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Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm

Pereira, Filipe Dwan; Oliveira, Elaine H.T.; Fernandes, David; Cristea, Alexandra

Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm Thumbnail


Authors

Filipe Dwan Pereira

Elaine H.T. Oliveira

David Fernandes



Abstract

Many researchers have started extracting student behaviour by cleaning data collected from web environments and using it as features in machine learning (ML) models. Using log data collected from an online judge, we have compiled a set of successful features correlated with the student grade and applying them on a database representing 486 CS1 students. We used this set of features in ML pipelines which were optimised, featuring a combination of an automated approach with an evolutionary algorithm and hyperparameter-tuning with random search. As a result, we achieved an accuracy of 75.55%, using data from only the first two weeks to predict the student final grades. We show how our pipeline outperforms state-of-the-art work on similar scenarios.

Citation

Pereira, F. D., Oliveira, E. H., Fernandes, D., & Cristea, A. (2019). Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm. . https://doi.org/10.1109/icalt.2019.00066

Presentation Conference Type Conference Paper (Published)
Conference Name 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)
Start Date Jul 15, 2019
End Date Jul 18, 2019
Online Publication Date Sep 2, 2019
Publication Date 2019
Deposit Date Nov 9, 2021
Publicly Available Date Nov 9, 2021
Publisher Institute of Electrical and Electronics Engineers
DOI https://doi.org/10.1109/icalt.2019.00066
Public URL https://durham-repository.worktribe.com/output/1138994

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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.






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