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In contemporary education, the prevalence of the test-oriented education has led to a single, rigid learning assessment, as well as the neglecting of the personalized learning needs of learners. The rise of smart classroom and multimodal learning analytics provides a solution to this problem. In the smart classroom, learners interact with instructors, peers, technology, and learning aids through multisensory channels, generating a large amount of multimodal data. The collection and analysis of such data can be further supported with the help of key technologies in the smart classroom. To understand this analysis, the study models a multimodal learning analytics grounded in the smart classroom, based on the concepts and characteristics of multimodal learning analysis and smart classroom. By drawing on the analysis of existing models, this model is centered on a six-step multimodal learning analytics cycle that involves establishing goals, collecting data, processing data, analyzing data, providing feedback, and implementing interventions, and also includes key elements such as stakeholders, the technological environment of smart classroom, types of multimodal data, and collection devices. The study explores the specific elements and practical operational requirements of each step of the model, with a view to provide theoretical support for the development of a learning analytics system in the smart classroom at a later stage, providing more comprehensive and personalized assessment method for classroom teaching, and promoting the intelligent and personalized development of education. |
