Multisensory Interaction and Analytics to Enhance Smart Learning Environments: A Systematic Literature Review

Abstract

Smart learning environments (SLEs) leverage technological developments to enable effective, engaging, and personalized learning. SLEs rely on sensor and advanced interconnectivity capabilities to infer and reason. A natural set of affordances for SLEs are those of multisensory environments (MSEs). MSEs enable humans to make full use of their senses and equip learning systems with new information, methods of information exchange, and intelligent capabilities. In addition to novel forms of interaction, MSEs also offer novel forms of 'learner traces' through multimodal learning analytics (MMLA). This article presents the results of a systematic literature review on how multisensory interactions and the respective analytics can support the use and design of SLEs. The findings from the analysis of 33 papers synthesize and clarify the latest advancements in the intersection of interactions and analytics in MSEs, discuss how those advancements can support SLEs' affordances and uses, and pave the way for improving our understanding of how various interaction modalities can support learning and under what conditions.