Effects of AI Affordances on Student Engagement in EFL Classrooms: A Structural Equation Modelling and Latent Profile Analysis

IF 2.8 3区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

European Journal of Education Pub Date : 2024-10-16 DOI:10.1111/ejed.12808

Jinfen Xu, Juan Li

{"title":"Effects of AI Affordances on Student Engagement in EFL Classrooms: A Structural Equation Modelling and Latent Profile Analysis","authors":"Jinfen Xu, Juan Li","doi":"10.1111/ejed.12808","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Various AI technologies have been extensively introduced in language learning, showing positive impacts on students' learning, especially on their classroom-based engagement. Yet, AI's comprehensive affordances as well as influences across different cohorts of student engagement remain underexplored. Given this, the current study, employing structural equation modelling (SEM), delineated the factor structures and predictive relationships of AI affordances and student engagement. Besides, to clarify the variations across different engagement subgroups, the study also explored latent profiles of student engagement and their moderating effects through latent profile analysis (LPA). SEM and LPA were conducted using AMOS 23 and Mplus 8, respectively. The participants comprised 408 undergraduate students from various universities in China, who have engaged in English as a Foreign Language (EFL) learning within AI-empowered classroom environments. Factor analysis indicated that both AI affordances and student engagement exhibited two second-order factor structures. AI affordances were categorised into four dimensions: convenience, interactivity, personalisation and social presence. Student engagement was also divided into four dimensions: cognitive, behavioural, emotional and social engagement. Additionally, AI affordances significantly affected student engagement, with this impact being moderated by different student engagement profiles. Student engagement was segmented into three sub-groups: non/low engagement, high engagement and moderate engagement. Therein, AI affordances showed a notable effect on the non-/low engagement group. These findings provide a solid foundation for future research in the integration of AI technologies with language learning.</p>\n </div>","PeriodicalId":47585,"journal":{"name":"European Journal of Education","volume":"59 4","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Education","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ejed.12808","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}

引用次数: 0

Abstract

Various AI technologies have been extensively introduced in language learning, showing positive impacts on students' learning, especially on their classroom-based engagement. Yet, AI's comprehensive affordances as well as influences across different cohorts of student engagement remain underexplored. Given this, the current study, employing structural equation modelling (SEM), delineated the factor structures and predictive relationships of AI affordances and student engagement. Besides, to clarify the variations across different engagement subgroups, the study also explored latent profiles of student engagement and their moderating effects through latent profile analysis (LPA). SEM and LPA were conducted using AMOS 23 and Mplus 8, respectively. The participants comprised 408 undergraduate students from various universities in China, who have engaged in English as a Foreign Language (EFL) learning within AI-empowered classroom environments. Factor analysis indicated that both AI affordances and student engagement exhibited two second-order factor structures. AI affordances were categorised into four dimensions: convenience, interactivity, personalisation and social presence. Student engagement was also divided into four dimensions: cognitive, behavioural, emotional and social engagement. Additionally, AI affordances significantly affected student engagement, with this impact being moderated by different student engagement profiles. Student engagement was segmented into three sub-groups: non/low engagement, high engagement and moderate engagement. Therein, AI affordances showed a notable effect on the non-/low engagement group. These findings provide a solid foundation for future research in the integration of AI technologies with language learning.

查看原文本刊更多论文

AI Affordances 对 EFL 课堂中学生参与的影响：结构方程建模和潜在特征分析

各种人工智能技术已被广泛引入语言学习，对学生的学习，尤其是课堂参与产生了积极影响。然而，人工智能的综合能力以及对不同群体学生参与度的影响仍未得到充分探索。有鉴于此，本研究采用结构方程建模（SEM）方法，划分了人工智能能力与学生参与度的因子结构和预测关系。此外，为了澄清不同参与度亚群之间的差异，本研究还通过潜在特征分析（LPA）探讨了学生参与度的潜在特征及其调节作用。SEM 和 LPA 分别使用 AMOS 23 和 Mplus 8 进行。参与者包括来自中国不同高校的 408 名本科生，他们在人工智能赋能的课堂环境中参与了英语作为外语（EFL）的学习。因子分析结果表明，人工智能能力和学生参与都呈现出两个二阶因子结构。人工智能可负担性分为四个维度：便利性、互动性、个性化和社会存在。学生参与度也分为四个维度：认知参与度、行为参与度、情感参与度和社会参与度。此外，人工智能的可负担性对学生的参与度有显著影响，而这种影响会受到不同学生参与度情况的调节。学生参与度被分为三个子群体：非/低参与度、高参与度和中等参与度。其中，人工智能负担能力对非/低参与度组有明显影响。这些发现为今后研究人工智能技术与语言学习的整合奠定了坚实的基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

European Journal of Education EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

4.50

自引率

0.00%

发文量

期刊介绍： The prime aims of the European Journal of Education are: - To examine, compare and assess education policies, trends, reforms and programmes of European countries in an international perspective - To disseminate policy debates and research results to a wide audience of academics, researchers, practitioners and students of education sciences - To contribute to the policy debate at the national and European level by providing European administrators and policy-makers in international organisations, national and local governments with comparative and up-to-date material centred on specific themes of common interest.