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

Ryosuke Ishizue, Kazunori Sakamoto, Hironori Washizaki, Yoshiaki Fukazawa

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

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
    EditorsAhmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen
    PublisherAsia-Pacific Society for Computers in Education
    Pages40-49
    Number of pages10
    ISBN (Print)9789869401265
    Publication statusPublished - 2017 Jan 1
    Event25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
    Duration: 2017 Dec 42017 Dec 8

    Other

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

    Keywords

    • Machine-learning
    • Placement
    • Programming class
    • Psychological scale

    ASJC Scopus subject areas

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

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  • Cite this

    Ishizue, R., Sakamoto, K., Washizaki, H., & Fukazawa, Y. (2017). Student placement predictor for programming class using classes attitude, psychological scale, and code metrics. In A. F. Mohd Ayub, A. Mitrovic, J-C. Yang, S. L. Wong, & W. Chen (Eds.), Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings (pp. 40-49). Asia-Pacific Society for Computers in Education.