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Predicting academic student performance based on e-learning platform engagement using learning management system data


Citation

Muin, Siti Maisara Murniyati and Sidi, Fatimah and Ishak, Iskandar and Ibrahim, Hamidah and Affendey, Lilly Suriani and Daud, Mohd Faizal and Abdul, Syemsul Bahrim and Abu Bakar, Rostam (2023) Predicting academic student performance based on e-learning platform engagement using learning management system data. International Journal on Recent and Innovation Trends in Computing and Communication, 11 (9). pp. 1859-1866. ISSN 2321-8169

Abstract

The identification of at-risk students has become increasingly more significant as these students are in the precarious position of failing their courses. This study aims to achieve the objective of proposing a student performance prediction model to identify the stage of the course where at-risk students (students with the highest potential of failing their courses) can be identified based on student information system and learning management system data. The proposed student performance prediction model leverages machine learning methods to predict at-risk students, combining data from Universiti Putra Malaysia’s (UPM) Student Information System (SIS) and learning managementsystem (PutraBlast). Two experiments were conducted to satisfy the objective. The first experiment uses the full semester data to test multiple machine learning models to identify the best model for this dataset. In the second experiment, the dataset was separated intofour course stages with four predictive models trained oneach stage. Students. Results show that GB outperforms other classifiers when trained on the full semester data. However, classifier performance decreases when trained on data from earlier stages of the course. Hence, based on theseresults, the earliest stage to predict at-risk students is identified to be the W1—W12 stage.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.17762/ijritcc.v11i9.9178
Publisher: Auricle Global Society of Education and Research
Keywords: Predicting student performance; Learning analytic; Learning management systems; E-learning
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 27 Aug 2024 07:42
Last Modified: 27 Aug 2024 07:42
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.17762/ijritcc.v11i9.9178
URI: http://psasir.upm.edu.my/id/eprint/109145
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