Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning
Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This rese...
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| Format: | Article |
| Language: | English |
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Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)
2024-12-01
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| Series: | Journal of Applied Engineering and Technological Science |
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| Online Access: | https://journal.yrpipku.com/index.php/jaets/article/view/6128 |
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| _version_ | 1846116949375844352 |
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| author | Chandrakumar Thangavel Valliammai S E Amritha P. P Karthik Chandran Subrata Chowdhury Nguyen Thi Thu Bo Quoc Bao Duc-Tan Tran Duc-Nghia Tran Do Quang Trang |
| author_facet | Chandrakumar Thangavel Valliammai S E Amritha P. P Karthik Chandran Subrata Chowdhury Nguyen Thi Thu Bo Quoc Bao Duc-Tan Tran Duc-Nghia Tran Do Quang Trang |
| author_sort | Chandrakumar Thangavel |
| collection | DOAJ |
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Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.
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| format | Article |
| id | doaj-art-8ed8cf08eb5e4309bba4c8a0432eab8c |
| institution | Kabale University |
| issn | 2715-6087 2715-6079 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI) |
| record_format | Article |
| series | Journal of Applied Engineering and Technological Science |
| spelling | doaj-art-8ed8cf08eb5e4309bba4c8a0432eab8c2024-12-18T12:30:54ZengYayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)Journal of Applied Engineering and Technological Science2715-60872715-60792024-12-016110.37385/jaets.v6i1.6128Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning Chandrakumar Thangavel Valliammai S E0Amritha P. P1Karthik Chandran2Subrata Chowdhury3Nguyen Thi Thu4Bo Quoc Bao5Duc-Tan Tran6Duc-Nghia Tran7Do Quang Trang8Thiagarajar College of EngineeringThiagarajar College of EngineeringJyothi Engineering CollegeSri Venkateswara College of Engineering &Technology (A)Hanoi University of IndustryHanoi University of IndustryPhenikaa UniversityVietnam Academy of Science and TechnologyVietnam National Post and Telecommunication Group Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources. https://journal.yrpipku.com/index.php/jaets/article/view/6128COVID-19MoodleOnline LearningManagement SystemMachine Learning |
| spellingShingle | Chandrakumar Thangavel Valliammai S E Amritha P. P Karthik Chandran Subrata Chowdhury Nguyen Thi Thu Bo Quoc Bao Duc-Tan Tran Duc-Nghia Tran Do Quang Trang Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning Journal of Applied Engineering and Technological Science COVID-19 Moodle Online Learning Management System Machine Learning |
| title | Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning |
| title_full | Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning |
| title_fullStr | Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning |
| title_full_unstemmed | Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning |
| title_short | Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning |
| title_sort | students activeness measure in moodle learning management system using machine learning |
| topic | COVID-19 Moodle Online Learning Management System Machine Learning |
| url | https://journal.yrpipku.com/index.php/jaets/article/view/6128 |
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