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Using a Cognitive/Metacognitive Task Model to Analyze Students Learning Behaviors

  • Gautam Biswas
  • John S. Kinnebrew
  • James R. Segedy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)

Abstract

Adapting to learners’ needs and providing useful, individualized feedback to help them succeed has been a hallmark of most intelligent tutoring systems. More recently, to promote deep learning and critical thinking skills in STEM disciplines, researchers have begun developing open-ended learning environments that present learners with complex problems and a set of tools for learning and problem solving. To be successful in such environments, learners must employ a variety of cognitive skills and metacognitive strategies. This paper discusses a framework that combines a theory-driven, top-down approach with a bottom-up, pattern-discovery approach for analyzing learning activity data in these environments. Combining these approaches allows for more complex qualitative and quantitative interpretation of a student’s cognitive and metacognitive abilities. The results of this analysis provide a foundation for developing performance- and behavior-based learner models in conjunction with adaptive scaffolding mechanisms to promote effective, personalized learning experiences.

Keywords

metacognition theory-driven top-down analysis pattern-driven bottom-up analysis effectiveness measures pattern mining adaptivity tutoring 

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References

  1. 1.
    Bransford, J.D., Brown, A.L., Cocking, R.R. (eds.): How people learn. National Academy Press, Washington, DC (2000)Google Scholar
  2. 2.
    Brown, A., Bransford, J.D., Ferrara, R.A., Campione, J.C.: Learning, remembering, and understanding. In: Mussen, P. (ed.) Handbook of Child Psychology. John Wiley, Hoboken (1983)Google Scholar
  3. 3.
    Flavell, J.: Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist 34(10), 906 (1979)CrossRefGoogle Scholar
  4. 4.
    Ho, J., Lukov, L., Chawla, S.: Sequential pattern mining with constraints on large protein databases. In: Proceedings of the 12th International Conference on Management of Data (COMAD), pp. 89–100 (2005)Google Scholar
  5. 5.
    Kinnebrew, J.S., Segedy, J.R., Biswas, G.: Analyzing the Temporal Evolution of Students’ Behaviors in Open-Ended Learning Environments. In: Metacognition and Learning (to appear, 2014)Google Scholar
  6. 6.
    Kinnebrew, J.S., Biswas, G.: Identifying learning behaviors by contextualizing differential sequence mining with action features and performance evolution. In: Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012), Chania, Greece (June 2012)Google Scholar
  7. 7.
    Kinnebrew, J.S., Loretz, K.M., Biswas, G.: A contextualized, differential sequence mining method to derive students’ learning behavior patterns. Journal of Educational Data Mining 5(1), 190–219 (2013)Google Scholar
  8. 8.
    Land, S.: Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development 48(3), 61–78 (2000)CrossRefGoogle Scholar
  9. 9.
    Leelawong, K., Biswas, G.: Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education 18(3), 181–208 (2008)Google Scholar
  10. 10.
    Luckin, R., du Boulay, B.: Ecolab: The development and evaluation of a Vygotskian design framework. International Journal of Artificial Intelligence in Education 10(2), 198–220 (1999)Google Scholar
  11. 11.
    Luckin, R., Hammerton, L.: Getting to know me: Helping learners understand their own learning needs through metacognitive scaffolding. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 759–771. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Park, O.C., Lee, J.: Adaptive instructional systems. Educational Technology Research and Development 25, 651–684 (2003)Google Scholar
  13. 13.
    Romero, C., Ventura, S.: Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics 40(6), 601–618 (2010)CrossRefGoogle Scholar
  14. 14.
    Segedy, J., Loretz, K., Biswas, G.: Model-driven assessment of learners in an open-ended learning environment. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 200–204. ACM, New York (2013)CrossRefGoogle Scholar
  15. 15.
    Segedy, J.R., Biswas, G., Sulcer, B.: A model-based behavior analysis approach for open-ended environments. Journal of Educational Technology & Society 17(1), 272–282 (2014)Google Scholar
  16. 16.
    Spires, H., Rowe, J., Mott, B., Lester, J.: Problem solving and game-based learning: Effects of middle grade students’ hypothesis testing strategies on learning outcomes. Journal of Educational Computing Research 44(4), 453–472 (2011)CrossRefGoogle Scholar
  17. 17.
    Veenman, M.V.: Metacognition in Science Education: Definitions, Constituents, and Their Intricate Relation with Cognition. In: Metacognition in Science Education, pp. 21–36. Springer, Netherlands (2012)CrossRefGoogle Scholar
  18. 18.
    Winne, P.: A metacognitive view of individual differences in self-regulated learning. Learning and individual differences 8(4), 327–353 (1996)CrossRefGoogle Scholar
  19. 19.
    Winne, P.H., Hadwin, A.F.: Studying as self-regulated learning. In: Hacker, D.J., Dunlosky, J., Graesser, A. (eds.) Metacognition in Educational Theory and Practice, pp. 277–304. Lawrence Erlbaum Associates Publishers (1998)Google Scholar
  20. 20.
    Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D., Zimmerman, B. (eds.) Motivation and Self-regulated Learning: Theory, Research, and Applications, pp. 297–314. Taylor & Francis, NY (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gautam Biswas
    • 1
  • John S. Kinnebrew
    • 1
  • James R. Segedy
    • 1
  1. 1.ISIS/EECS DepartmentVanderbilt UniversityNashvilleUSA

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