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Learning from learning curves: discovering interpretable learning trajectories

Published:13 March 2017Publication History

ABSTRACT

We propose a data driven method for decomposing population level learning curve models into mutually exclusive distinctive groups each consisting of similar learning trajectories. We validate this method on six knowledge components from the log data from an online tutoring system ASSIST-ment. Preliminary analysis reveals interpretable patterns of "skill growth" that correlate with students' performance in the subsequently administered state standardized tests.

References

  1. {Koedinger et al. 2010} Koedinger, K. R., R. S. J. Baker, K. Cunningham, and A. Skogsholm (2010). A Data Repository for the EDM community : The PSLC DataShop. Handbook of Educational Data Mining, 43--55.Google ScholarGoogle Scholar
  2. {Nagin 2005} Nagin, D. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.Google ScholarGoogle Scholar
  3. {Trivedi et al. 2011} Trivedi, S., Z. Pardoz, and N. Heffernan (2011). Spectural Clustering in Educational Data MIning. Proceedings of the 4th International Conference on Educational Data Mining, 129--138.Google ScholarGoogle Scholar

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  1. Learning from learning curves: discovering interpretable learning trajectories

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        cover image ACM Other conferences
        LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
        March 2017
        631 pages
        ISBN:9781450348706
        DOI:10.1145/3027385

        Copyright © 2017 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 March 2017

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        Acceptance Rates

        LAK '17 Paper Acceptance Rate36of114submissions,32%Overall Acceptance Rate236of782submissions,30%

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