“A study of educational data mining: evidence from a Thai university,”
Open accessAbstract
Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.
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Generalizing performance prediction to non-traditional modes of learning such as hybrid or blended learning and online learning has benefited from the inclusion of additional information sources such as web-browsing information, discussion forum activity and student study habits.
“The process of generalizing performance prediction to non-traditional modes of learning such as hybrid or blended learning and on-line learning has benefitted from the inclusion of additional information sources such as web-browsing information (Trakunphutthirak et al., 2019), discussion forum activity and student study habits (Gitinabard et al., 2019).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Trakunphutthirak et al., 2019
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Proactive and reactive engagement of artificial intelligence methods for education: a review
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- Authors
- Trakunphutthirak, R., Cheung, Y., & Lee, V. C. S.
- Affiliations
- Monash University (AU)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 734-741
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2904669168
- Cited by
- 13
- References
- 19
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 4 · 2022: 1 · 2021: 3 · 2020: 2
- FWCI
- 1.74
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- Monash University Research Portal (Monash University) (publishedVersion, other-oa)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Outcomes and Influences
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- educational data mining, student performance prediction, at-risk students, time management, random forest, Thai university students
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