Good students look back previous pages

Sachio Hirokawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Educational institutions have many expectations for the use of E-book. The top expectation is to evaluate and to improve the education system based on the accumulated learning activity log data. This paper applied machine learning to predict the learner's final score from e-Book browsing logs. The present paper evaluated the prediction performance of the good students with the final grade of 80 or more from their learning access logs. An experimental evaluation revealed that the prediction performance (accuracy) was only 64% if we use only the accessed page information. However, the accuracy was improved to 89% when consecutive browsing page transition information was used. Furthermore, it was confirmed that returning to the previous page as a feature of the highest grades student.s.

    Original languageEnglish
    Title of host publicationICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings
    EditorsLung-Hsiang Wong, Michelle Banawan, Niwat Srisawasdi, Jie Chi Yang, Ma. Mercedes T. Rodrigo, Maiga Chang, Ying-Tien Wu
    PublisherAsia-Pacific Society for Computers in Education
    Pages457-466
    Number of pages10
    ISBN (Electronic)9789869721424
    Publication statusPublished - Nov 24 2018
    Event26th International Conference on Computers in Education, ICCE 2018 - Metro Manila, Philippines
    Duration: Nov 26 2018Nov 30 2018

    Publication series

    NameICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings

    Other

    Other26th International Conference on Computers in Education, ICCE 2018
    CountryPhilippines
    CityMetro Manila
    Period11/26/1811/30/18

    All Science Journal Classification (ASJC) codes

    • Computer Science (miscellaneous)
    • Computer Science Applications
    • Education

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