Advertisement

Using Local and Global Self-evaluations to Predict Students’ Problem Solving Behaviour

  • Lenka Schnaubert
  • Eric Andrès
  • Susanne Narciss
  • Sergey Sosnovsky
  • Anja Eichelmann
  • George Goguadze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7563)

Abstract

This paper investigates how local and global self-evaluations of capabilities can be used to predict pupils’ problem-solving behaviour in the domain of fraction learning. To answer this question we analyzed logfiles of pupils who worked on multi-trial fraction tasks. Logistic regression analyses revealed that local confidence judgements assessed online improve the prediction of post-error solving, as well as skipping behaviour significantly, while pre-assessed global perception of competence failed to do so. Yet, for all computed models, the impact of our prediction is rather small. Further research is necessary to enrich these models with other relevant user- as well as task-characteristics to make them usable for adaptation.

Keywords

Area Under Curve Initial Error Cognitive Load Theory Educational Psychology Review Mastery Motivation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Spada, H., Plesch, C., Wiedmann, M., Kaendler, C., Deiglmayr, A., Mullins, D., Rummel, N.: D1.6: Final report on the STELLAR Delphi study: Future directions for TEL and TEL research: Areas of Tension, Core Research Areas, and Grand Challenge Problems. The STELLAR Network of Excellence (2012)Google Scholar
  2. 2.
    Vandewaetere, M., Desmet, P., Clarebout, G.: The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior 27, 118–130 (2011); Third International Cognitive Load Theory Conference on Current Research Topics in Cognitive Load TheoryCrossRefGoogle Scholar
  3. 3.
    Schnaubert, L., Andrès, E., Narciss, S., Eichelmann, A., Goguadze, G., Melis, E.: Student Behavior in Error-Correction-Tasks and Its Relation to Perception of Competence. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 370–383. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Bouffard, T., Narciss, S.: Benefits and risks of positive biases in self-evaluation of academic competence: Introduction. International Journal of Educational Research 50, 205–208 (2011)CrossRefGoogle Scholar
  5. 5.
    Eckert, C., Schilling, D., Stiensmeier-Pelster, J.: Einfluss des fähigkeitsselbstkonzepts auf die intelligenz- und konzentrationsleistung. Zeitschrift für Pädagogische Psychologie 20, 41–48 (2006)CrossRefGoogle Scholar
  6. 6.
    Dempsey, J.V., Driscoll, M.P.: Error and feedback: Relationship between content analysis and confidence of response. Psychological Reports 78, 1079–1089 (1996)CrossRefGoogle Scholar
  7. 7.
    Zhao, Q., Linderholm, T.: Adult metacomprehension: Judgment processes and accuracy constraints. Developmental Psychology 20, 191–206 (2008)Google Scholar
  8. 8.
    Harter, S.: A model of intrinsic mastery motivation in children: Individual differences and developmental changes. In: Collins, A. (ed.) Minnesota Symposia on Child Psychology, pp. 215–255. Erlbaum (1981)Google Scholar
  9. 9.
    Marsh, H.W., Shavelson, R.: Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist 20, 107–123 (1985)CrossRefGoogle Scholar
  10. 10.
    Zhao, Q., Linderholm, T.: Anchoring effects on prospective and retrospective metacomprehension judgments as a function of peer performance information. Metacognition and Learning 6, 25–43 (2011)CrossRefGoogle Scholar
  11. 11.
    Bandura, A.: Self-efficacy. Encyclopedia of Human Behavior 4, 71–81 (1994)Google Scholar
  12. 12.
    Schunk, D.H.: Self-efficacy and academic motivation. Educational Psychologist 26, 207–231 (1991)Google Scholar
  13. 13.
    Zimmerman, B.J., Bandura, A., Martinez-Pons, M.: Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal 29, 663–676 (1992)Google Scholar
  14. 14.
    Narciss, S., Körndle, H., Dresel, M.: Self-evaluation accuracy and satisfaction with performance: Are there affective costs or benefits of positive self-evaluation bias? International Journal of Educational Research 50, 230–240 (2011); Knowledge: The Legacy of Competence, pp. 143–151. Springer, Netherlands (2008) CrossRefGoogle Scholar
  15. 15.
    Dunlosky, J., Metcalfe, J.: Metacognition. Sage Publications (2009)Google Scholar
  16. 16.
    Dougherty, M.R.P.: Integration of the ecological and error model of overconfidence. Journal of Experimental Psychology: General 130 (2001)Google Scholar
  17. 17.
    Gigerenzer, G., Hoffrage, U., Kleinbölting, H.: Probabilistic mental models: A brunswikian theory of confidence. Psychological Review 98, 506–528 (1991)CrossRefGoogle Scholar
  18. 18.
    Koriat, A., Lichtenstein, S., Fischhoff, B.: Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory 6, 107–118 (1980)CrossRefGoogle Scholar
  19. 19.
    Koriat, A., Nussinson, R., Bless, H., Shaked, N.: Information-based and experience-based metacognitive judgments: Evidence from subjective confidence. In: Dunlosky, J., Bjork, R.A. (eds.) A Handbook of Memory and Metamemory, pp. 117–136. Lawrence Erlbaum Associates (2008)Google Scholar
  20. 20.
    Kröner, S., Biermann, A.: The relationship between confidence and self-concept towards a model of response confidence. Intelligence 35, 580–590 (2007)CrossRefGoogle Scholar
  21. 21.
    Kulhavy, R., Stock, W.: Feedback in written instruction: The place of response certitude. Educational Psychology Review 1, 279–308 (1989)CrossRefGoogle Scholar
  22. 22.
    Hancock, T.E., Stock, W.A., Kulhavy, R.W.: Predicting feedback effects from response-certitude estimates. Bulletin of the Psychonomic Society 30, 173–176 (1992)Google Scholar
  23. 23.
    Hancock, T.E., Thurman, R.A., Hubbard, D.C.: An expanded control model for the use of instructional feedback. Contemporary Educational Psychology 20, 410–425 (1995)CrossRefGoogle Scholar
  24. 24.
    Vasilyeva, E., Pechenizkiy, M., De Bra, P.: Tailoring of Feedback in Web-Based Learning: The Role of Response Certitude in the Assessment. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 771–773. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Kulhavy, R.W., Wager, W.: Feedback in programmed instruction: Historical context and implications for practice. In: Dempsey, J.V., Sales, G.C. (eds.) Interactive Instruction and Feedback, pp. 3–20. Educational Technology Publications, Englewood Cliffs (1993)Google Scholar
  26. 26.
    Fazio, L., Marsh, E.: Surprising feedback improves later memory. Psychonomic Bulletin & Review 16, 88–92 (2009)CrossRefGoogle Scholar
  27. 27.
    Butterfield, B., Metcalfe, J.: Errors committed with high confidence are hypercorrected. Journal of Experimental Psychology Learning Memory and Cognition 27, 1491–1494 (2001)CrossRefGoogle Scholar
  28. 28.
    Butterfield, B., Metcalfe, J.: The correction of errors committed with high confidence. Metacognition and Learning 1, 69–84 (2006)CrossRefGoogle Scholar
  29. 29.
    Metcalfe, J., Finn, B.: Peoples hypercorrection of high-confidence errors: did they know it all along? Journal of Experimental Psychology Learning Memory and Cognition 37, 437–448 (2011)CrossRefGoogle Scholar
  30. 30.
    Stankov, L., Crawford, J.D.: Self-confidence and performance on tests of cognitive abilities. Intelligence 25, 93–109 (1997)CrossRefGoogle Scholar
  31. 31.
    Melis, E., Goguadze, G., Homik, M., Libbrecht, P., Ullrich, C., Winterstein, S.: Semantic-aware components and services in activemath. British Journal of Educational Technology. Special Issue: Semantic Web for E-learning 37, 405–423 (2006)Google Scholar
  32. 32.
    Narciss, S.: Informatives tutorielles Feedback. Waxmann (2006)Google Scholar
  33. 33.
    Mory, E.H.: Feedback research revisited. In: Jonassen, D.H. (ed.) Handbook of Research on Educational Communications and Technology, 2nd edn., pp. 745–783. Lawrence Erlbaum Associates, Mahwah (2004)Google Scholar
  34. 34.
    Thiede, K.W., Anderson, M.C.M., Therriault, D.: Accuracy of Metacognitive Monitoring Affects Learning of Texts. Journal of Educational Psychology 95, 66–73 (1980)CrossRefGoogle Scholar
  35. 35.
    Stone, N.J.: Exploring the relationship between calibration and self-regulated learning. Educational Psychology Review 12, 437–475 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lenka Schnaubert
    • 1
  • Eric Andrès
    • 2
  • Susanne Narciss
    • 1
  • Sergey Sosnovsky
    • 2
  • Anja Eichelmann
    • 1
  • George Goguadze
    • 2
  1. 1.Technische Universität DresdenDresdenGermany
  2. 2.Deutsches Forschungszentrum für Künstliche IntelligenzSaarbrückenGermany

Personalised recommendations