Sensor-Based Analytics in Education: Lessons Learned from Research in Multimodal Learning Analytics

Abstract

The use of sensors to support learning research and practice is not new, whether in the context of wearable technology, context-aware technology, ubiquitous systems or else. Nevertheless, the proliferation of sensing technology has driven the field of learning technology in the development of tools and methods that can generate and leverage sensor-based analytics (SBA) to support complex learning processes. SBA fulfills the vision of integrating multiple sources of information, coming from different channels to strengthen learning systems’ desired features (e.g., adaptation, affect detection) and augment learners’ abilities (e.g., through embodied interaction and cognition). In addition, it offers a promising avenue for improving the research measurements in the field. In this chapter, the authors present how SBA has advanced learning technology through the lenses of their offered qualities, indented objectives and inevitable challenges. Through three case study examples, we showcase how those advancements are reflected in contemporary Multimodal Learning Analytics (MMLA) research. The chapter is concluded with a discussion on the role of SBA and a future research agenda that depicts how the lessons learned from the encountered challenges of MMLA can help us further improve the adoption of SBA for learning technology research and practice.