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.

翻译：多模态学习分析（MMLA）创新利用快速发展的传感技术和人工智能算法，收集物理学习空间中发生的丰富学习活动数据。对这些数据的分析为研究和支持学习开辟了令人兴奋的新途径。然而，在"实地"部署MMLA创新时，经常出现实际和后勤方面的挑战。这些挑战可能涉及从增强学习空间传感能力的技术问题，到教师任务复杂性的增加和知情同意等。这些实际操作问题鲜有讨论。本文通过呈现一项为期两年、以人为中心的MMLA实地研究（涉及399名学生和17名教育工作者）中汲取的一系列经验教训，填补了这一空白。这些经验教训被归纳为与以下方面相关的主题：(i) 部署的技术/物理方面；(ii) 多模态数据与界面；(iii) 设计过程；(iv) 参与、伦理与隐私；以及 (v) 部署的可持续性。