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

Pereira, Filipe Dwan and Oliveira, Elaine H. T. and Fernandes, David and Cristea, Alexandra (2019) 'Early Performance Prediction for CS1 Course Students using a Combination of Machine Learning and an Evolutionary Algorithm.', 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) Maceio, Brazil, 15-18 July 2019.

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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/ICALT.2019.00066
Publisher statement:© 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.
Date accepted:No date available
Date deposited:09 November 2021
Date of first online publication:02 September 2019
Date first made open access:09 November 2021

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