Author(s)

Ying Shi^*, XinYue Zhang

Affiliation(s)

College of Information Technology, Jilin Agricultural University, Changchun 130118, Jilin, China.

Corresponding Author

Ying Shi

ABSTRACT

This study examines the current status and future prospects of student behavior analysis in smart classrooms, with a focus on the context of educational informatization, the impact of artificial intelligence on education, the process of learning behavior analysis, the application of relevant technologies, the present state of behavior classification, and associated research achievements and applications. Against the backdrop of educational digital transformation, smart classrooms integrate big data and artificial intelligence to enable precise monitoring and analysis of students’ learning behaviors, thereby providing essential support for personalized instruction. Research findings indicate that smart classrooms can effectively enhance students’ classroom attention and engagement, while assisting teachers in better understanding learning conditions and optimizing instructional strategies. Nevertheless, practical implementation still faces numerous challenges, including technology reliability and data privacy protection. Future research should further expand the diversity and coverage of data sources, emphasize the application of multimodal data fusion analysis, and explore the role of smart classrooms in promoting educational equity and personalized teaching, in order to advance the development of smart education.

KEYWORDS

Smart classrooms; Patterns of student behavior; Educational informatization

CITE THIS PAPER

Ying Shi, XinYue Zhang. Student behavior analysis in smart classrooms: current trends and future directions. World Journal of Educational Studies. 2025, 3(4): 11-16. DOI: https://doi.org/10.61784/wjes3061.

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