A Review of Multimodal Learning Analytics Architectures

There is an increasing interest in Multimodal Learning Analytics (MMLA), which involves complex technical issues in gathering, merging and analyzing different types of learning data from heterogeneous data sources. However, there is still no common reference architecture to face these technical challenges of MMLA. This paper summarizes the state of the art of MMLA software architectures through a systematic literature review. Our analysis of nine architecture proposals highlights the uneven support provided by existing architectures to the different activities of the analytics data value chain (DVC). We find out in those infrastructures that data organization and decision-making support have been under-explored so far. Based on the lessons learnt from the review, we also identify that design tensions like architecture distribution, flexibility and extensibility (and an increased focus on data organization and decision making) are some of the most promising issues to be addressed by the MMLA community in the near future.