FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics
CoRR(2024)
摘要
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 undergraduate students who used
MMLA visualisations for post-activity reflection. The findings highlighted the
significance of accurate and comprehensive data representation to ensure
visualisation fairness, the need for different levels of data access to foster
accountability, the imperative of measuring and cultivating transparency with
students, and the necessity of transforming informed consent from dichotomous
to continuous and measurable scales. While students value the benefits of MMLA,
they also emphasise the importance of ethical considerations, highlighting a
pressing need for the LA and MMLA community to investigate and address FATE
issues actively.
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