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

这项研究的主要目的是考察英语作为外语(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选择和一系列学习活动之间的关系。一旦建立了这种理解，将有助于对各种内容交付模式进行更详细的检查。在整个2022学年，我们部署了各种定性编码技术和基于主成分分析的方法来进行数据分析，同时还进一步调查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的学生设备偏好。我们2022年的研究成果已在多个学术会议上传播，并发表了会议记录，包括FIE 2022、EDUNINE2022、EUROCALL 2022和LWMOOCS 2022。IEEE教育学报的一期特刊介绍了其中一个会议论文集的加长版。我们的初步发现也被接受，在同一期IEEE教育学报特刊上发表了一篇长篇文章。此外，已批准出版两本书：一本由斯普林格出版，另一本由IGI Global出版，后者以教育和工程领域的全面专业知识为特色。

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.