Predicting student success for programming courses in a fully online learning environment
Date
2020
Authors
Bretana, N.A.
Robati, M.
Rawat, A.
Pandey, A.
Khatri, S.
Kaushal, K.
Nair, S.
Abadia, R.
Cheang, G.
Editors
So, H.J.
Rodrigo, M.M.
Mason, J.
Mitrovic, A.
Rodrigo, M.M.
Mason, J.
Mitrovic, A.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
28th International Conference on Computers in Education (ICCE 2020). Proceedings, 2020 / So, H.J., Rodrigo, M.M., Mason, J., Mitrovic, A. (ed./s), vol.1, pp.47-56
Statement of Responsibility
Conference Name
ICCE 2020: 28th International Conference on Computers in Education (23 Nov 2020 - 27 Nov 2020 : Online)
Abstract
The emergence of online learning environments is important for teaching programming courses. In this study, demographic and performance-related data from two programming courses of a fully online learning platform, UniSA Online, were explored. Statistically significant features were identified using Varian Inflation Factor and Chi-Square test. Four prediction models were trained and tested using four sets of features: demographic, performance, statistically significant features, and all available features. The model trained using demographic features yielded an accuracy of 45.45%. The models trained usind performance-related features, statistically significant features, and all features yielded an accuracy of 86.86%, 86.53%, and 86.53%, respectively. This highlights the importance of performance-related data in predicting student success outcomes in learning programming via a fully online learning environment.
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Dissertation Note
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Copyright 2020 Asia-Pacific Society for Computers in Education