Student Engagement Dataset (SED): An Online Learning Activity Dataset
Distance learning has become a popular educational medium, and the Internet has spread since the early 2000s. To leverage this phenomenon, learning analytics and data mining can provide insights into improving pedagogy and assessing student engagement. To this end, a student-centric dataset was cons...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844083/ |
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| author | M. S. S. Kassim Z. H. Azizul A. A. H. Ahmad Fuaad |
| author_facet | M. S. S. Kassim Z. H. Azizul A. A. H. Ahmad Fuaad |
| author_sort | M. S. S. Kassim |
| collection | DOAJ |
| description | Distance learning has become a popular educational medium, and the Internet has spread since the early 2000s. To leverage this phenomenon, learning analytics and data mining can provide insights into improving pedagogy and assessing student engagement. To this end, a student-centric dataset was constructed by extracting data from Universiti Malaya’s Moodle-based Virtual Learning Environment (VLE), which serves approximately 25,000 students annually. In this paper, we present the Student Engagement Dataset (SED). The dataset consists of 16,609 students and 2,407 courses. It contains information such as grades and daily logged online activities (approximately 12 million data points), including temporal data across four tables. The tables include student engagement features created by aggregating raw activity data. Here, we present the dataset’s properties and describe the data collection, selection, and processing steps. Correlation analysis of student engagement features showed a statistically significant but weak negative correlation between the number of courses, early morning logins, assignments, and top students’ performance. SED is expected to present new opportunities for researchers in the learning analytics domain. |
| format | Article |
| id | doaj-art-20edd567ca444d1ab4dceec3bdce355c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-20edd567ca444d1ab4dceec3bdce355c2025-02-11T00:00:46ZengIEEEIEEE Access2169-35362025-01-0113236072361710.1109/ACCESS.2025.353110210844083Student Engagement Dataset (SED): An Online Learning Activity DatasetM. S. S. Kassim0https://orcid.org/0000-0003-1271-7888Z. H. Azizul1https://orcid.org/0000-0002-8314-6464A. A. H. Ahmad Fuaad2Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur, MalaysiaDistance learning has become a popular educational medium, and the Internet has spread since the early 2000s. To leverage this phenomenon, learning analytics and data mining can provide insights into improving pedagogy and assessing student engagement. To this end, a student-centric dataset was constructed by extracting data from Universiti Malaya’s Moodle-based Virtual Learning Environment (VLE), which serves approximately 25,000 students annually. In this paper, we present the Student Engagement Dataset (SED). The dataset consists of 16,609 students and 2,407 courses. It contains information such as grades and daily logged online activities (approximately 12 million data points), including temporal data across four tables. The tables include student engagement features created by aggregating raw activity data. Here, we present the dataset’s properties and describe the data collection, selection, and processing steps. Correlation analysis of student engagement features showed a statistically significant but weak negative correlation between the number of courses, early morning logins, assignments, and top students’ performance. SED is expected to present new opportunities for researchers in the learning analytics domain.https://ieeexplore.ieee.org/document/10844083/Learning analyticslearning management systems (LMSs)online learningvirtual learning environments (VLEs)student engagement |
| spellingShingle | M. S. S. Kassim Z. H. Azizul A. A. H. Ahmad Fuaad Student Engagement Dataset (SED): An Online Learning Activity Dataset IEEE Access Learning analytics learning management systems (LMSs) online learning virtual learning environments (VLEs) student engagement |
| title | Student Engagement Dataset (SED): An Online Learning Activity Dataset |
| title_full | Student Engagement Dataset (SED): An Online Learning Activity Dataset |
| title_fullStr | Student Engagement Dataset (SED): An Online Learning Activity Dataset |
| title_full_unstemmed | Student Engagement Dataset (SED): An Online Learning Activity Dataset |
| title_short | Student Engagement Dataset (SED): An Online Learning Activity Dataset |
| title_sort | student engagement dataset sed an online learning activity dataset |
| topic | Learning analytics learning management systems (LMSs) online learning virtual learning environments (VLEs) student engagement |
| url | https://ieeexplore.ieee.org/document/10844083/ |
| work_keys_str_mv | AT msskassim studentengagementdatasetsedanonlinelearningactivitydataset AT zhazizul studentengagementdatasetsedanonlinelearningactivitydataset AT aahahmadfuaad studentengagementdatasetsedanonlinelearningactivitydataset |