Skip to main content
Log in

What students learn in problem-based learning: a process analysis

  • Published:
Instructional Science Aims and scope Submit manuscript

Abstract

This study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine students for an entire PBL cycle were recorded. The relevant concepts articulated and studied individually while working on the problem-at-hand were identified as units of analysis and counted to demonstrate the growth in concepts acquired over the PBL cycle. We identified two distinct phases in the process—an initial concept articulation, and a later concept repetition phase. To overcome the sample-size limitations of the first study, we analyzed the verbal interactions of, and resources studied, by another 35 students in an entire PBL cycle using structural equation modeling. Results show that students’ verbal contributions during the problem analysis phase strongly influenced their verbal contributions during self-directed learning and reporting phases. Verbal contributions and individual study influenced similarly the contributions during the reporting phase. Increased verbalizations of concepts during the reporting phase also led to higher achievement. We found that collaborative learning is significant in the PBL process, and may be more important than individual study in determining students’ achievement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Arbuckle, J. L. (2006). Amos 7.0 user’s guide. Chicago: SPSS.

    Google Scholar 

  • Barrows, H. S. (1986). A taxonomy of problem-based learning methods. Medical Education, 20, 481–486.

    Article  Google Scholar 

  • Barrows, H. S. (1988). The tutorial process. Springfield, IL: Southern Illinois University School of Medicine.

    Google Scholar 

  • Barrows, H. S. (1996). Problem-based learning in medicine and beyond: A brief overview. In L. Wilkerson & W. H. Gijselaers (Eds.), New directions for teaching and learning (Vol. 68, pp. 3–11). San Francisco: Jossey-Bass Publishers.

    Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.

    Google Scholar 

  • Capon, N., & Kuhn, D. (2004). What’s so good about problem-based learning? Cognition and Instruction, 22(1), 61–79.

    Article  Google Scholar 

  • Chi, M. T. H., Deleeuw, N., Chiu, M. H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.

    Google Scholar 

  • Cobb, P. (1994). Where is the mind? Constructivist and sociocultural perspectives on mathematical development. Educational Researcher, 23, 13–20.

    Google Scholar 

  • De Grave, W. S., Boshuizen, H. P. A., & Schmidt, H. G. (1996). Problem based learning: Cognitive and metacognitive processes during problem analysis. Instructional Science, 24(5), 321–341.

    Article  Google Scholar 

  • De Grave, W. S., Schmidt, H. G., & Boshuizen, H. P. A. (2001). Effects of problem-based discussion on studying a subsequent text: A randomized trial among first year medical students. Instructional Science, 29(1), 33–44.

    Article  Google Scholar 

  • Dochy, F., Segers, M., & Buehl, M. M. (1999). The relation between assessment practices and outcomes of studies: The case of research on prior knowledge. Review of Educational Research, 69(2), 42.

    Google Scholar 

  • Dochy, F., Segers, M., Van den Bossche, P., & Gijbels, D. (2003). Effects of problem-based learning: A meta-analysis. Learning and Instruction, 13(5), 533–568.

    Article  Google Scholar 

  • Dolmans, D., & Schmidt, H. G. (2006). What do we know about cognitive and motivational effects of small group tutorials in problem-based learning? Advances in Health Sciences Education, 11(4), 321–336.

    Article  Google Scholar 

  • Dolmans, D., Schmidt, H. G., & Gijselaers, W. H. (1995). The relationship between student-generated learning issues and self-study in problem-based learning. Instructional Science, 22(4), 251–267.

    Article  Google Scholar 

  • Driver, R., Asoko, H., Leach, J., Mortimer, E., & Scott, P. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23, 5–12.

    Google Scholar 

  • Gallagher, S. A., Stepien, W. J., & Rosenthal, H. (1992). The effects of problem-based learning on problem solving. Gifted Child Quarterly, 36, 195–200.

    Article  Google Scholar 

  • Glaser, R., & Bassok, M. (1989). Learning theory and the study of instruction. Annual Review of Psychology, 40, 631–666.

    Article  Google Scholar 

  • Glenn, P. J., Koschmann, T., & Conlee, M. (1999). Theory presentation and assessment in a problem-based learning group. Discourse Processes, 27(2), 119–133.

    Article  Google Scholar 

  • Hak, T., & Maguire, P. (2000). Group process: The black box of studies on problem-based learning. Academic Medicine, 75(7), 769–772.

    Article  Google Scholar 

  • Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., & Barrows, H. S. (2008). Facilitating collaborative knowledge building. Cognition and Instruction, 26, 48–94.

    Article  Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

    Article  Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8 user’s reference guide. Chicago: Scientific Software International.

    Google Scholar 

  • Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., et al. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by Design™ into practice. Journal of the Learning Sciences, 12(4), 495–547.

    Article  Google Scholar 

  • Koschmann, T., Glenn, P., & Conlee, M. (1997). Analyzing the emergence of a learning issue in a problem-based learning meeting. Medical Education Online, 2, 1–9.

    Google Scholar 

  • MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–226.

    Article  Google Scholar 

  • Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289–316.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Novak, J. D. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools for schools and corporations. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning. Annual Review of Psychology, 49, 345–375.

    Article  Google Scholar 

  • Rivard, L. P., & Straw, S. B. (2000). The effect of talk and writing on learning science: An exploratory study. Science Education, 84(5), 566–593.

    Article  Google Scholar 

  • Schmidt, H. G. (1983). Problem-based learning: Rationale and description. Medical Education, 17(1), 11–16.

    Article  Google Scholar 

  • Schmidt, H. G. (1993). Foundations of problem-based learning—some explanatory notes. Medical Education, 27(5), 422–432.

    Article  Google Scholar 

  • Schmidt, H. G., De Volder, M. L., De Grave, W. S., Moust, J. H. C., & Patel, V. L. (1989). Explanatory models in the processing of science text: The role of prior knowledge activation through small-group discussion. Journal of Educational Psychology, 81(4), 610–619.

    Article  Google Scholar 

  • Schmidt, H. G., Loyens, S. M. M., Van Gog, T., & Paas, F. (2007). Problem-based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 91–97.

    Article  Google Scholar 

  • Schmidt, H. G., & Moust, J. H. C. (2000). Factors affecting small-group tutorial learning: A review of research. In D. H. Evensen & C. E. Hmelo-Silver (Eds.), Problem-based learning: A research perspective on learning interactions (pp. 19–52). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Schmidt, H. G., Van der Molen, H. T., Te Winkel, W. W. R., & Wijnen, W. H. F. W. (2009). Constructivist, problem-based, learning does work: A meta-analysis of curricular comparisons involving a single medical school. Educational Psychologist, 44, 227–249.

    Article  Google Scholar 

  • Van den Hurk, M. M., Dolmans, D., Wolfhagen, I., & Van der Vleuten, C. P. M. (2001). Testing a causal model for learning in a problem-based curriculum. Advances in Health Sciences Education, 6(2), 141–149.

    Article  Google Scholar 

  • Van den Hurk, M. M., Wolfhagen, I., Dolmans, D., & Van der Vleuten, C. P. M. (1999). The impact of student-generated learning issues on individual study time and academic achievement. Medical Education, 33(11), 808–814.

    Article  Google Scholar 

  • Visschers-Pleijers, A. J., Dolmans, D., de Leng, B. A., Wolfhagen, I. H., & Van der Vleuten, C. P. (2006). Analysis of verbal interactions in tutorial groups: A process study. Medical Education, 40(2), 129–137.

    Article  Google Scholar 

  • Yew, E. H. J., & Schmidt, H. G. (2008). Evidence for constructive, self-regulatory, and collaborative processes in problem-based learning. Advances in Health Sciences Education, 14(2), 251–273.

    Article  Google Scholar 

  • Zimmerman, B. J. (1990). Self-regulated learning and academic-achievement—an overview. Educational Psychologist, 25(1), 3–17.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elaine H. J. Yew.

Appendix

Appendix

Basic Science problem that students worked on for the day

Code of Life

I am the family face;

Flesh perishes, I live on,

Projecting trait and trace

Through time to times anon,

And leaping from place to place

Over oblivion.

From “Heredity” by Thomas Hardy

(First published in Moments of Vision and Miscellaneous Verses, Macmillan, 1917)

The idea of the gene came first. The gene is the thing that carries information about the living organism. The gene tells if one’s hair is black and eyes are blue. The gene tells if one can curl one’s tongue. The gene carries the ‘family face’ that goes ‘through time to times anon’ from mother to daughter, father to son, or the other ways across, over time.

Is the gene a substance you can find in your body, or a kind of a soul-like invisible thing?

Explore the concept of a gene and the role it plays in an organism. Is it possible that the gene is represented by an identifiable molecule, one that is able to carry information akin to a line of code, giving it the ability to execute highly detailed tasks? Determine the qualities such a molecule should have.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yew, E.H.J., Schmidt, H.G. What students learn in problem-based learning: a process analysis. Instr Sci 40, 371–395 (2012). https://doi.org/10.1007/s11251-011-9181-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-011-9181-6

Keywords

Navigation