Classification of Learner Retention using Machine Learning Approaches
Learner retention issues require a huge commitment from a university as the process of monitoring learners' re-registration status from the beginning of each semester until they graduate can be quite tedious. When the number of learners who re-register for a subsequent semester is low, it not o...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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2021
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| Online Access: | https://ieeexplore.ieee.org/abstract/document/9617055 |
| _version_ | 1848801698227683328 |
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| author | Nur Amalina Diyana Suhaimi , Norshaliza Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Masrom, Nurazean Maarop, |
| author_facet | Nur Amalina Diyana Suhaimi , Norshaliza Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Masrom, Nurazean Maarop, |
| author_sort | Nur Amalina Diyana Suhaimi , |
| building | OUM Institutional Repository |
| collection | Online Access |
| description | Learner retention issues require a huge commitment from a university as the process of monitoring learners' re-registration status from the beginning of each semester until they graduate can be quite tedious. When the number of learners who re-register for a subsequent semester is low, it not only affects the university's image but also its ranking and reputation in the education sector. Therefore, the university must identify, at an early stage, the likelihood of a learner is not retained in the following semester. This study proposed to experiment with the classification methods for solving the issue of learner retention at Open University Malaysia by comparing three Supervised Machine Learning algorithms namely Logistic Regression, Support Vector Machine, and k-Nearest Neighbor. The performance of these algorithms was evaluated based on accuracy, precision, recall, and f-measure. It is determined that Support Vector Machine showed the best accuracy in classifying the learners' retention rate with 80% accuracy. The benefit of performing Machine Learning is that it enables the identification of at-risk learners at the earliest opportunity and therefore implement the earliest interventions to retain them. (Abstract by authors) |
| first_indexed | 2025-11-14T21:11:35Z |
| format | Conference or Workshop Item |
| id | oai:eprints.oum.edu.my:1469 |
| institution | Open University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:11:35Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:eprints.oum.edu.my:14692022-08-05T00:45:52Z Classification of Learner Retention using Machine Learning Approaches Nur Amalina Diyana Suhaimi , Norshaliza Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Masrom, Nurazean Maarop, LB1028 Research - Education LB2300 Higher Education Learner retention issues require a huge commitment from a university as the process of monitoring learners' re-registration status from the beginning of each semester until they graduate can be quite tedious. When the number of learners who re-register for a subsequent semester is low, it not only affects the university's image but also its ranking and reputation in the education sector. Therefore, the university must identify, at an early stage, the likelihood of a learner is not retained in the following semester. This study proposed to experiment with the classification methods for solving the issue of learner retention at Open University Malaysia by comparing three Supervised Machine Learning algorithms namely Logistic Regression, Support Vector Machine, and k-Nearest Neighbor. The performance of these algorithms was evaluated based on accuracy, precision, recall, and f-measure. It is determined that Support Vector Machine showed the best accuracy in classifying the learners' retention rate with 80% accuracy. The benefit of performing Machine Learning is that it enables the identification of at-risk learners at the earliest opportunity and therefore implement the earliest interventions to retain them. (Abstract by authors) 2021 Conference or Workshop Item PeerReviewed https://ieeexplore.ieee.org/abstract/document/9617055 Nur Amalina Diyana Suhaimi , and Norshaliza Kamaruddin, and Thirumeni T Subramaniam, and Nilam Nur Amir Sjarif, and Maslin Masrom, and Nurazean Maarop, (2021) Classification of Learner Retention using Machine Learning Approaches. In: 2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS), 25-26 October 2021, Johor Bahru, Malaysia. https://library.oum.edu.my/repository/1469/ |
| spellingShingle | LB1028 Research - Education LB2300 Higher Education Nur Amalina Diyana Suhaimi , Norshaliza Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Masrom, Nurazean Maarop, Classification of Learner Retention using Machine Learning Approaches |
| title | Classification of Learner Retention using Machine Learning Approaches |
| title_full | Classification of Learner Retention using Machine Learning Approaches |
| title_fullStr | Classification of Learner Retention using Machine Learning Approaches |
| title_full_unstemmed | Classification of Learner Retention using Machine Learning Approaches |
| title_short | Classification of Learner Retention using Machine Learning Approaches |
| title_sort | classification of learner retention using machine learning approaches |
| topic | LB1028 Research - Education LB2300 Higher Education |
| url | https://ieeexplore.ieee.org/abstract/document/9617055 https://ieeexplore.ieee.org/abstract/document/9617055 |