Finding key integer values in many features for learners' academic performance prediction

Yudai Tanabe, Koki Kagari, Yuki Kitanaka, Kazuhiro Takeuchi, Sachio Hirokawa

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

    1 Citation (Scopus)

    Abstract

    In recent years, along with the proliferation of the learning management system (LMS), a large amount of data regarding the interaction between the system and the learners has been accumulated. Correspondingly, various data mining methods have been applied to these data. In order to employ a suitable computational model that is at the core of the data mining method and is not automatically acquired by the mining method itself, it is important to make or find various reasonable hypotheses for target variables. In this paper, we propose a method for analyzing closely the degree to which the explanatory variables represented in integer value contributes to predicting categorical objective variables, such as a learner's academic performance. Specifically, we describe that a decision tree combining support vector machines (SVM) achieves accuracy consistent with existing research, and it contributes further extraction of particular explanatory values from the integer features. Before making a model with SVM, our proposal method expands original features represented by integer value to corresponding binary features. With this expansion of original features, we can identify the key values that closely relate to a learner's academic performance from behavioral features gathered from LMS. Identifying such key values in specific features plays an important role in developing a hypothesis that explains the objective variables, using them as explanatory variables. We believe that closer analysis of these key explanatory values will find latent knowledge that can improve learners' academic abilities.

    Original languageEnglish
    Title of host publicationProceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017
    PublisherAssociation for Computing Machinery
    Pages167-171
    Number of pages5
    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

    Other

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

    All Science Journal Classification (ASJC) codes

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

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