Based on data for students from 46 disciplines, this research delves into the effects of a massive quantity of variables related to students’ learning styles with the help of a powerful machine learning algorithm. Second, it introduces a novel machine learning method named the SVM to the field of education to identify an optimal set of factors that can simultaneously differentiate students of different academic disciplines.
Students across 46 disciplines were enrolled in this course, providing numerous disciplinary factor resources for investigating learning styles. First, it investigates how disciplinary background impacts students’ learning styles in a blended learning environment based on data collected in a compulsory college English course. In an effort to address this gap, this study aims to achieve two major objectives. However, knowledge of the learning styles of students from different disciplines in blended learning environments is limited. Researchers in recent years have explored students’ learning styles from various perspectives. Knowing a student’s learning style and personalizing instruction to students’ learning style could enhance their satisfaction, improve their academic performance, and even reduce the time necessary to learn.
Theoretically, learning style provides a window into students’ learning processes, predicts students’ learning outcomes, and plays a critical role in designing individualized instruction. Originally developed from the field of psychology, psychological classification, and cognitive research several decades ago, the term “learning style” is generally defined as the learner’s innate and individualized preference for ways of participation in learning practice. Learning style, as an integral and vital part of a student’s learning process, has been constantly discussed in the field of education and pedagogy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 with the recipient Jie Hu, Second Batch of 2019 Industry-University Collaborative Education Project of Chinese Ministry of Education with the recipient Jie Hu, SUPERB College English Action Plan with the recipient Jie Hu, and the Fundamental Research Funds for the Central Universities of Zhejiang University with the recipient Jie Hu.Ĭompeting interests: The authors have declared that no competing interests exist. Received: Accepted: ApPublished: May 20, 2021Ĭopyright: © 2021 Hu et al. PLoS ONE 16(5):Įditor: Haoran Xie, Lingnan University, HONG KONG
Citation: Hu J, Peng Y, Chen X, Yu H (2021) Differentiating the learning styles of college students in different disciplines in a college English blended learning setting.
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