Abstract

This chapter builds on the notion that multimodal learning analytics (MMLA) has potential to offer new innovations for the field of education and can be gradually implemented to provide constitutive explanations to improve individual student regulation. We will ground the chapter on the theoretical framework of self-regulated learning (SRL), addressing especially the pivotal role of metacognitive monitoring as a part of regulated learning cycle. Hence, we will introduce the relationship between physiological arousal and monitoring. In particular, we will demonstrate via empirical examples how network analysis methods can provide new insights on the complex interrelations of how monitoring manifests in interactions and evolves across phases of SRL at the a) group level, b) temporal level, and c) individual student level. We conclude the chapter by discussing the prospects, possibilities and challenges of the network analysis as a methodological approach for SRL research.