Data-Driven Approaches to Behavior Analysis in Smart Classrooms: A Review of AI Algorithms

Abstract

With the rapid advancement of educational informatization, smart classrooms—as new teaching ecosystems integrating multiple sensing devices and data streams—are profoundly transforming the observation and analysis of classroom behaviors. Artificial intelligence algorithms, particularly machine learning and deep learning methods, provide a solid foundation for the efficient mining and intelligent interpretation of behavioral data, helping to reveal students' learning states, interaction patterns, and cognitive dynamics. This paper systematically reviews core domestic and international literature from 2015 to 2022, focusing on artificial intelligence algorithms in the field of intelligent classroom behavior analysis. The research encompasses key technical approaches including single-modal, multi-modal fusion, deep learning, and edge computing. By comparing multiple dimensions including recognition accuracy, robustness, real-time performance, and scalability, the study further highlights the significant advantages of multimodal fusion and deep learning architectures in complex behavior recognition. It simultaneously identifies key challenges such as data scarcity, insufficient model generalization capabilities, interpretability, and privacy protection. This paper summarizes the urgent need to establish a standardized evaluation system and explores the latest trends in human-machine collaboration. The conclusion emphasizes that future advancements in intelligent classroom behavior analysis must rely on interdisciplinary innovation to drive algorithmic interpretability, privacy compliance, and deep integration with real teaching scenarios. This will facilitate the transition of behavioral intelligence analysis from experimental validation to large-scale application. This research aims to provide systematic theoretical guidance and a technical roadmap for scholars and practitioners in educational informatization, promoting the standardization and sustainable development of intelligent classroom behavior analysis research.