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International Conference on Interactive Collaborative Learning

ICL 2016: Interactive Collaborative Learning pp 175–189Cite as

Predicting Students’ Behavior During an E-Learning Course Using Data Mining

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 545))

Abstract

This paper introduces a process of building a prediction model for student’s final grade and time of finishing, based on students’ previous behavior. Prediction model was developed using data mining with regression analysis, principle component analysis and hierarchical clustering of symbolic histogram valued data. 35 different features of students’ activates was considered but only the 9 most important, so called principle components, were used in the model. Then, using histogram valued data - a type of symbolic data that allows learning processes to be described in a more natural form, and a hierarchical clustering, previous students’ behaviors were grouped. For an accurate prediction, a closest cluster to student’s current progress was found. To verify the model’s correctness, predictions were tested on a largest course in e-learning system in 2015 fall semester. The model was found to work sufficiently.

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Acknowledgments

This research was supported by European Social Fund’s Doctoral Studies and Internationalization Programme DoRa and DoRa+.

Author information

Authors and Affiliations

  1. Tallinn University of Technology, Tallinn, Estonia

    Kadri Umbleja

  2. Tokyo Denki University, Saitama, Japan

    Manabu Ichino

Authors
  1. Kadri Umbleja
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  2. Manabu Ichino
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Corresponding author

Correspondence to Kadri Umbleja .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. International E-Learn Asso&Kaleid Learni, New York, New York, USA

    David Guralnick

  3. Ulster University, Belfast, Ireland

    James Uhomoibhi

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Umbleja, K., Ichino, M. (2017). Predicting Students’ Behavior During an E-Learning Course Using Data Mining. In: Auer, M., Guralnick, D., Uhomoibhi, J. (eds) Interactive Collaborative Learning. ICL 2016. Advances in Intelligent Systems and Computing, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-319-50340-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-50340-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50339-4

  • Online ISBN: 978-3-319-50340-0

  • eBook Packages: EngineeringEngineering (R0)

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