THE EXPLORATION OF A MACHINE LEARNING APPROACH FOR THE ASSESSMENT OF LEARNING STYLES CHANGES

Yueer Wei, Qingxia Yang, Jiangping Chen, and Jie Hu

Keywords

Learning styles, machine learning, support vector machine

Abstract

Student’s individual learning style contributes significantly to the improvement of their learning efficiency. However, the changes in the learning style of English as the second/foreign language (ESL/EFL) undergraduates among different grades are still unknown. This research paper explored the changes in the learning style among Foreign Languages major undergraduates in various grades. Firstly, the index of learning styles was selected to measure their learning styles as data collection instrument. Secondly, a machine learning approach (support vector machine) was utilized to assess changes in learning styles by degree of separation. As a result, the degree of separation between the first- and the fourth-year students was found to be substantially higher than that between the first-year students and students of other grades. Moreover, the third-year disciplinary study was identified as the key contributor to the changes of learning styles. In sum, as one kind of artificial intelligence techniques, the machine learning technique was explored for the first time and found to be effective for assessing the changes of learning styles in various grades in the ESL/EFL context.

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