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
Event25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
Duration: 2017 Dec 42017 Dec 8

Publication series

NameProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings

Other

Other25th International Conference on Computers in Education, ICCE 2017
Country/TerritoryNew 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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