Is Over Practice Necessary? --Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining

  • Authors:

  • Hao Cen;Kenneth R. Koedinger;Brian Junker

  • Affiliations:

  • Carnegie Mellon University, 5000 Forbes, Pittsburgh, PA, U.S.A., hcen@andrew.cmu.edu, koedinger@cmu.edu, brian@stat.cmu.edu;Carnegie Mellon University, 5000 Forbes, Pittsburgh, PA, U.S.A., hcen@andrew.cmu.edu, koedinger@cmu.edu, brian@stat.cmu.edu;Carnegie Mellon University, 5000 Forbes, Pittsburgh, PA, U.S.A., hcen@andrew.cmu.edu, koedinger@cmu.edu, brian@stat.cmu.edu

  • Venue:
  • Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
  • Year:
  • 2007

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Abstract

This study examined the effectiveness of an educational data mining method --Learning Factors Analysis (LFA) --on improving the learning efficiency in the Cognitive Tutor curriculum. LFA uses a statistical model to predict how students perform in each practice of a knowledge component (KC), and identifies over-practiced or under-practiced KCs. By using the LFA findings on the Cognitive Tutor geometry curriculum, we optimized the curriculum with the goal of improving student learning efficiency. With a control group design, we analyzed the learning performance and the learning time of high school students participating in the Optimized Cognitive Tutor geometry curriculum. Results were compared to students participating in the traditional Cognitive Tutor geometry curriculum. Analyses indicated that students in the optimized condition saved a significant amount of time in the optimized curriculum units, compared with the time spent by the control group. There was no significant difference in the learning performance of the two groups in either an immediate post test or a two-week-later retention test. Findings support the use of this data mining technique to improve learning efficiency with other computer-tutor-based curricula.