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A systematic review of studies on predicting student learning outcomes using learning analytics

Published:13 March 2017Publication History

ABSTRACT

Predicting student learning outcomes is one of the prominent themes in Learning Analytics research. These studies varied to a significant extent in terms of the techniques being used, the contexts in which they were situated, and the consequent effectiveness of the prediction. This paper presented the preliminary results of a systematic review of studies in predictive learning analytics. With the goal to find out what methodologies work for what circumstances, this study will be able to facilitate future research in this area, contributing to relevant system developments that are of pedagogic values.

References

  1. Shahiria, A. M., Husaina, W., & Rashida, N. A. 2015. A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414--422.Google ScholarGoogle ScholarCross RefCross Ref
  2. Strauss, A. L. 1987. Qualitative Analysis for Social Scientists. Cambridge University Press, London.Google ScholarGoogle Scholar
  3. Strauss, A. L., & Corbin, J. 1998. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2nd ed. Sage Publications, Thousand Oaks, CA.Google ScholarGoogle Scholar

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  1. A systematic review of studies on predicting student learning outcomes using learning analytics

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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

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the 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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