Navigating the Ethical Landscape of Multimodal Learning Analytics: A Guiding Framework

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Artificial intelligence (AI) and multimodal data (MMD) are gaining popularity in education for their ability to monitor and support complex teaching and learning processes. This line of research and practice was recently named Multimodal Learning Analytics (MMLA). However, MMLA raise serious ethical concerns given the pervasive nature of MMD and the opaque AI techniques that process them. This study aims to explore ethical concerns related to MMLA use in higher education and proposes a framework for raising awareness of these concerns, which could lead to more ethical MMLA research and practice. This chapter presents the findings of 60 interviews with educational stakeholders (39 higher education students, 12 researchers, 8 educators, and 1 representative of an MMLA company). A thematic coding of verbatim transcriptions revealed nine distinct themes. The themes and associated probing questions for MMLA stakeholders are presented as a draft of the first ethical MMLA framework. The chapter is concluded with a discussion of the emerging themes and suggestions for MMLA research and practice.

Ryan Watkins