Literaturnachweis - Detailanzeige
| Autor/inn/en | Luan, Hui; Tsai, Chin-Chung |
|---|---|
| Titel | A Review of Using Machine Learning Approaches for Precision Education |
| Quelle | In: Educational Technology & Society, 24 (2021) 1, S.250-266 (17 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1436-4522 |
| Schlagwörter | Artificial Intelligence; Individualized Instruction; Individual Differences; Educational Trends; Educational Research; Blended Learning; Electronic Learning; College Students; Computer Science Education; STEM Education; Outcomes of Education; Evaluation Methods |
| Abstract | In recent years, in the field of education, there has been a clear progressive trend toward precision education. As a rapidly evolving AI technique, machine learning is viewed as an important means to realize it. In this paper, we systematically review 40 empirical studies regarding machine-learning-based precision education. The results showed that the majority of studies focused on the prediction of learning performance or dropouts, and were carried out in online or blended learning environments among university students majoring in computer science or STEM, whereas the data sources were divergent. The commonly used machine learning algorithms, evaluation methods, and validation approaches are presented. The emerging issues and future directions are discussed accordingly. (As Provided). |
| Anmerkungen | International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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