Advances in Health Sciences Education

, Volume 20, Issue 4, pp 1033–1052 | Cite as

The role of environmental and individual characteristics in the development of student achievement: a comparison between a traditional and a problem-based-learning curriculum

  • Stefan K. SchauberEmail author
  • Martin Hecht
  • Zineb M. Nouns
  • Adelheid Kuhlmey
  • Susanne Dettmer


In medical education, the effect of the educational environment on student achievement has primarily been investigated in comparisons between traditional and problem-based learning (PBL) curricula. As many of these studies have reached no clear conclusions on the superiority of the PBL approach, the effect of curricular reform on student performance remains an issue. We employed a theoretical framework that integrates antecedents of student achievement from various psychosocial domains to examine how students interact with their curricular environment. In a longitudinal study with N = 1,646 participants, we assessed students in a traditional and a PBL-centered curriculum. The measures administered included students’ perception of the learning environment, self-efficacy beliefs, positive study-related affect, social support, indicators of self-regulated learning, and academic achievement assessed through progress tests. We compared the relations between these characteristics in the two curricular environments. The results are two-fold. First, substantial relations of various psychosocial domains and their associations with achievement were identified. Second, our analyses indicated that there are no substantial differences between traditional and PBL-based curricula concerning the relational structure of psychosocial variables and achievement. Drawing definite conclusions on the role of curricular-level interventions in the development of student’s academic achievement is constrained by the quasi-experimental design as wells as the selection of variables included. However, in the specific context described here, our results may still support the view of student activity as the key ingredient in the acquisition of achievement and performance.


Problem-based learning Curricular comparison Progress test Structural equation modelling Achievement Emotion Development 



The work of Stefan K. Schauber was funded by the German Federal Ministry of Education and Research (BMBF) within the project “Competence Acquisition and Learning Trajectories in Medical Training” (Grant 01JG1055).


  1. Albanese, M. A., & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Academic Medicine, 68(1), 52–81.CrossRefGoogle Scholar
  2. Artino, A. R., Dong, T., DeZee, K. J., Gilliland, W. R., Waechter, D. M., Cruess, D., & Durning, S. J. (2012a). Achievement goal structures and self-regulated learning: relationships and changes in medical school. Academic Medicine, 87(10), 1375–1381.CrossRefGoogle Scholar
  3. Artino, A. R., Holmboe, E. S., & Durning, S. J. (2012b). Control-value theory: Using achievement emotions to improve understanding of motivation, learning, and performance in medical education: AMEE Guide No. 64. Medical Teacher, 34(3), e148–e160.CrossRefGoogle Scholar
  4. Artino, A. R., La Rochelle, J. S., & Durning, S. J. (2010). Second-year medical students’ motivational beliefs, emotions, and achievement. Medical Education, 44(12), 1203–1212.CrossRefGoogle Scholar
  5. Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260.CrossRefGoogle Scholar
  6. Barrows, H. S. (1996). Problem-based learning in medicine and beyond: A brief overview. New Directions for Teaching and Learning, 1996(68), 3–12.CrossRefGoogle Scholar
  7. Bate, E., Hommes, J., Duvivier, R., & Taylor, D. C. M. (2014). Problem-based learning (PBL): Getting the most out of your students—Their roles and responsibilities: AMEE Guide No. 84. Medical Teacher, 36(1), 1–12.CrossRefGoogle Scholar
  8. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4, Version 1.0-5. Accessed 22 Jan 2015.
  9. Bergman, E. M., Prince, K. J., Drukker, J., van der Vleuten, C. P. M., & Scherpbier, A. J. (2008). How much anatomy is enough? Anatomical Sciences Education, 1(4), 184–188.CrossRefGoogle Scholar
  10. Berkson, L. (1993). Problem-based learning: Have the expectations been met? Academic Medicine, 68(10), S79.CrossRefGoogle Scholar
  11. Borenstein, M. (2009). Introduction to meta-analysis. Chichester: Wiley.CrossRefGoogle Scholar
  12. Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Daily variability in working memory is coupled with negative affect: The role of attention and motivation. Emotion, 12(3), 605.CrossRefGoogle Scholar
  13. Burt, D. B., Zembar, M. J., & Niederehe, G. (1995). Depression and memory impairment: a meta-analysis of the association, its pattern, and specificity. Psychological Bulletin, 117(2), 285.CrossRefGoogle Scholar
  14. Cohen, S., & Hoberman, H. M. (1983). Positive events and social supports as buffers of life change stress. Journal of Applied Social Psychology, 13(2), 99–125.CrossRefGoogle Scholar
  15. Cohen-Schotanus, J., Muijtjens, A. M. M., Schönrock-Adema, J., Geertsma, J., & van der Vleuten, C. P. M. (2008). Effects of conventional and problem-based learning on clinical and general competencies and career development. Medical Education, 42(3), 256–265.CrossRefGoogle Scholar
  16. Colliver, J. A. (2000). Effectiveness of problem-based learning curricula: research and theory. Academic Medicine, 75(3), 259–266.CrossRefGoogle Scholar
  17. Dettmer, S., Kuhlmey, A., & Schulz, S. (2010). Karriere- und Lebensplanung: Gehen oder bleiben? Dtsch Arztebl International, 107(1-2), A-30.Google Scholar
  18. Diseth, Å. (2007). Approaches to learning, course experience and examination grade among undergraduate psychology students: testing of mediator effects and construct validity. Studies in Higher Education, 32(3), 373–388.CrossRefGoogle Scholar
  19. Diseth, Å. (2011). Self-efficacy, goal orientations and learning strategies as mediators between preceding and subsequent academic achievement. Learning and Individual Differences, 21(2), 191–195.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Dolmans, D. H. J. M., de Grave, W., Wolfhagen, I. H. A. P., & van der Vleuten, C. P. M. (2005). Problem-based learning: future challenges for educational practice and research. Medical Education, 39(7), 732–741.CrossRefGoogle Scholar
  22. Dolmans, D., & Gijbels, D. (2013). Research on problem-based learning: Future challenges. Medical Education, 47(2), 214–218.CrossRefGoogle Scholar
  23. Drukker, J., Hylkema, N., Prince, K. J. A. H., Scherpbier, A. J. J. A., Van der Vleuten, C. P., & van Mameren, H. (2003). Does problem-based learning lead to deficiencies in basic science knowledge? An empirical case on anatomy. Medical Education, 37(1), 15–21.CrossRefGoogle Scholar
  24. Duvivier, R. J., van Dalen, J., Muijtjens, A. M., Moulaert, V. R., van der Vleuten, C. P. M., & Scherpbier, A. J. (2011). The role of deliberate practice in the acquisition of clinical skills. BMC Medical Education, 11(1), 101.CrossRefGoogle Scholar
  25. Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8(1), 128–141.CrossRefGoogle Scholar
  26. Engel, G. (1977). The need for a new medical model: a challenge for biomedicine. Science, 196(4286), 129–136.CrossRefGoogle Scholar
  27. Farrow, R., & Norman, G. (2003). The effectiveness of PBL: the debate continues. Is meta-analysis helpful? Medical Education, 37(12), 1131–1132.CrossRefGoogle Scholar
  28. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Analytical methods for social research. Cambridge, New York: Cambridge University Press.Google Scholar
  29. Gijbels, D., Segers, M., & Struyf, E. (2008). Constructivist learning environments and the (im)possibility to change students’ perceptions of assessment demands and approaches to learning. Instructional Science, 36, 431–443.CrossRefGoogle Scholar
  30. Hartling, L., Spooner, C., Tjosvold, L., & Oswald, A. (2010). Problem-based learning in pre-clinical medical education: 22 years of outcome research. Medical Teacher, 32(1), 28–35.CrossRefGoogle Scholar
  31. Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.CrossRefGoogle Scholar
  32. Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174–180.CrossRefGoogle Scholar
  33. Hommes, J., Bossche, P., Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study. Advances in Health Sciences Education, 19(4), 541–563.CrossRefGoogle Scholar
  34. Hommes, J., Rienties, B., Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2012). Visualising the invisible: a network approach to reveal the informal social side of student learning. Advances in Health Sciences Education, 17(5), 743–757.CrossRefGoogle Scholar
  35. Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Quantitative methodology series. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  36. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.CrossRefGoogle Scholar
  37. Kaufman, D. M., & Mann, K. V. (1996). Comparing students’ attitudes in problem-based and conventional curricula. Academic Medicine, 71(10), 1096–1099.CrossRefGoogle Scholar
  38. Kiessling, C., Schubert, B., Scheffner, D., & Burger, W. (2004). First year medical students’ perceptions of stress and support: a comparison between reformed and traditional track curricula. Medical Education, 38(5), 504–509.CrossRefGoogle Scholar
  39. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.CrossRefGoogle Scholar
  40. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.Google Scholar
  41. Koh, G. C.-H., Khoo, H. E., Wong, M. L., & Koh, D. (2008). The effects of problem-based learning during medical school on physician competency: a systematic review. Canadian Medical Association Journal, 178(1), 34–41.CrossRefGoogle Scholar
  42. Kulasegaram, K. M., Grierson, L. E. M., & Norman, G. R. (2013). The roles of deliberate practice and innate ability in developing expertise: evidence and implications. Medical Education, 47(10), 979–989.CrossRefGoogle Scholar
  43. Kusurkar, R. A., ten Cate, T. J., Vos, C. M. P., Westers, P., & Croiset, G. (2013). How motivation affects academic performance: a structural equation modelling analysis. Advances in Health Sciences Education, 18(1), 57–69.CrossRefGoogle Scholar
  44. Laireiter, A.-R. (1996). Skalen Soziale Unterstützung.: SSU. Mödling: Dr. Schuhfried.Google Scholar
  45. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed). Wiley series in probability and statistics. Hoboken, N.J: Wiley.Google Scholar
  46. Loyens, S. M. M., Magda, J., & Rikers, R. M. J. P. (2008). Self-directed learning in problem-based learning and its relationships with self-regulated learning. Educational Psychology Review, 20(4), 411–427.CrossRefGoogle Scholar
  47. Mattick, K., & Knight, L. (2007). High-quality learning: Harder to achieve than we think? Medical Education, 41(7), 638–644.CrossRefGoogle Scholar
  48. Maudsley, G. (1999). Do we all mean the same thing by “problem-based learning”? A review of the concepts and a formulation of the ground rules. Academic Medicine, 74(2), 178–185.CrossRefGoogle Scholar
  49. Moulaert, V., Verwijnen, M. G. M., Rikers, R., & Scherpbier, A. J. (2004). The effects of deliberate practice in undergraduate medical education. Medical Education, 38(10), 1044–1052.CrossRefGoogle Scholar
  50. Moust, J. H. C., van Berkel, H. J. M., & Schmidt, H. G. (2005). Signs of erosion: Reflections on three decades of problem-based learning at Maastricht University. Higher Education, 50(4), 665–683.CrossRefGoogle Scholar
  51. Muthén, L., & Muthén, B. (1998–2011). Mplus User’s Guide (6th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  52. Neufeld, V. R., & Barrows, H. S. (1974). The “McMaster Philosophy”: an approach to medical education. Journal of Medical Education, 49(11), 1040–1050.Google Scholar
  53. Neville, A. J., & Norman, G. R. (2007). PBL in the undergraduate MD Program at McMaster University: Three iterations in three decades. Academic Medicine, 82(4), 370–374.CrossRefGoogle Scholar
  54. Newman, M. (2003). A pilot systematic review and meta-analysis on the effectiveness of problem-based learning. On behalf of the Campbell Collaboration Systematic Review Group on the Effectiveness of Problem-based Learning. Newcastle upon Tyne, UK: Learning and Teaching Support Network-01, University of Newcastle. Google Scholar
  55. Nijhuis, J. H., Segers, M. R., & Gijselaers, W. (2005). Influence of redesigning a learning environment on student perceptions and learning strategies. Learning Environments Research, 8(1), 67–93.CrossRefGoogle Scholar
  56. Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. American Psychologist, 67(2), 130–159.CrossRefGoogle Scholar
  57. Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424.CrossRefGoogle Scholar
  58. Norman, G. (2004). Editorial: Beyond PBL. Advances in Health Sciences Education, 9(4), 257–260.CrossRefGoogle Scholar
  59. Norman, G. (2008). Problem-based learning makes a difference. But why? Canadian Medical Association Journal, 178(1), 61–62.CrossRefGoogle Scholar
  60. Norman, G. R., & Schmidt, H. G. (2000). Effectiveness of problem-based learning curricula: theory, practice and paper darts. Medical Education, 34(9), 721–728.CrossRefGoogle Scholar
  61. Nouns, Z. M., & Georg, W. (2010). Progress testing in German speaking countries. Medical Teacher, 32(6), 467–470.CrossRefGoogle Scholar
  62. Nouns, Z., Hanfler, S., Brauns, K., Foeller, T., Fuhrmann, S., Koelbel, S., & Mertens, A. (2004). Do progress tests predict the outcome of national exams? AMEE Conference 2004 Edinburgh. Short Communication 2F3.Google Scholar
  63. Nouns, Z., Schauber, S., Witt, C., Kingreen, H., & Schüttpelz-Brauns, K. (2012). Development of knowledge in basic sciences: a comparison of two medical curricula. Medical Education, 46(12), 1206–1214.CrossRefGoogle Scholar
  64. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341.CrossRefGoogle Scholar
  65. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48.CrossRefGoogle Scholar
  66. Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105.CrossRefGoogle Scholar
  67. Pintrich, P. R., Smith, D. A. F., García, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.CrossRefGoogle Scholar
  68. Preacher, K. J. (2008). Latent growth curve modeling. Thousand Oaks: Sage.Google Scholar
  69. R Core Team. (2013). R: A Language and Environment for Statistical Computing. Vienna, Austria.
  70. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. doi: 10.1037/a0026838.CrossRefGoogle Scholar
  71. Riediger, M., Wrzus, C., Schmiedek, F., Wagner, G. G., & Lindenberger, U. (2011). Is seeking bad mood cognitively demanding? Contra-hedonic orientation and working-memory capacity in everyday life. Emotion, 11(3), 656.CrossRefGoogle Scholar
  72. Rytkönen, H., Parpala, A., Lindblom-Ylänne, S., Virtanen, V., & Postareff, L. (2012). Factors affecting bioscience students’ academic achievement. Instructional Science, 40(2), 241–256.CrossRefGoogle Scholar
  73. Schauber, S. K., Hecht, M., Nouns, Z. M., & Dettmer, S. (2013). On the role of biomedical knowledge in the acquisition of clinical knowledge. Medical Education, 47(12), 1223–1235.CrossRefGoogle Scholar
  74. Schmidmaier, R., Holzer, M., Angstwurm, M., Nouns, Z., Reincke, M., & Fischer, M. R. (2010). Using the Progress Test Medizin (PTM) for evaluation of the Medical Curriculum Munich (MeCuM). GMS Zeitschrift für medizinische Ausbildung, 27(5), 1–14.Google Scholar
  75. Schmidt, H. G. (1983). Problem-based learning: Rationale and description. Medical Education, 17(1), 11–16.CrossRefGoogle Scholar
  76. Schmidt, H. G., Rotgans, J. I., & Yew, E. H. J. (2011). The process of problem-based learning: what works and why. Medical Education, 45(8), 792–806.CrossRefGoogle Scholar
  77. Schmidt, H. G., van der Moldem, H. T., te Winkel, W. W., & Wijen, W. H. (2009). Constructivist, problem-based learning does work: A meta-analysis of curricular comparisons involving a single medical school. Educational Psychologist, 44(4), 227–249.CrossRefGoogle Scholar
  78. Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31(1), 64–96.CrossRefGoogle Scholar
  79. Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). Windsor: NFER-NELSON.Google Scholar
  80. Sliwinski, M. J., Smyth, J. M., Hofer, S. M., & Stawski, R. S. (2006). Intraindividual coupling of daily stress and cognition. Psychology and Aging, 21(3), 545.CrossRefGoogle Scholar
  81. Smits, P. B. A. (2002). Problem based learning in continuing medical education: a review of controlled evaluation studies. BMJ, 324(7330), 153–156.CrossRefGoogle Scholar
  82. Stegers-Jager, K. M., Cohen-Schotanus, J., & Themmen, A. P. N. (2012). Motivation, learning strategies, participation and medical school performance. Medical Education, 46(7), 678–688.CrossRefGoogle Scholar
  83. Strobel, J., & van Barneveld, A. (2009). When is PBL more effective? A Meta-synthesis of meta-analyses comparing PBL to conventional classrooms. Interdisciplinary Journal of Problem-based Learning, 3(1), 45–58.Google Scholar
  84. Struyven, K., Dochy, F., Janssens, S., & Gielen, S. (2006). On the dynamics of students’ approaches to learning: The effects of the teaching/learning environment. Learning and Instruction, 16, 279–294.CrossRefGoogle Scholar
  85. ten Cate, O. (2001). What happens to the student? The neglected variable in educational outcome research. Advances in Health Sciences Education, 6(1), 81–88.CrossRefGoogle Scholar
  86. van der Veken, J., Valcke, M., de Maeseneer, J., & Derese, A. (2009a). Impact of the transition from a conventional to an integrated contextual medical curriculum on students’ learning patterns: A longitudinal study. Medical Teacher, 31(5), 433–441.CrossRefGoogle Scholar
  87. van der Veken, Jos, Valcke, M., de Maeseneer, J., Schuwirth, L., & Derese, A. (2009b). Impact on knowledge acquisition of the transition from a conventional to an integrated contextual medical curriculum. Medical Education, 43(7), 704–713.CrossRefGoogle Scholar
  88. Vanthournout, G., Donche, V., Gijbels, D., & Petegem, P. (2009). Alternative data-analysis techniques in research on student learning: illustrations of a person-oriented and developmental perspectives. Reflecting Education, 5(2), 35–51.Google Scholar
  89. Verhoeven, B. H., Snellen-Balendong, H. A., Hay, I. T., Boon, J. M., Van Der Linde, M. J., Blitz-Lindeque, J. J., & Scherpbier, A. (2005). The versatility of progress testing assessed in an international context: a start for benchmarking global standardization? Medical Teacher, 27(6), 514–520.CrossRefGoogle Scholar
  90. Verhoeven, B. H., & Verwijnen, G. M. (1998). An analysis of progress test results of PBL and non-PBL students. Medical Teacher, 20(4), 310–316.CrossRefGoogle Scholar
  91. Verhoeven, B. H., Verwijnen, G. M., Scherpbier, A., & Van der Vleuten, C. P. (2002). Growth of medical knowledge. Medical Education, 36(8), 711–717.CrossRefGoogle Scholar
  92. Vernon, D. T., & Blake, R. L. (1993). Does problem-based learning work? A meta-analysis of evaluative research. Academic Medicine, 68(7), 550–563.CrossRefGoogle Scholar
  93. Walberg, H. J., & Tsai, S.-L. (1983). Matthew effects in education. American Educational Research Journal, 20(3), 359–373.Google Scholar
  94. Wäschle, K., Allgaier, A., Lachner, A., Fink, S., & Nückles, M. (2014). Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction, 29, 103–114.CrossRefGoogle Scholar
  95. Westermann, R., Heise, E., Spies, K., & Trautwein, U. (1996). Identifikation und Erfassung von Komponenten der Studienzufriedenheit. [Identifying and assessing components of student satisfaction.]. Psychologie in Erziehung und Unterricht, 43(1), 1–22.Google Scholar
  96. Wild, K.-P., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. Zeitschrift für Differentielle und Diagnostische Psychologie, 15, 185–200.Google Scholar
  97. Wolf, F. M. (1986). Meta-Analysis: Quantitative Methods for Research Synthesis. Meta-analysis: Quantitative Methods for Research Synthesis. Thousand Oaks: SAGE Publications.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Stefan K. Schauber
    • 1
    Email author
  • Martin Hecht
    • 2
  • Zineb M. Nouns
    • 3
  • Adelheid Kuhlmey
    • 4
  • Susanne Dettmer
    • 4
  1. 1.Institute of Medical Sociology and Rehabilitation Science and Department for AssessmentCharité – Universitätsmedizin BerlinBerlinGermany
  2. 2.Institute for Educational Quality ImprovementHumboldt–Universität zu BerlinBerlinGermany
  3. 3.Institute of Medical EducationUniversity of BernBernSwitzerland
  4. 4.Institute of Medical Sociology and Rehabilitation ScienceCharité – Universitätsmedizin BerlinBerlinGermany

Personalised recommendations