利用人工智能预测年轻学习者的在线学习参与度

IF 1.2 Q2 EDUCATION & EDUCATIONAL RESEARCH
Xiaoqiu Xu, Deborah M. Dugdale, Xin Wei, Wenjuan Mi
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引用次数: 3

摘要

摘要在过去的十年里,在线语言学习服务的激增使第二语言学习者受益匪浅。然而,对于学习者,尤其是年轻学习者,是否参与在线学习,以及教育工作者如何提高在线学习体验的参与度,人们缺乏了解。这项研究考察了一个人工智能(AI)驱动的自动化系统,该系统使用语音和面部识别在一对一的25分钟在线英语课上实时跟踪教师和学习者的语音、面部表情和互动。每个学习者在网课后72小时内完成了一项学习者参与度调查。结果表明,在这种一对一的在线学习环境中,年轻学习者的参与度很高(平均值=4.5,满分5)。学习者的正面暴露(表明他们在课堂上的注意力)和英语水平是学习者参与度的重要而积极的预测因素。教师的总讲话时长和教学时间在预测学习者参与度方面趋于显著。讨论了教育意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging Artificial Intelligence to Predict Young Learner Online Learning Engagement
ABSTRACT The recent surge of online language learning services in the past decade has benefitted second language learners. However, there is a lack of understanding of whether learners, especially young learners, are engaged in online learning, and how educators can enhance the engagement of the online learning experience. This study examines an artificial intelligence (AI)- powered automated system that uses voice and facial recognition to track both teacher and learner speech, facial expressions, and interactions in real-time in a one-to-one 25-minute online English class. Each learner completed a learner engagement survey within 72 hours of the online class. Results demonstrated that young learners were highly engaged during this one-to-one online learning setting (mean = 4.5, out of 5). Learners’ frontal face exposure (indicating their attentiveness during class) and English proficiency levels are significant and positive predictors of learner engagement. Teachers’ total length of speech and instructional time tended toward significance in predicting learner engagement. Educational implications are discussed.
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来源期刊
American Journal of Distance Education
American Journal of Distance Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
7.20
自引率
3.10%
发文量
30
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