Naseem, Mohammed and Reddy, Emmenual and Nand, Ravneil (2021) Statistical methods for data mining Mathematics students' online presence. [Conference Proceedings]
Full text not available from this repository.Abstract
COVID-19 has caused major changes in every aspect of human endeavor, including efforts in the higher education sector. A sudden shift from face-to-face and blended settings to a completely online delivery mode has introduced changes to conventional teaching methods, and made learning rely heavily on technology and the Internet. Hence, students' online engagement with these tools has become even more important for their academic success. Therefore, there is a need to investigate the effects of various indicators of students' online presence on their academic performance. This paper explores the effectiveness of online presence in Higher Education Institutes, where COVID-19 has shifted the deliveries to online mode. The chosen indicator is frequency that will be adequately used to quantify the effectiveness of online presence on student performance in online mathematics courses. Statistical methods are used to measure the correlation and association between students' online presence indicators and their performance. As such, it would allow to build models to predict future outcomes or occurrences and student performances, with a major focus on mathematics and statistics courses. The results show that there is an increase in student online interaction in courses during COVID-19 era; however, it is consistent with the Online Measureable Presence Model (OMPM) model where frequency was the dominant indicator of student performance.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Emmenual Reddy |
| Date Deposited: | 18 Apr 2022 23:40 |
| Last Modified: | 06 Mar 2024 21:47 |
| URI: | https://repository.usp.ac.fj/id/eprint/13371 |
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