Key attribute for predicting student academic performance

Sachio Hirokawa

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

    1 Citation (Scopus)

    Abstract

    Predicting student final score from student's attributes is an important issue of learning analytic. Not only to achieve high prediction performance but also to identifying the key attributes is an important research theme. This paper evaluated exhaustively the prediction performance based on all possible combinations of four types of attributes - behavioral features, demographic features, academic background, and parent participation. The behavioral features are given as numerical data. But, we represented them as pair of an attribute name and the value. This vectorization yields 417 dimensional data, while naively represented data has 68 dimension. By applyig support vector machine and feature selection, we obtained the optimal prediction performance, with respect to feature selection, with accuracy 0.8096 and F-measure 0.7726. We confirmed that the behavioral feature is so crucial that the accuracy reaches 0.7905 without other features except behavioral feature. The combination of behavior feature and demographic feature gained F-measure 0.7662.

    Original languageEnglish
    Title of host publicationProceedings of the 10th International Conference on Education Technology and Computers, ICETC 2018
    PublisherAssociation for Computing Machinery
    Pages308-313
    Number of pages6
    ISBN (Electronic)9781450365178
    DOIs
    Publication statusPublished - Oct 26 2018
    Event10th International Conference on Education Technology and Computers, ICETC 2018 - Tokyo, Japan
    Duration: Oct 26 2018Oct 28 2018

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference10th International Conference on Education Technology and Computers, ICETC 2018
    CountryJapan
    CityTokyo
    Period10/26/1810/28/18

    All Science Journal Classification (ASJC) codes

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

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