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

• Published in: ACM transactions on computer-human interaction 2023-11, Vol.31 (1), p.1-41, Article 8
• Main Authors: Martinez-Maldonado, Roberto, Echeverria, Vanessa, Fernandez-Nieto, Gloria, Yan, Lixiang, Zhao, Linxuan, Alfredo, Riordan, Li, Xinyu, Dix, Samantha, Jaggard, Hollie, Wotherspoon, Rosie, Osborne, Abra, Shum, Simon Buckingham, Gašević, Dragan
• Format: Article
• Language: English
• Subjects: Collaborative and social computing systems and tools; Empirical studies in ubiquitous and mobile computing; Human-centered computing
• ISSN: 1073-0516; 1557-7325
• DOI: 10.1145/3622784

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 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. These practicalities have been rarely investigated. This article 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 in the context of nursing education. 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) sustainability of the deployment.