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A Review of Multimodal Data-Driven Evaluation Research on Blended Teaching Models

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DOI: 10.23977/aetp.2026.100320 | Downloads: 3 | Views: 40

Author(s)

Minling Zhu 1, Wangbo Su 1, Dongning Wu 2, Hongbo Huang 1

Affiliation(s)

1 College of Computer Science, Beijing Information Science and Technology University, Beijing, China
2 China Wisdom Engineering Association, Beijing, China

Corresponding Author

Minling Zhu

ABSTRACT

Blended teaching has become the new normal in higher education, yet its evaluation remains challenging due to the distribution of learning behaviors across online and offline spaces. Multimodal learning analytics offers a new paradigm for precise evaluation. This paper systematically reviews research on multimodal data-driven evaluation of blended teaching, examining theoretical foundations, data sources, methodologies, applications, and challenges. The review reveals that multimodal evaluation has evolved from single-dimensional behavioral measurement toward comprehensive assessment encompassing cognition, emotion, behavior, and social interaction. Methods have shifted from traditional questionnaires to machine learning and multimodal fusion, while evaluation content has moved from outcome-oriented to process-oriented assessment. Knowledge graphs are emerging as tools for knowledge visualization and cognitive diagnosis within multimodal frameworks. However, technical bottlenecks in data fusion, insufficient model interpretability, and ethical concerns remain critical challenges. Future research should focus on developing interpretable models, advancing educational multimodal fusion algorithms, and establishing frameworks that balance effectiveness with ethical considerations.

KEYWORDS

Multimodal Learning Analytics; Blended Teaching; Teaching Model Evaluation; Data Fusion; Knowledge Graph

CITE THIS PAPER

Minling Zhu, Wangbo Su, Dongning Wu, Hongbo Huang. A Review of Multimodal Data-Driven Evaluation Research on Blended Teaching Models. Advances in Educational Technology and Psychology (2026). Vol. 10, No. 3, 157-166. DOI: http://dx.doi.org/10.23977/aetp.2026.100320.

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