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The Usefulness of Log Based Clustering in a Complex Simulation Environment

  • Samad Kardan
  • Ido Roll
  • Cristina Conati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

Data mining techniques have been successfully employed on user interaction data in exploratory learning environments. In this paper we investigate using data mining techniques for analyzing student behaviors in an especially-complex exploratory environment, with over one hundred possible actions at any given point. Furthermore, the outcomes of these actions depend on their context. We propose a multi-layer action-events structure to deal with the complexity of the data and employ clustering and rule mining to examine student behaviors in terms of learning performance and effects of different degrees of scaffolding. Our findings show that using the proposed multi-layer structure for describing action-events enables the clustering algorithm to effectively identify the successful and unsuccessful students in terms of learning performance across activities in the presence or absence of external scaffolding. We also report and discuss the prominent behavior patterns of each group and investigate short term effects of scaffolding.

Keywords

Educational Data Mining Clustering Scaffolding 

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References

  1. 1.
    Koedinger, K.R., Corbett, A.T.: Cognitive tutors: Technology bringing learning science to the classroom. In: The Cambridge Handbook of the Learning Sciences, pp. 61–78 (2006)Google Scholar
  2. 2.
    VanLehn, K.: The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education 16, 227–265 (2006)Google Scholar
  3. 3.
    Shute, V.J., Ventura, M., Kim, Y.J.: Assessment and Learning of Qualitative Physics in Newton’s Playground. The Journal of Educational Research 106, 423–430 (2013)CrossRefGoogle Scholar
  4. 4.
    Gobert, J.D., Pedro, M.A.S., Baker, R.S.J.d., Toto, E., Montalvo, O.: Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. JEDM - Journal of Educational Data Mining 4, 111–143 (2012)Google Scholar
  5. 5.
    Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. International Journal of Artificial Intelligence in Education 18, 181–208 (2008)Google Scholar
  6. 6.
    Roll, I., Aleven, V., Koedinger, K.R.: The Invention Lab: Using a Hybrid of Model Tracing and Constraint-Based Modeling to Offer Intelligent Support in Inquiry Environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 115–124. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21, 267–280 (2011)CrossRefGoogle Scholar
  8. 8.
    Gong, Y., Beck, J.E., Ruiz, C.: Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 102–113. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Shih, B., Koedinger, K.R., Scheines, R.: Unsupervised Discovery of Student Strategies. In: Proceedings of the 3rd Intl. Conf. on Educational Data Mining, pp. 201–210 (2010)Google Scholar
  10. 10.
    Kardan, S., Conati, C.: A Framework for Capturing Distinguishing User Interaction Behaviours in Novel Interfaces. In: Proc. of the 4th Int. Conf. on Educational Data Mining, Eindhoven, The Netherlands, pp. 159–168 (2011)Google Scholar
  11. 11.
    Wieman, C.E., Adams, W.K., Perkins, K.K.: PhET: Simulations That Enhance Learning. Science 322, 682–683 (2008)CrossRefGoogle Scholar
  12. 12.
    De Jong, T., Van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research 68, 179–201 (1998)CrossRefGoogle Scholar
  13. 13.
    Kardan, S.: Data mining for adding adaptive interventions to exploratory and open-ended environments. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 365–368. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Zhang, C., Zhang, S.: Association rule mining: Models and algorithms. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11, 10–18 (2009)CrossRefGoogle Scholar
  16. 16.
    Kardan, S., Conati, C.: Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 215–227. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)CrossRefGoogle Scholar
  18. 18.
    Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)CrossRefzbMATHGoogle Scholar
  19. 19.
    Roll, I., Yee, N., Briseno, A.: Students’ Adaptation and Transfer of Strategies Across Levels of Scaffolding in an Exploratory Environment. In: Proc. of the 12th Intl. Conf. on Intelligent Tutoring Systems (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Samad Kardan
    • 1
  • Ido Roll
    • 2
  • Cristina Conati
    • 1
  1. 1.Department of computer ScienceUniversity of British ColumbiaCanada
  2. 2.Centre for Teaching, Learning, and TechnologyUniversity of British ColumbiaVancouverCanada

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