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

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

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 st...

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Main Authors:	Muin, Siti Maisara Murniyati, Sidi, Fatimah, Ishak, Iskandar, Ibrahim, Hamidah, Affendey, Lilly Suriani, Daud, Mohd Faizal, Abdul, Syemsul Bahrim, Abu Bakar, Rostam
Format:	Article
Published:	Auricle Global Society of Education and Research 2023
Online Access:	http://psasir.upm.edu.my/id/eprint/109145/ https://ijritcc.org/index.php/ijritcc/article/view/9178
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id	my.upm.eprints.109145
record_format	eprints
spelling	my.upm.eprints.1091452024-08-27T07:42:48Z http://psasir.upm.edu.my/id/eprint/109145/ Predicting academic student performance based on e-learning platform engagement using learning management system data Muin, Siti Maisara Murniyati Sidi, Fatimah Ishak, Iskandar Ibrahim, Hamidah Affendey, Lilly Suriani Daud, Mohd Faizal Abdul, Syemsul Bahrim Abu Bakar, Rostam 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. Auricle Global Society of Education and Research 2023 Article PeerReviewed 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 https://ijritcc.org/index.php/ijritcc/article/view/9178 10.17762/ijritcc.v11i9.9178
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	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.
format	Article
author	Muin, Siti Maisara Murniyati Sidi, Fatimah Ishak, Iskandar Ibrahim, Hamidah Affendey, Lilly Suriani Daud, Mohd Faizal Abdul, Syemsul Bahrim Abu Bakar, Rostam
spellingShingle	Muin, Siti Maisara Murniyati Sidi, Fatimah Ishak, Iskandar Ibrahim, Hamidah Affendey, Lilly Suriani Daud, Mohd Faizal Abdul, Syemsul Bahrim Abu Bakar, Rostam Predicting academic student performance based on e-learning platform engagement using learning management system data
author_facet	Muin, Siti Maisara Murniyati Sidi, Fatimah Ishak, Iskandar Ibrahim, Hamidah Affendey, Lilly Suriani Daud, Mohd Faizal Abdul, Syemsul Bahrim Abu Bakar, Rostam
author_sort	Muin, Siti Maisara Murniyati
title	Predicting academic student performance based on e-learning platform engagement using learning management system data
title_short	Predicting academic student performance based on e-learning platform engagement using learning management system data
title_full	Predicting academic student performance based on e-learning platform engagement using learning management system data
title_fullStr	Predicting academic student performance based on e-learning platform engagement using learning management system data
title_full_unstemmed	Predicting academic student performance based on e-learning platform engagement using learning management system data
title_sort	predicting academic student performance based on e-learning platform engagement using learning management system data
publisher	Auricle Global Society of Education and Research
publishDate	2023
url	http://psasir.upm.edu.my/id/eprint/109145/ https://ijritcc.org/index.php/ijritcc/article/view/9178
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score	13.211869

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

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