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

• Published in: arXiv.org 2023-03
• Main Authors: Martinez-Maldonado, Roberto, Echeverria, Vanessa, Fernandez-Nieto, Gloria, Yan, Lixiang, Zhao, Linxuan, Riordan, Alfredo, Li, Xinyu, Dix, Samantha, Jaggard, Hollie, Wotherspoon, Rosie, Osborne, Abra, Gašević, Dragan, Simon Buckingham Shum
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Informed consent; Innovations; Machine learning; Task complexity
• ISSN: 2331-8422

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.