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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

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Filipe D. Pereira

Elaine Oliveira

David Fernandes

Luciano Silva

Gene Aguiar

Mohammad Alshehri


Seiji Isotani

Eva Millán

Amy Ogan

Peter Hastings

Bruce McLaren

Rose Luckin


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.


Pereira, F. D., Oliveira, E., Cristea, A., Fernandes, D., Silva, L., Aguiar, G., …Alshehri, M. (2019). Early Dropout Prediction for Programming Courses Supported by Online Judges. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), .

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
Volume 11626
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743,1611-3349
ISBN 978-3-030-23206-1
Public URL


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