Barrows, H. S. (1986). Taxonomy of problem-based learning methods. Medical Education, 20(6), 481–486. https://doi.org/10.1111/j.1365-2923.1986.tb01386.x
- Barrows, H. S., & Tamblyn, R. M. (1980). Problem-based Learning: An Approach to Medical Education. Springer.
- Bezanilla, M. J., Fernández-Nogueira, D., Poblete, M., & Galindo-D, D. (2021). Methodologies for critical teaching thinking: The view of higher education students. Educational Sciences, 11(2), 56. https://doi.org/10.3390/educsci11020056
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed). . Lawrence Erlbaum.
- Creswell, J. W., & Plano Clark, V. (2017). Designing and conducting mixed-methods research (3rd ed.). Sage Publications.
- Davidson, J. E., & Sternberg, R. J. (1998). Smart problem solving: How metacognition helps. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in Educational Theory and Practice (pp. 47–68). Lawrence Erlbaum.
- Dignath, C., & Büttner, G. (2018). Teachers' direct and indirect promotion of self-regulated learning in primary and secondary mathematics classes: Insights from video-based classroom observations and teacher interviews. Metacognition and Learning, 13(2), 127–157. https://doi.org/10.1007/s11409-018-9181-x
- Efklides, A. (2008). Metacognition: Defining aspects and levels of functioning in relation to self-regulation and coregulation. European Psychologist, 13(4), 277–287. https://doi.org/10.1027/1016-9040.13.4.277
- Flavell, J. H. (1976). The nature of intelligence (pp. 231–236) In L. B. Resnick (Ed.) The Nature of Intelligence (pp. 231–236). Erlbaum.
- Fornell, C. & Larcker, D. F. (1981). Evaluation of structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18(1), 39–50.
- Harrison, G. M., & Vallin, L. M. (2018). Evaluating the Metacognitive Awareness Inventory: A critical analysis of key issues. Learning and Instruction, 55, 56–66. https://doi.org/10.1016/j.learninstruc.2017.09.001
- Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
- Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
- Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/BF02300500
- Lonka, K. (2018). Innovative schools: Teaching and learning in the digital era. Tuomo Lehtonen.
- Loyens, S. M. M., Magda, J., & Rikers, R. M. J. P. (2008). Self-directed learning in problem-based learning and its relationship with self-regulated learning. Educational Psychology Review, 20(4), 411–427. https://doi.org/10.1007/s10648-008-9082-7
- Masek, A. & Yamin, S. (2010). The effect of problem-based learning on critical thinking ability: A theoretical and empirical review. International Review of Social Sciences and Humanities, 2(1), 215–221.
- Nguyen, L. C., Hoa, H. Q., & Hien, L. H. P. (2025). Integrating design thinking into STEM education: Enhancing the problem-solving skills of high school students. EURASIA Journal of Mathematics, Science and Technology Education, 21(4), Article em2611. https://doi.org/10.29333/ejmste/16084
- Norman, G. R., & Schmidt, H. G. (1992). The psychological basis of problem-based learning: A review of the evidence. Academic Medicine, 67(9), 557–565. https://doi.org/10.1097/00001888-199209000-00002
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). . McGraw-Hill.
- OECD. (2018). PISA 2018 Assessment and Analytical Framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en
- Okoli, C. & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations, and applications. Information & Management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002
- Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x
- Polya, G. (1945). How to solve it: A new aspect of the mathematical method. Princeton University Press: Princeton University Press
- Savery, J. R. (2006). Overview of problem-based learning: Definitions and distinctions. Interdisciplinary Journal of Problem-Based Learning, 1(1), 9–20. https://doi.org/10.7771/1541-5015.1002
- Schmidt, H. G. (1993). Foundations of problem-based learning: Some explanatory notes. Medical Education, 27(5), 422–432. https://doi.org/10.1111/j.1365-2923.1993.tb00296.x
- Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033
- Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351–371. https://doi.org/10.1007/BF02212307
- Allie Van Barneveld and John Strobel (2009). When is PBL more effective? A meta-synthesis of meta-analyses comparing PBL to conventional classroom learning. Interdisciplinary Journal of Problem-Based Learning, 3(1), 44–58. https://doi.org/10.7771/1541-5015.1046
- Sungur, S., & Tekkaya, C. (2006). Effects of problem-based learning and traditional instruction on self-regulated learning. The Journal of Educational Research, 99(5), 307–320. https://doi.org/10.3200/JOER.99.5.307-320
- Tan, O. S. (2003). Problem-based learning innovation: Using problems to power learning in the 21st century. Thomson Learning.
- Veenman, M. V. J. (2012). Metacognition in science education: Definitions, constituents, and their intricate relationship with cognition. In B. J. Fraser, K. Tobin, & C. McRobbie (Eds.) The Second International Handbook of Science Education (pp. 21–30). Springer. https://doi.org/10.1007/978-1-4020-9041-7_2
- Veenman, M. V. J., Van Hout Wolters, B. H., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0
- Woods, D. R. (2000). Problem-based Learning: How to Gain the Most from PBL Waterdown, Ontario, Canada: Donald R. Woods.
- Yin, R. K. (2014). Case study research: Design and methods (5th ed.). . Sage.
- Zohar, A., & Barzilai, S. (2013). Review of research on metacognition in science education: Current and future directions. Studies in Science Education, 49(2), 121–169. https://doi.org/10.1080/03057267.2013.847261
- Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
- Bezanilla, M. J., Fernández-Nogueira, D., Poblete, M., & Galindo-D (2021). Methodologies for critical teaching thinking: The view of higher education students. Educational Sciences, 11 (2), 56. https://doi.org/10.3390/educsci11020056
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.).. Lawrence Erlbaum.
- Creswell, J.W., & Plano Clark, V. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
- Davidson, J.E., & Sternberg, RJ (1998). Smart problem solving: How metacognition helps. In DJ Hacker, J. Dunlosky, & AC Graesser (Eds.), Metacognition in Educational Theory and Practice (pp. 47–68). Lawrence Erlbaum.
- D. R. Woods, Problem-based Learning: How to Gain the Most from PBL? Waterdown, Ontario, Donald R. Woods, 2000.
- Dignath , C., & Büttner, G. (2018). Teachers' direct and indirect promotion of self-regulated learning in primary and secondary mathematics classes: Insights from video-based classroom observations and teacher interviews. Metacognition and Learning, 13 (2), 127–157. https://doi.org/10.1007/s11409-018-9181-x
- Efklides , A. (2008). Metacognition: Defining aspects and levels of functioning in relation to self-regulation and coregulation. European Psychologist, 13 (4), 277–287. https://doi.org/10.1027/1016-9040.13.4.277
- Flavell, J. H. (1976). Metacognitive aspects of problem solving In L.B. Resnick (Ed.) The Nature of Intelligence (p.. 231–236). Erlbaum.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement errors.Journal of Marketing Research, 18 (1), 39–50.
- Harrison, G. M., & Vallin, L. M (2018). Evaluating the Metacognitive Awareness Inventory: A Critical Analysis of Key Issues. Learning and Instruction, 55, 56–66. https://doi.org/10.1016/j.learninstruc.2017.09.001
- Hmelo -Silver, C.E. (2004). Educational Psychology Review 16 (3), 235–266. Educational Psychology Review 16 (3), 235–2 6. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
- H. G. Schmidt, "Foundations of problem‐based learning: some explanatory notes," Medical Education, vol. 27, no. 5, pp. 422- 432, 1993. https://doi.org/10.1111/j.1365-2923.1993.tb00296.x
- H. S. Barrows, "A taxonomy of problem-based learning methods," Medical Education, vol. 20, no. 6, pp. 481–486, 1986. https://doi.org/10.1111/j.1365-2923.1986.tb01386.x.
- Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6 (1), 1–55.
- Jonassen, D.H. (2000). Toward a design theory of problem-solving. Educational Technology Research and Development, 48 (4), 63–85. https://doi.org/10.1007/BF02300500
- K. N. L. Wee, 2004, "Foundations of problem‐based learning: some explanatory notes," Medical Education, vol. 27, no. 5, pp. 422- 432, 1993. https://doi.org/10.1111/j.1365-2923.1993.tb00296.x
- K. Lonka, Innovative schools: Teaching and learning in the digital era. Helsinki, Finland: Tuomo Lehtonen, 2018.
- Loyens, S.M.M., Magda, J., & Rikers, RMJP (2008). Self-directed learning in problem-based learning and its relationship with self-regulated learning. Educational Psychology Review, 20 (4), 411–427. https://doi.org/10.1007/s10648-008-9082-7
- Nguyen, LC, Hoa, HQ, & Hien, LHP (2025). Integrating design thinking into STEM education: Enhancing the problem-solving skills of high school students. EURASIA Journal of Mathematics, Science, and Technology Education, 21 (4), em2611. DOI: 10.29333/ejmste/16084
- Norman, G.R. & Schmidt, H.G. (1992). The psychological basis of problem-based learning: A review of the evidence. Academic Medicine, 67 (9), 557–565. https://doi.org/10.1097/00001888-199209000-00002
- Nunnally, J.C., & Bernstein, I.H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
- OECD. (2018). PISA 2018 Assessment and Analytical Framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en
- Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: An example, design considerations, and applications. Information & Management, 42 (1), 15–29. https://doi.org/10.1016/j.im.2003.11.002
- Pintrich , P.R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16 (4), 385–407. https://doi.org/10.1007/s10648-004-0006-x
- Polya, G. (1945). How to solve it: A new aspect of the mathematical method. Princeton University Press: Princeton University Press
- Savery, J.R. (2006). Interdisciplinary Journal of Problem-Based Learning, 1 (1), 9–20. Interdisciplinary Journal of Problem-Based Learning, 1 (1), 9–20. https://doi.org/10.7771/1541-5015.1002
- Schraw, G., & Dennison, R. S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19 (4), 460–475. https://doi.org/10.1006/ceps.1994.1033
- Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7 (4), 351–371. https://doi.org/10.1007/BF02212307
- Allie Van Barneveld and John Strobel (2009). When is PBL more effective? A meta-synthesis of meta-analyses comparing PBL to conventional classroom learning Interdisciplinary Journal of Problem-Based Learning, 3 (1), 44–58. https://doi.org/10.7771/1541-5015.1046
- Sungur, S., & Tekkaya, C. (2006). Effects of problem-based learning and traditional instruction on self-regulated learning. The Journal of Educational Research, 99 (5), 307–320. https://doi.org/10.3200/JOER.99.5.307-320
- Tan, O.S. (2003). Problem-based learning innovation: Using problems to power learning in the 21st century. Thomson Learning.
- Veenman, M. V. J. (2012). Metacognition in science education: Definitions, constituents, and their intricate relationship with cognition. In BJ Fraser, K. Tobin and C. McRobbie (Eds.), Second International Handbook of Science Education (pp. 21–30). Springer. https://doi.org/10.1007/978-1-4020-9041-7_2
- Veenman, MVJ, Van Hout-Wolters, B.H., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1 (1), 3–14. https://doi.org/10.1007/s11409-006-6893-0
- Yin, R.K. (2014). Case study research: Design and methods (5th ed.). . Sage.
- Zohar, A., & Barzilai, S. (2013). Review of research on metacognition in science education: Current and future directions. Studies in Science Education, 49 (2), 121–169. https://doi.org/10.1080/03057267.2013.847261
- Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2