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

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, July). Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm. Presented at 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil

• 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