The role of learning theory in multimodal learning analytics

Abstract

AbstractThis study presents the outcomes of a semi‐systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory‐driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory‐driven MMLA research and how this acceleration can extend or even create new theoretical knowledge. Practitioner notesWhat is already known about this topic Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena. MMLA can help us monitor learning activity at the micro‐level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces. MMLA provide new opportunities to support students' learning. What this paper adds Some MMLA works use theory, but, overall, the role of theory is currently limited. The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory. Most of the theory‐driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it. Implications for practice and/or policy If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories. MMLA research is mature enough to contribute to learning theory, and more research should aim to do so. MMLA researchers and practitioners, including technology designers, developers, educators and policy‐makers, can use this review as an overview of the current state of theory‐driven MMLA.