Analysis of learning interactions can happen for different purposes. As
educational practices increasingly take place in hybrid settings, data from both spaces are
needed. At the same time, to analyse and make sense of machine aggregated data afforded
by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We
posit that human labelled (classroom observations) and automated observations
(multimodal learning data) can enrich each other. Researchers have suggested learning
design (LD) for contextualisation, the availability of which is often limited in authentic
settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize
systematic documentation and data collection within different research designs and
scenarios, paying special attention to authentic classroom contexts. Finally, we discuss
further research directions and challenges.Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges
