Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course
Prior education research has focused on using learning analytics to predict the academic performance of Massive Online Learning Courses (MOOCs) and e- learning courses in universities. There is limited research on online learning that has been transitioned from physical classes and that has continue...
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| Formáid: | Alt |
| Teanga: | English |
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MDPI AG
2022-12-01
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| Sraith: | Education Sciences |
| Ábhair: | |
| Rochtain ar líne: | https://www.mdpi.com/2227-7102/12/12/935 |
| _version_ | 1827640566844751872 |
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| author | Tin Tin Ting Shi Lin Teh Mee Chin Wee |
| author_facet | Tin Tin Ting Shi Lin Teh Mee Chin Wee |
| author_sort | Tin Tin Ting |
| collection | DOAJ |
| description | Prior education research has focused on using learning analytics to predict the academic performance of Massive Online Learning Courses (MOOCs) and e- learning courses in universities. There is limited research on online learning that has been transitioned from physical classes and that has continued to use active learning approaches in an online environment. This study aims to determine the variables affecting students’ academic performance for a computing course in a research-intense university during the COVID-19 pandemic. Variables that are indicative of self-regulated learning such as time management, frequency of accessing learning materials and the Learning Management System (LMS), participation in assessment activities and discussions, and the results of formative assessments were extracted from the LMS reports and log files to predict the students’ total marks and final exam results. The findings revealed that good time management and active participation are important for academic success. The results also supported the model for the early prediction of summative assessment performance using formative assessment results. Additionally, this study concludes that the gap in predictive power between formative assessment results and online learning behaviors is small. This research is considered unique because it demonstrates predictive models for students’ academic success for an institution that was forced to transition from physical to online learning. It highlights the importance of self-regulated learning behavior and formative assessments in the contemporary era. |
| first_indexed | 2024-03-09T17:01:29Z |
| format | Article |
| id | doaj.art-4982b94514a043c590a5dcb372dae3bf |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-09T17:01:29Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-4982b94514a043c590a5dcb372dae3bf2023-11-24T14:28:44ZengMDPI AGEducation Sciences2227-71022022-12-01121293510.3390/educsci12120935Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science CourseTin Tin Ting0Shi Lin Teh1Mee Chin Wee2Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, MalaysiaSchool of Information Technology, Monash University Malaysia, Bandar Sunway 47500, MalaysiaSchool of Information Technology, Monash University Malaysia, Bandar Sunway 47500, MalaysiaPrior education research has focused on using learning analytics to predict the academic performance of Massive Online Learning Courses (MOOCs) and e- learning courses in universities. There is limited research on online learning that has been transitioned from physical classes and that has continued to use active learning approaches in an online environment. This study aims to determine the variables affecting students’ academic performance for a computing course in a research-intense university during the COVID-19 pandemic. Variables that are indicative of self-regulated learning such as time management, frequency of accessing learning materials and the Learning Management System (LMS), participation in assessment activities and discussions, and the results of formative assessments were extracted from the LMS reports and log files to predict the students’ total marks and final exam results. The findings revealed that good time management and active participation are important for academic success. The results also supported the model for the early prediction of summative assessment performance using formative assessment results. Additionally, this study concludes that the gap in predictive power between formative assessment results and online learning behaviors is small. This research is considered unique because it demonstrates predictive models for students’ academic success for an institution that was forced to transition from physical to online learning. It highlights the importance of self-regulated learning behavior and formative assessments in the contemporary era.https://www.mdpi.com/2227-7102/12/12/935Learning Management Systemacademic performance predictionsummative assessmentsformative assessmentslearning behavior |
| spellingShingle | Tin Tin Ting Shi Lin Teh Mee Chin Wee Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course Education Sciences Learning Management System academic performance prediction summative assessments formative assessments learning behavior |
| title | Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course |
| title_full | Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course |
| title_fullStr | Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course |
| title_full_unstemmed | Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course |
| title_short | Ascertaining the Online Learning Behaviors and Formative Assessments Affecting Students’ Academic Performance during the COVID-19 Pandemic: A Case Study of a Computer Science Course |
| title_sort | ascertaining the online learning behaviors and formative assessments affecting students academic performance during the covid 19 pandemic a case study of a computer science course |
| topic | Learning Management System academic performance prediction summative assessments formative assessments learning behavior |
| url | https://www.mdpi.com/2227-7102/12/12/935 |
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