| Autor: |
Jie Hu, Yi Peng, Xueliang Chen, Hangyan Yu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
PLoS ONE, Vol 16, Iss 5, p e0251545 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0251545 |
| Popis: |
Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan's taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students' discipline-specific learning styles in a college blended learning setting. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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