FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics
Yueqiao Jin, Vanessa Echeverria, Lixiang Yan, Linxuan Zhao, Riordan, Alfredo, Yi-Shan Tsai, Dragan Ga\v{s}evi\'c, Roberto Martinez-Maldonado

TL;DR
This paper explores students' perceptions of fairness, accountability, transparency, and ethics in multimodal learning analytics within authentic collaborative learning settings, emphasizing the importance of ethical practices and transparent data use.
Contribution
It provides an empirical assessment of FATE issues in MMLA through student interviews, highlighting key considerations for ethical implementation in real-world educational contexts.
Findings
Accurate data visualization enhances perceived fairness.
Different data access levels promote accountability.
Continuous consent improves ethical engagement.
Abstract
Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14…
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Taxonomy
TopicsOnline Learning and Analytics · Online and Blended Learning · Innovative Teaching and Learning Methods
