Predicting learning result of learner in e-learning course with feature selection using SVM

Yuki Kitanaka, Kazuhiro Takeuchi, Sachio Hirokawa

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

    Abstract

    In recent years, data mining targeting educational data has been widely performed. With the spread of the e-learning system, activities of various learners have been recorded. By analyzing this record, research is being conducted to evaluate the achievement level of the learner and to find hidden problems. In this paper, we compare the existing method and the method by SVM using feature selection for the method of classifying the final result from the learner's activity record. This confirms the effectiveness of the method using feature selection. Next, we confirmed that the click stream which is the activity data in the elearning system is more effective than the learner's profile in classification of grades. In the classification of learners with good grades, the connection records and the number of clicks in the latter period of the learning period are important factors, further the difference in the important features by the grade evaluation was shown.

    Original languageEnglish
    Title of host publicationProceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017
    PublisherAssociation for Computing Machinery
    Pages122-125
    Number of pages4
    ISBN (Electronic)9781450354356
    DOIs
    Publication statusPublished - Dec 20 2017
    Event9th International Conference on Education Technology and Computers, ICETC 2017 - Barcelona, Spain
    Duration: Dec 20 2017Dec 22 2017

    Publication series

    NameACM International Conference Proceeding Series

    Other

    Other9th International Conference on Education Technology and Computers, ICETC 2017
    CountrySpain
    CityBarcelona
    Period12/20/1712/22/17

    All Science Journal Classification (ASJC) codes

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications

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