Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

The effectiveness of propositional manipulation as a lecturing method in the statistics knowledge domain

  • 247 Accesses

  • 3 Citations

Abstract

The current experiment examined the potential effects of the method of propositional manipulation (MPM) as a lecturing method on motivation to learn and conceptual understanding of statistics. MPM aims to help students develop conceptual understanding by guiding them into self-explanation at two different stages: First, at the stage of propositions (statements referring to single statistical concepts and ideas), and subsequently, at the stage of more complex problems that comprise a set of relevant propositions. A total of 71 bachelor students in psychology who were preparing for the re-sit of their inferential statistics exam participated in one of two possible lectures. Topic, content, lecturer, and duration of both lectures were the same, and in both lectures five true/false hypotheses were presented. Students in the first lecture (control group) discussed interactively the truth or falsity of each hypothesis. In the second lecture (MPM group), this interactive discussion was structured by presenting a number of short open-ended questions along with each hypothesis. Conceptual understanding was measured by means of a twelve items multiple choice test. Further, the intrinsic motivation inventory was administered to examine motivation to learn. The results indicate that MPM does not lead to enhanced motivation to learn but can facilitate conceptual understanding development among students.

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

References

  1. Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.

  2. Broers, N. J. (2002). Selection and use of propositional knowledge in statistical problem solving. Learning and Instruction, 12, 323–344.

  3. Broers, N. J. (2008). Helping students to build a conceptual understanding of elementary statistics. The American Statistician, 62, 1–6.

  4. Broers, N. J. (2009). Using propositions for the assessment of structural knowledge. Journal of Statistics Education, 17, 1–19.

  5. Broers, N. J., & Imbos, Tj. (2005). Charting and manipulating propositions as methods to promote self-explanation in the study of statistics. Learning and Instruction, 15, 517–538.

  6. Broers, N. J., Mur, M. C., & Bude, L. (2005). Directed self-explanation in the study of statistics. In G. Burrill & M. Camden (Eds.), Curricular development in statistics education (pp. 21–35). Voorburg: International statistical institute.

  7. Bruinsma, M. (2003). Leidt hogere motivatie tot betere prestaties? Motivatie, informatieverwerking, en studievoortgang in het hoger onderwijs. [Does higher motivation result in higher achievement? Motivation, cognitive processing and achievement in higher education]. Pedagogische Studien, 80, 226–238.

  8. Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. (1994). Facilitating internalization: the self-determination theory perspective. Journal of Personality, 62, 119–142.

  9. Fischer, F. (2002). Gemeinsame Wissenskonstruktion: Theoretische und methodologische Aspekte [Joint knowledge construction: theoretical and methodological aspects]. Psychologische Rundschau, 53, 119–134.

  10. Huberty, C. J., Dresden, J., & Bak, B. (1993). Relations among dimensions of statistical knowledge. Educational and Psychological Measurement, 53, 523–532.

  11. Hulsizer, M. R., & Woolf, L. M. (2009). A guide to teaching statistics: innovations and best practices. Oxford: Wiley-Blackwell.

  12. Johnson, D. W., Johnson, R. T., & Smith, K. (2007). The state of cooperative learning in postsecondary and professional settings. Educational Psychology Review, 19, 15–29.

  13. Kalyuga, S. (2009). Knowledge elaboration: a cognitive load perspective. Learning and Instruction, 19, 402–410.

  14. Kalyuga, S., & Hanham, J. (2010). Instructing in generalized knowledge structures to develop flexible problem solving skills. Computers in Human Behavior (2010). doi:10.101G/j.cnb.2010.05.024.

  15. Knipfer, K., Mayr, E., Zahn, C., Schwan, S., & Hesse, F. W. (2009). Computer support for knowledge communication in science exhibitions: novel perspectives from research on collaborative learning. Educational Research Review, 4, 196–209.

  16. Kuhl, J. (2000). A functional-design approach in motivation and self-regulation: the dynamics of personality systems interactions. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 111–169). San Diego: Academic Press.

  17. Leppink, J. (2010). Adjusting cognitive load to the student’s level of expertise for increasing motivation to learn, Proceedings of the Eighth International Conference on Teaching Statistics [Conference paper]. Slovenia: Ljubljana.

  18. Leppink, J. (2011). Zelfverklaring door middel van argumentatie: de invloed van voorkennis [Self-explanation by means of argumentation: the influence of prior knowledge], Onderwijs Research Dagen 2011 [Conference paper]. The Netherlands: Maastricht.

  19. Leppink, J. (2012). Propositional knowledge for conceptual understanding of statistics. PhD Dissertation. Maastricht: Maastricht University.

  20. Leppink, J., Broers, N. J., Imbos, Tj, Van der Vleuten, C. P. M., & Berger, M. P. F. (2011). Exploring task-and student-related factors in the method of propositional manipulation (MPM). Journal of Statistics Education, 19, 1–23.

  21. Leppink, J., Broers, N. J., Imbos, Tj, Van der Vleuten, C. P. M., & Berger, M. P. F. (2012a). Self-explanation in the domain of statistics: an expertise reversal effect. Higher Education, 63, 771–785.

  22. Leppink, J., Broers, N. J., Imbos, Tj, Van der Vleuten, C. P. M., & Berger, M. P. F. (2012b). Prior knowledge moderates instructional effects on conceptual understanding of statistics. Educational Research and Evaluation, 18, 37–51.

  23. Levesque, C., Zuehlke, A. N., Stanek, L. R., & Ryan, R. M. (2004). Autonomy and competence in German and American university students: a comparative study based on self-determination theory. Journal of Educational Psychology, 96, 68–84.

  24. Markland, D., & Hardy, L. (1997). On the factorial and construct validity of the intrinsic motivation inventory: conceptual and operational concerns. Research Quarterly for Exercise and Sports, 68, 20–32.

  25. Martens, R. L., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e-learning in authentic computer tasks. Journal of Computer Assisted Learning, 20, 368–376.

  26. McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: a confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60, 48–58.

  27. Novak, J. D. (2002). Meaningful learning: the essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners. Learning, 548–571.

  28. Ryan, R. M. (1982). Control and information in the intrapersonal sphere: an extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450–461.

  29. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

  30. Ryan, R. M., Koestner, R., & Deci, E. L. (1991). Varied forms of persistence: when free-choice behavior is not intrinsically motivated. Motivation and Emotion, 15, 185–205.

  31. Ryan, R. M., Mims, V., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: a review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45, 736–750.

  32. Tsigilis, N., & Theodosiou, A. (2003). Temporal stability of the intrinsic motivation inventory. Perceptual and Motor Skills, 97, 271–280.

  33. Van Buuren, J. A. (2008). Van vakgericht naar competentiegericht statistiekonderwijs: een interventiestudie in een opleiding psychologie [From subject-oriented to competence-based statistics education: an intervention study in a school of psychology] PhD Dissertation, Open University of the Netherlands. Voerendaal: Schrijen Lippertz Huntjens.

Download references

Author information

Correspondence to Jimmie Leppink.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Leppink, J., Broers, N.J., Imbos, T. et al. The effectiveness of propositional manipulation as a lecturing method in the statistics knowledge domain. Instr Sci 41, 1127–1140 (2013). https://doi.org/10.1007/s11251-013-9268-3

Download citation

Keywords

  • Propositional manipulation
  • Guided self-explanation
  • Motivation to learn statistics
  • Conceptual understanding of statistics