Using Machine Learning to Identify Learner Styles across Devices in Online EFL Lessons

本研究的主要目的是审查英语作为外语（EFL）教育领域内的学习风格，利用机器学习（ML）来识别在线学生学习风格的变化。一个关键的中间阶段涉及理解设备的选择和一系列学习活动之间的相关性。这种理解一旦确立，将有助于更详细地审查各种内容提供模式。在整个2022学年，我们部署了各种定性编码技术和基于PCA的方法进行数据分析，同时还进一步研究了设备使用在教育中的作用，特别是分析了在线学习环境中学生的设备偏好。我们2022年研究的结果已在多个学术会议上发布，并发表了论文集，包括FIE 2022，EDUNINE 2022，EUROCALL 2022和LWMOOCS 2022。IEEE Transactions in Education的一个特刊专门介绍了这些会议记录之一的扩展版本。我们的初步研究结果也获得了接受的扩展出版物在同一个特殊问题的IEEE教育交易。此外，两本书的建议书已被批准出版：一本由Springer出版，另一本由IGI Global出版，后者涵盖了教育和工程领域的全面专业知识。

The primary objective of this research is to scrutinize learning styles within the sphere of English as a Foreign Language (EFL) education, utilizing Machine Learning (ML) to identify alterations in the learning styles of online students. A critical intermediate phase involves comprehending the correlation between the choice of device and an array of learning activities. Once established, this comprehension will facilitate a more detailed examination of various modes of content delivery. Throughout the academic year of 2022, we deployed an assortment of qualitative coding techniques and PCA-based methodologies for data analysis, while also furthering our investigation into the role of device usage in education, specifically analyzing student device preferences in an online learning environment.The findings derived from our 2022 research have been disseminated at multiple academic conferences with published proceedings, including FIE 2022, EDUNINE2022, EUROCALL 2022, and LWMOOCS 2022. A special issue of the IEEE Transactions in Education featured an extended version of one of these conference proceedings. Our preliminary findings also received acceptance for an extended publication in the same special issue of IEEE Transactions in Education. Additionally, two book proposals have been approved for publication: one by Springer and the second by IGI Global, with the latter featuring a comprehensive range of expertise from the fields of education and engineering.