Predicting Student Success for Programming Courses in a Fully Online Learning Environment

Authors

  • Neil Arvin BRETANA UniSA Online, University of South Australia, Australia Author
  • Mehdi ROBATI UniSA Online, University of South Australia, Australia Author
  • Aastha RAWAT UniSA STEM, University of South Australia, Australia Author
  • Aashi PANDEY UniSA STEM, University of South Australia, Australia Author
  • Shreya KHATRI UniSA STEM, University of South Australia, Australia Author
  • Kritika KAUSHAL UniSA STEM, University of South Australia, Australia Author
  • Sidarth NAIR UniSA STEM, University of South Australia, Australia Author
  • Gerald CHEANG UniSA STEM, University of South Australia, Australia Author
  • Rhoda ABADIA Author

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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Published

2020-11-23

How to Cite

Predicting Student Success for Programming Courses in a Fully Online Learning Environment . (2020). International Conference on Computers in Education, 47-56. https://library.apsce.net/index.php/ICCE/article/view/3896