Informing Authoring Best Practices Through an Analysis of Pedagogical Content and Student Behavior

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)

Abstract

Among other factors, student behavior during learning activities is affected by the pedagogical content they are interacting with. In this paper, we analyze this effect in the context of a problem-solving based online Physics course. We use a representation of the content in terms of its position, composition and visual layout to identify eight content types that correspond to problem solving sub-tasks. Canonical examples as well as a sequence model of these tasks are presented. Student behaviors, measured in terms of activity, help-requests, mistakes and time on task, are compared across each content type. Students request more help while working through complex computational tasks and make more mistakes on tasks that apply conceptual knowledge. We discuss how these findings can inform the design of pedagogical content and authoring tools.

Keywords

Student behavior Content development Authoring Online learning Problem solving 

References

  1. 1.
    Aleven, V., Roll, I., McLaren, B.M., Koedinger, K.R.: Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educ. Psychol. 45(4), 226–233 (2010)CrossRefGoogle Scholar
  2. 2.
    Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why students engage in “Gaming the System” behavior in interactive learning environments. J. Interact. Learn. Res. 19(2), 185–224 (2008)Google Scholar
  3. 3.
    Cha, H.J., Kim, Y.S., Park, S.H., Yoon, T.B., Jung, Y.M., Lee, J.H.: Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In: Ikeda, M., Ashley, K.D., Chan, T.W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 513–524. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Beal, C.R., Qu, L., Lee, H.: Mathematics motivation and achievement as predictors of high school students’ guessing and help-seeking with instructional software. J. Comput. Assist. Learn. 24(6), 507–514 (2008)CrossRefGoogle Scholar
  5. 5.
    Walonoski, J.A., Heffernan, N.T.: Prevention of off-task gaming behavior in intelligent tutoring systems. In: Ikeda, M., Ashley, K.D., Chan, T.W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 722–724. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Koedinger, K.R., Aleven, V.: Exploring the assistance dilemma in experiments with cognitive tutors. Educ. Psychol. Rev. 19(3), 239–264 (2007)CrossRefGoogle Scholar
  7. 7.
    Kumar, R., Chung, G.K., Madni, A., Roberts, B.: First evaluation of the physics instantiation of a problem-solving-based online learning platform. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS, vol. 9112, pp. 686–689. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Kumar, R., Roy, M., Roberts, R.B., Makhoul, J.I.: Towards automatically building tutor models. In: International Conference on Intelligent Tutoring Systems (2014)Google Scholar
  10. 10.
    Stamper, J., Eagle, M., Barnes, T., Croy, M.: Experimental evaluation of automatic hint generation for a logic tutor. Int. J. Artif. Intell. Educ. 22, 3–17 (2013)Google Scholar
  11. 11.
    Chi, M.T.H., Feltovich, P.J., Glaser, R.: Categorization and representation of physics problems by experts and novices. Cogn. Sci. 5, 121–152 (1981)CrossRefGoogle Scholar
  12. 12.
    Kumar, R., Roy, M.E, Pattison-Gordon, E., Roberts, R.B.: General purpose ITS development tools. Workshop on Intelligent Tutoring System Authoring Tools, 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI (2014)Google Scholar
  13. 13.
    Sweller, J.: Cognitive load during problem solving: effects on learning. Cogn. Sci. 12, 257–285 (1988)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Raytheon BBN TechnologiesCambridgeUSA

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