Differentiating the learning styles of college students in different disciplines in a college English blended learning setting
| Autor: | Xueliang Chen, Jie Hu, Hangyan Yu, Yi Peng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Questionnaires
Male Support Vector Machine Social Sciences Machine Learning Learning styles Learning and Memory Mathematical and Statistical Techniques Sociology Feature (machine learning) Psychology Language Schools Multidisciplinary Applied Mathematics Simulation and Modeling Statistics Professions Research Design Physical Sciences Medicine Female Algorithms Research Article Adult College English Computer and Information Sciences Adolescent Universities Science Colleges Research and Analysis Methods Education Human Learning Machine Learning Algorithms Young Adult Artificial Intelligence Support Vector Machines Taxonomy (general) Mathematics education Learning Humans Statistical Methods Students Analysis of Variance Survey Research Cognitive Psychology Biology and Life Sciences Educational psychology Teachers Support vector machine Blended learning People and Places Individual learning Cognitive Science Population Groupings Mathematics Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 5, p e0251545 (2021) |
| 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: | OpenAIRE |
| Externí odkaz: |
|