Early Dropout Prediction in Online Learning of University using Machine Learning
Recently, most universities plan to open or open online learning courses, but the problem of dropout of online learning is still a problem for universities. Online learning has the advantage of being able to receive education anytime, anywhere, but it is true that the dropout rate is higher than o...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2021-12-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/732 |
| _version_ | 1827998663904854016 |
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| author | Hee Sun Park Seong Joon Yoo |
| author_facet | Hee Sun Park Seong Joon Yoo |
| author_sort | Hee Sun Park |
| collection | DOAJ |
| description | Recently, most universities plan to open or open online learning courses, but the problem of dropout of online learning is still a problem for universities. Online learning has the advantage of being able to receive education anytime, anywhere, but it is true that the dropout rate is higher than offline classes because you have to manage and control your own study time without the help of a professor or manager. Therefore, it is very important for professors and managers to support students in a timely act to avoid the risk of dropout of university online classes. This study used the access log data recorded in the Learning Management System (LMS) and the learner's statistical information and calculated data, and aims to present predictive algorithms suitable for online learning dropout early prediction systems at universities. This study features a 7-year online learning history log data recorded in the Cyber University LMS system to overcome the data count limitations of existing studies and predict the risk of drop-out during the learning period. The characteristics of the data you utilized were used to validate the availability of predictive models by applying learner statistical information, number of system connections, number of lectures, previous semester grade data, machine learning based decision tree, arbitrary forest (RF), support vector machine (SVM) and deep learning (DNN). Studies show that random forest (RF) algorithms have the best prediction and performance, and deep learning algorithms also apply to learning management (LMS) systems. |
| first_indexed | 2024-04-10T05:47:32Z |
| format | Article |
| id | doaj.art-76a00939a21549abad96fcc208ce5a67 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:32Z |
| publishDate | 2021-12-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-76a00939a21549abad96fcc208ce5a672023-03-05T10:30:14ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042021-12-015434735310.30630/joiv.5.4.732289Early Dropout Prediction in Online Learning of University using Machine LearningHee Sun Park0Seong Joon Yoo1Department of Computer Science, Sejong University, 209 Neungdong-ro Gwanging-gu , Korea ,Seoul, 05006, South KoreaDepartment of Computer Science, Sejong University, 209 Neungdong-ro Gwanging-gu , Korea ,Seoul, 05006, South KoreaRecently, most universities plan to open or open online learning courses, but the problem of dropout of online learning is still a problem for universities. Online learning has the advantage of being able to receive education anytime, anywhere, but it is true that the dropout rate is higher than offline classes because you have to manage and control your own study time without the help of a professor or manager. Therefore, it is very important for professors and managers to support students in a timely act to avoid the risk of dropout of university online classes. This study used the access log data recorded in the Learning Management System (LMS) and the learner's statistical information and calculated data, and aims to present predictive algorithms suitable for online learning dropout early prediction systems at universities. This study features a 7-year online learning history log data recorded in the Cyber University LMS system to overcome the data count limitations of existing studies and predict the risk of drop-out during the learning period. The characteristics of the data you utilized were used to validate the availability of predictive models by applying learner statistical information, number of system connections, number of lectures, previous semester grade data, machine learning based decision tree, arbitrary forest (RF), support vector machine (SVM) and deep learning (DNN). Studies show that random forest (RF) algorithms have the best prediction and performance, and deep learning algorithms also apply to learning management (LMS) systems.https://joiv.org/index.php/joiv/article/view/732dropout predictiononline learningmachine learningdeep learning. |
| spellingShingle | Hee Sun Park Seong Joon Yoo Early Dropout Prediction in Online Learning of University using Machine Learning JOIV: International Journal on Informatics Visualization dropout prediction online learning machine learning deep learning. |
| title | Early Dropout Prediction in Online Learning of University using Machine Learning |
| title_full | Early Dropout Prediction in Online Learning of University using Machine Learning |
| title_fullStr | Early Dropout Prediction in Online Learning of University using Machine Learning |
| title_full_unstemmed | Early Dropout Prediction in Online Learning of University using Machine Learning |
| title_short | Early Dropout Prediction in Online Learning of University using Machine Learning |
| title_sort | early dropout prediction in online learning of university using machine learning |
| topic | dropout prediction online learning machine learning deep learning. |
| url | https://joiv.org/index.php/joiv/article/view/732 |
| work_keys_str_mv | AT heesunpark earlydropoutpredictioninonlinelearningofuniversityusingmachinelearning AT seongjoonyoo earlydropoutpredictioninonlinelearningofuniversityusingmachinelearning |