软件部署
学习分析
背景(考古学)
计算机科学
数据科学
过程(计算)
分析
持续性
大数据
知识管理
人机交互
人工智能
软件工程
生态学
生物
操作系统
古生物学
作者
Roberto Martínez‐Maldonado,Vanessa Echeverría,Gloria Fernandez‐Nieto,Lixiang Yan,Linxuan Zhao,Riordan Alfredo,Xinyu Li,Samantha Dix,Hollie Jaggard,Rosie Wotherspoon,Abra Osborne,Simon Buckingham Shum,Simon Buckingham Shum
摘要
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.
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