Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Abstract Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. Practitioner Notes What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.