Early Dropout Prediction for Programming Courses Supported by Online Judges

Pereira, Filipe D.; Oliveira, Elaine; Cristea, Alexandra; Fernandes, David; Silva, Luciano; Aguiar, Gene; Alamri, Ahmed; Alshehri, Mohammad

Abstract

Many educational institutions have been using online judges in programming classes, amongst others, to provide faster feedback for students and to reduce the teacher’s workload. There is some evidence that online judges also help in reducing dropout. Nevertheless, there is still a high level of dropout noticeable in introductory programming classes. In this sense, the objective of this work is to develop and validate a method for predicting student dropout using data from the first two weeks of study, to allow for early intervention. Instead of the classical questionnaire-based method, we opted for a non-subjective, data-driven approach. However, such approaches are known to suffer from a potential overload of factors, which may not all be relevant to the prediction task. As a result, we reached a very promising 80% of accuracy, and performed explicit extraction of the main factors leading to student dropout.

Citation

Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., Alamri, A., & Alshehri, M. (2019, June). Early Dropout Prediction for Programming Courses Supported by Online Judges. Presented at AIED 2019, Chicago, IL

• Presentation Conference Type: Conference Paper (published)
• Conference Name: AIED 2019
• Start Date: Jun 25, 2019
• End Date: Jun 29, 2019
• Publication Date: 2019
• Deposit Date: Sep 19, 2019
• Publicly Available Date: Nov 9, 2021
• Print ISSN: 0302-9743
• Volume: 11626
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743,1611-3349
• ISBN: 978-3-030-23206-1
• DOI: https://doi.org/10.1007/978-3-030-23207-8_13
• Public URL: https://durham-repository.worktribe.com/output/1143406