TY - GEN
T1 - Student placement predictor for programming class using classes attitude, psychological scale, and code metrics
AU - Ishizue, Ryosuke
AU - Sakamoto, Kazunori
AU - Washizaki, Hironori
AU - Fukazawa, Yoshiaki
N1 - Publisher Copyright:
© 2017 Asia-Pacific Society for Computers in Education. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Machine-learning
KW - Placement
KW - Programming class
KW - Psychological scale
UR - http://www.scopus.com/inward/record.url?scp=85053920941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053920941&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85053920941
SN - 9789869401265
T3 - Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
SP - 40
EP - 49
BT - Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
A2 - Mohd Ayub, Ahmad Fauzi
A2 - Mitrovic, Antonija
A2 - Yang, Jie-Chi
A2 - Wong, Su Luan
A2 - Chen, Wenli
PB - Asia-Pacific Society for Computers in Education
T2 - 25th International Conference on Computers in Education, ICCE 2017
Y2 - 4 December 2017 through 8 December 2017
ER -