Lessons Learnt from a Multimodal Learning Analytics Deployment In-the-wild

March 16, 2023 Β· Declared Dead Β· πŸ› ACM Trans. Comput. Hum. Interact.

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Authors Roberto Martinez-Maldonado, Vanessa Echeverria, Gloria Fernandez-Nieto, Lixiang Yan, Linxuan Zhao, Riordan Alfredo, Xinyu Li, Samantha Dix, Hollie Jaggard, Rosie Wotherspoon, Abra Osborne, Dragan Gaőević, Simon Buckingham Shum arXiv ID 2303.09099 Category cs.HC: Human-Computer Interaction Citations 39 Venue ACM Trans. Comput. Hum. Interact. Last Checked 3 months ago
Abstract
Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical learning spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges commonly appear while deploying MMLA innovations "in-the-wild". These can span from technical issues related to enhancing the learning space with sensing capabilities, to the increased complexity of teachers' tasks and informed consent. These practicalities have been rarely discussed. This paper addresses this gap by presenting a set of lessons learnt from a 2-year human-centred MMLA in-the-wild study conducted with 399 students and 17 educators. The lessons learnt were synthesised into topics related to i) technological/physical aspects of the deployment; ii) multimodal data and interfaces; iii) the design process; iv) participation, ethics and privacy; and v) the sustainability of the deployment.
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