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Machine learning algorithms for early predicting dropout student online learning

Online learning is different from offline learning in the classroom with supervision from the lecturer. Online learning using the Learning Management System (LMS) media requires high awareness from students because their learning activities are not supervised, they are free to study wherever and whe...

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Main Authors: Dewi, Meta Amalya, Kurniadi, Felix Indra, Murad, Dina Fitria, Rabiha, Sucianna Ghadati, Awanis, Romli
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/41895/1/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout.pdf
http://umpir.ump.edu.my/id/eprint/41895/2/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout%20student%20online%20learning_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41895/
https://doi.org/10.1109/ICCED60214.2023.10425359
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spelling my.ump.umpir.418952024-08-30T00:14:17Z http://umpir.ump.edu.my/id/eprint/41895/ Machine learning algorithms for early predicting dropout student online learning Dewi, Meta Amalya Kurniadi, Felix Indra Murad, Dina Fitria Rabiha, Sucianna Ghadati Awanis, Romli QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Online learning is different from offline learning in the classroom with supervision from the lecturer. Online learning using the Learning Management System (LMS) media requires high awareness from students because their learning activities are not supervised, they are free to study wherever and whenever, so they need to manage and control their own study time without the help of lecturers or administrators. This is one of the causes of the high dropout rate among online learning students, so it is very important for lecturers and administrators to support students in a timely manner to avoid the risk of dropping out. This study uses access log data recorded in the LMS and student statistical information and calculated data and aims to present a suitable predictive algorithm for dropout early prediction systems for online learning students using machine learning. Of the 4 algorithms used, the highest recall value is in Naive Bayes (1), the highest precision is in Logistic Regression with Lasso (1), while the highest accuracy value (0.99) and F1score (0.97) are obtained from the Support Vector Machine which has value equal to Logistic Regression with Lasso. In general, the early dropout prediction model will allow lecturers and administrators to focus on students who have the potential to dropout and take quick action to improve their learning performance so as to reduce the number of student dropouts. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41895/1/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout.pdf pdf en http://umpir.ump.edu.my/id/eprint/41895/2/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout%20student%20online%20learning_ABS.pdf Dewi, Meta Amalya and Kurniadi, Felix Indra and Murad, Dina Fitria and Rabiha, Sucianna Ghadati and Awanis, Romli (2023) Machine learning algorithms for early predicting dropout student online learning. In: 2023 IEEE 9th International Conference on Computing, Engineering and Design, ICCED 2023. 9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023 , 7 - 8 November 2023 , Kuala Lumpur. pp. 1-4. (197271). ISBN 979-835037012-6 (Published) https://doi.org/10.1109/ICCED60214.2023.10425359
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Dewi, Meta Amalya
Kurniadi, Felix Indra
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
Machine learning algorithms for early predicting dropout student online learning
description Online learning is different from offline learning in the classroom with supervision from the lecturer. Online learning using the Learning Management System (LMS) media requires high awareness from students because their learning activities are not supervised, they are free to study wherever and whenever, so they need to manage and control their own study time without the help of lecturers or administrators. This is one of the causes of the high dropout rate among online learning students, so it is very important for lecturers and administrators to support students in a timely manner to avoid the risk of dropping out. This study uses access log data recorded in the LMS and student statistical information and calculated data and aims to present a suitable predictive algorithm for dropout early prediction systems for online learning students using machine learning. Of the 4 algorithms used, the highest recall value is in Naive Bayes (1), the highest precision is in Logistic Regression with Lasso (1), while the highest accuracy value (0.99) and F1score (0.97) are obtained from the Support Vector Machine which has value equal to Logistic Regression with Lasso. In general, the early dropout prediction model will allow lecturers and administrators to focus on students who have the potential to dropout and take quick action to improve their learning performance so as to reduce the number of student dropouts.
format Conference or Workshop Item
author Dewi, Meta Amalya
Kurniadi, Felix Indra
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
author_facet Dewi, Meta Amalya
Kurniadi, Felix Indra
Murad, Dina Fitria
Rabiha, Sucianna Ghadati
Awanis, Romli
author_sort Dewi, Meta Amalya
title Machine learning algorithms for early predicting dropout student online learning
title_short Machine learning algorithms for early predicting dropout student online learning
title_full Machine learning algorithms for early predicting dropout student online learning
title_fullStr Machine learning algorithms for early predicting dropout student online learning
title_full_unstemmed Machine learning algorithms for early predicting dropout student online learning
title_sort machine learning algorithms for early predicting dropout student online learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/41895/1/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout.pdf
http://umpir.ump.edu.my/id/eprint/41895/2/Machine%20learning%20algorithms%20for%20early%20predicting%20dropout%20student%20online%20learning_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41895/
https://doi.org/10.1109/ICCED60214.2023.10425359
_version_ 1822924593774460928
score 13.232432

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