Nur Amalina Diyana Suhaimi, Norshaliza Binti Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Bte Masrom, Nurazean Maarop
{"title":"使用机器学习方法的学习者保留率分类","authors":"Nur Amalina Diyana Suhaimi, Norshaliza Binti Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Bte Masrom, Nurazean Maarop","doi":"10.1109/ICRIIS53035.2021.9617055","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":269873,"journal":{"name":"2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Classification of Learner Retention using Machine Learning Approaches\",\"authors\":\"Nur Amalina Diyana Suhaimi, Norshaliza Binti Kamaruddin, Thirumeni T Subramaniam, Nilam Nur Amir Sjarif, Maslin Bte Masrom, Nurazean Maarop\",\"doi\":\"10.1109/ICRIIS53035.2021.9617055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":269873,\"journal\":{\"name\":\"2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRIIS53035.2021.9617055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th International Conference on Research and Innovation in Information Systems (ICRIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRIIS53035.2021.9617055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.