FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics
<|reference_start|>FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics: 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.<|reference_end|>
@article{jin2024fate,
    title={FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability,
  Transparency, and Ethics in Multimodal Learning Analytics},
    author={Yueqiao Jin, Vanessa Echeverria, Lixiang Yan, Linxuan Zhao, Riordan
  Alfredo, Yi-Shan Tsai, Dragan Gav{s}evi'c, Roberto Martinez-Maldonado},
    journal={arXiv preprint arXiv:2402.19071},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19071},
    primaryClass={cs.CY cs.HC}
}