Student placement predictor for programming class using classes attitude, psychological scale, and code metrics

Ryosuke Ishizue, Kazunori Sakamoto, Hironori Washizaki, Yoshiaki Fukazawa

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    It is often necessary to divide a class according to students' skill level and motivation to learn. This process is burdensome for teachers because they must prepare, implement, and evaluation a placement examination. This paper tries to predict the placement results via machine learning from some materials without such an examination. The explanatory variables are 1. Psychological Scale, 2. Programming Task, and 3. Student-answered Questionnaire. The participants are university students enrolled in a Java programming class. The target variable is the placement result based on an examination by a teacher of the class. Our classification model with Decision Tree has an F-measure of 0.937. We found that the set of the following explanatory variables can yield the best F-measure (0.937): (1) Class Fan Out Complexity, (2) Practical utility value, (3) Difficulty Level 4 (AOJ), (4) Difficulty Level 3 (AOJ), (5) Interest value, and (6) Never-Give-Up Attitude.

    本文言語English
    ホスト出版物のタイトルProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
    編集者Ahmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen
    出版社Asia-Pacific Society for Computers in Education
    ページ40-49
    ページ数10
    ISBN(印刷版)9789869401265
    出版ステータスPublished - 2017 1 1
    イベント25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
    継続期間: 2017 12 42017 12 8

    Other

    Other25th International Conference on Computers in Education, ICCE 2017
    CountryNew Zealand
    CityChristchurch
    Period17/12/417/12/8

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)
    • Computer Science Applications
    • Information Systems
    • Hardware and Architecture
    • Education

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