Predicting non-completion of programming exercises using action logs and keystrokes
Publication Type
Conference Proceeding Article
Publication Date
7-2019
Abstract
Computing programming skills is gaining importance but yet it is not an easy subject to master as exemplified in the high attribution rates of computer science courses. It is thus critical that we identify learners who struggle with computer programming at opportune moments to sustain and support their learning. In this study, the keystrokes and actions of learners are captured in real-time in a Java programming tutoring system and used to predict the possibility of non-completion of programming exercises on a granular basis. The results indicate that this can be detected at an adequate accuracy with the proposed feature engineering and machine learning techniques. The use of keystrokes and action logs is significant as they are unobtrusive and easy to set up and thus can easily be propagated for use in any learning environment.
Keywords
Keystrokes; Learning; Machine learning; Programming
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2019 International Symposium on Educational Technology, Hradec Kralove, Czech, July 2-4
First Page
271
Last Page
275
ISBN
9781728133874
Identifier
10.1109/ISET.2019.00064
Publisher
IEEE
City or Country
New York
Citation
FWA, Hua Leong.
Predicting non-completion of programming exercises using action logs and keystrokes. (2019). Proceedings of the 2019 International Symposium on Educational Technology, Hradec Kralove, Czech, July 2-4. 271-275.
Available at: https://ink.library.smu.edu.sg/sis_research/6906