Using AI for Educational Research in Multimodal Learning Analytics

Learning analytics (LA) is defined as the measurement, collection, reporting, and analysis of data about learners and their context for purposes of understanding and optimizing learning and its environments. LA research utilizes artificial intelligence (AI) algorithms to process data traces from student¿s actions in learning environments (e.g., learning management system, massive online open courses, serious games). Nonetheless, such environments often involve many types of human interaction, thus, specifically focusing on learner behavior through their interaction with computer-based learning contexts can lead to incomplete or ambiguous data traces. To tackle such limitations, multimodal learning analytics (MMLA) has emerged in recent years. MMLA introduces the use of multimodal devices (e.g., cameras, microphones, smartwatches, headsets) to collect data from the different communication modalities, physiological interactions, and environmental factors taking place in learning environments. This chapter presents how AI in MMLA can improve the analysis of multiple learning interactions through the review of three practical applications taking place in physical, remote, and hybrid learning environments, as well as a discussion on the challenges that AI in MMLA has yet to overcome to achieve its full potential. © 2024 selection and editorial matter, Manuel Cebral-Loureda, Elvira G. Rincón-Flores and Gildardo Sanchez-Ante.

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