TY - GEN
T1 - Machine Learning Model for Analyzing Learning Situations in Programming Learning
AU - Kawaguchi, Shota
AU - Sato, Yoshiki
AU - Nakayama, Hiroki
AU - Onuma, Ryo
AU - Nakamura, Shoichi
AU - Miyadera, Youzou
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/29
Y1 - 2019/1/29
N2 - In programming learning, students have individual difficulties, and teachers need to grasp those difficulties and provide appropriate support for the students. However, since it is a heavy burden for teachers, a method to automatically estimate the learning situations of students is required. In this research, we developed a method that adopts the development of a machine learning model as an approach to achieve this purpose. This machine learning model outputs the estimated learning situation when the source code editing history of new students is input. As a result of evaluating the developed method, it was possible to estimate the correct learning situations with high accuracy of 98%. The applicability of this learning situation estimation method in practical lessons was shown.
AB - In programming learning, students have individual difficulties, and teachers need to grasp those difficulties and provide appropriate support for the students. However, since it is a heavy burden for teachers, a method to automatically estimate the learning situations of students is required. In this research, we developed a method that adopts the development of a machine learning model as an approach to achieve this purpose. This machine learning model outputs the estimated learning situation when the source code editing history of new students is input. As a result of evaluating the developed method, it was possible to estimate the correct learning situations with high accuracy of 98%. The applicability of this learning situation estimation method in practical lessons was shown.
KW - Education Support
KW - Learning Situations Estimation
KW - Machine Learning
KW - Programming Learning
KW - Source Code Editing History
UR - http://www.scopus.com/inward/record.url?scp=85062785221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062785221&partnerID=8YFLogxK
U2 - 10.1109/ICBDAA.2018.8629776
DO - 10.1109/ICBDAA.2018.8629776
M3 - Conference contribution
AN - SCOPUS:85062785221
T3 - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
SP - 74
EP - 79
BT - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
Y2 - 21 November 2018 through 22 November 2018
ER -