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A Differential Approach for Identifying Important Student Learning Behavior Patterns with Evolving Usage over Time

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Book cover Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

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Abstract

Effective design and improvement of dynamic feedback in computer-based learning environments requires the ability to assess the effectiveness of a variety of feedback options, not only in terms of overall performance and learning, but also in terms of more subtle effects on students’ learning behavior and understanding. In this paper, we present a novel interestingness measure, and corresponding data mining and visualization approach, which aids the investigation and understanding of students’ learning behaviors. The presented approach identifies sequential patterns of activity that distinguish groups of students (e.g., groups that received different feedback during extended, complex learning activities) by differences in both total behavior pattern usage and evolution of pattern usage over time. We demonstrate the utility of this technique through application to student learning activity data from a recent experiment with the Betty’s Brain learning environment and four different feedback and learning scaffolding conditions.

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Notes

  1. 1.

    In the results presented in Sect. 5, we allowed a maximum gap of one action, allowing up to one irrelevant or variable action between consecutive actions in the pattern. However, in general, other sizes of gaps or no gap at all may be appropriate depending on the data and goals of an analysis.

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Acknowledgments

This work has been supported by NSF-IIS Award #0904387 and IES Award #R305A120186.

Author information

Authors and Affiliations

  1. Department of EECS and ISIS, Vanderbilt University, Nashville, TN, USA

    John S. Kinnebrew & Gautam Biswas

  2. Baseball Analytics, Kansas City Royals, Kansas City, MO, USA

    Daniel L. C. Mack

  3. Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan

    Chih-Kai Chang

Authors
  1. John S. Kinnebrew
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  2. Daniel L. C. Mack
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  3. Gautam Biswas
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  4. Chih-Kai Chang
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Corresponding author

Correspondence to John S. Kinnebrew .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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Kinnebrew, J.S., Mack, D.L.C., Biswas, G., Chang, CK. (2014). A Differential Approach for Identifying Important Student Learning Behavior Patterns with Evolving Usage over Time. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_27

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

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