- Title
- A review of machine learning methods used for educational data
- Creator
- Ersozlu, Zara; Taheri, Sona; Koch, Inge
- Relation
- Education and Information Technologies Vol. 29, p. 22125-22145
- Publisher Link
- http://dx.doi.org/10.1007/s10639-024-12704-0
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2024
- Description
- Integrating machine learning (ML) methods in educational research has the potential to greatly impact upon research, teaching, learning and assessment by enabling personalised learning, adaptive assessment and providing insights into student performance, progress and learning patterns. To reveal more about this notion, we investigated ML approaches used for educational data analysis in the last decade and provided recommendations for further research. Using a systematic literature review (SLR), we examined 77 publications from two large and high-impact databases for educational research using bibliometric mapping and evaluative review analysis. Our results suggest that the top five most frequently used keywords were similar in both databases. The majority of the publications (88%) utilised supervised ML approaches for predicting students’ performances and finding learning patterns. These methods include decision trees, support vector machines, random forests, and logistic regression. Semi-supervised learning methods were less frequently used, but also demonstrated promising results in predicting students’ performance. Finally, we discuss the implications of these results for statisticians, researchers, and policymakers in education.
- Subject
- machine learning methods; educational data analysis; transforming education research; systematic review
- Identifier
- http://hdl.handle.net/1959.13/1516110
- Identifier
- uon:56934
- Identifier
- ISSN:1360-2357
- Rights
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
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