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Early Dropout Prediction for Programming Courses Supported by Online Judges

Pereira, Filipe D. and Oliveira, Elaine and Cristea, Alexandra and Fernandes, David and Silva, Luciano and Aguiar, Gene and Alamri, Ahmed and Alshehri, Mohammad (2019) 'Early Dropout Prediction for Programming Courses Supported by Online Judges.', AIED 2019 Chicago, IL, 25-29 June 2019.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:The final authenticated version is available online at
Date accepted:No date available
Date deposited:09 November 2021
Date of first online publication:2019
Date first made open access:09 November 2021

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