AI-Enabled Learning Engagement Assessment for Smart Classrooms: Applications, Trends and Opportunities

DOI: https://doi.org/10.62517/jike.202404411

Author(s)

Liansuo Wei*, Fengxia Wang, Lijun Wan

Affiliation(s)

School of Information Engineering, Suqian University, Suqian, Jiangsu, China *Corresponding Author.

Abstract

The swift progress in Artificial Intelligence (AI) technology has significantly highlighted the role of smart classrooms in modern educational contexts. Despite this development, there remain notable challenges in the automatic assessment of students' engagement within smart classroom environments. To delve deeper into the historical context and trends regarding the evaluation of learning engagement, this study meticulously gathered and analyzed a substantial body of literature, comprising 1,281 publications indexed in the Social Sciences Citation Index (SSCI). These records, spanning from January 1, 1991, to October 1, 2023, were sourced from the Web of Science core database utilizing the search term "Evaluation of learning engagement." To visually interpret and map the findings, CiteSpace 6.2.R5 was employed to examine the volume of relevant research, the countries contributing to it, affiliated institutions, keyword correlations, temporal trends, and cited references. The analysis reveals a burgeoning interest in applying AI technologies to the evaluation of learning engagement within smart classrooms. Over the span of three decades, the evaluation of learning engagement has experienced distinct phases of evolution, categorized into three primary periods: the embryonic stage from 1991 to 2008, the steadily developing phase between 2009 and 2016, and the rapid growth period from 2017 to 2023. During this timeframe, research output has been notably higher in the United States, Australia, and Canada, contrasting with Japan, which has contributed comparatively fewer publications. Among the institutions, the University of Toronto and the University of Sydney emerged as the most frequently cited, while the journal Computers Education garnered the highest impact factor in this field. The dominant research focus within this domain primarily revolves around defining and measuring learning engagement, examining its correlation with learning outcomes, identifying influential factors, and assessing various evaluation strategies. Furthermore, the most cited works concerning "Evaluation of learning engagement" predominantly explore themes related to motivation and engagement in online learning contexts, particularly how gamification affects student motivation and engagement in Massive Open Online Courses (MOOCs).

Keywords

Artificial Intelligence; Learning Engagement Evaluation; CiteSpace

References

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