Self-Regulation, Motivation, Emotion, and Skill Mastery of Online Learning: New Evidence From a Survey During COVID-19

Introduction

Self-regulation is a core concept associated with the nature of learning that emphasizes the self-driven learning motivation exhibited by learners. During the past three decades, the notion of self-regulated learning (SRL) has developed into a sophisticated conceptual framework used by high-impact scholars and featuring theoretical models, empirical evidence, and established instruments (Panadero, 2017). The multidimensional construct of SRL reflects the perplexing aspects of learning by mapping observable behaviors (e.g., setting goals, monitoring progress, reflection) onto metacognition, motivation, emotion, culture, and other hidden drives internalized by learners. This mapping work enables education researchers and practitioners to unpack some myths associated with overachievers from disadvantaged backgrounds (e.g., the upward mobility of first-generation college students) or underachievers who waste their potential (e.g., the downward mobility of students who exhibit lower educational attainment than their parents) (Zimmerman & Risemberg, 1997). SRL has been proven to play a critical role not only in academic performance (e.g., course grades) but also in nonacademic outcomes (e.g., student engagement) (Anthonysamy et al., 2020), which matter more with regard to long-term knowledge retention and transferrable skills. Our chapter contributes to this line of investigation with regard to SRL by focusing on the associations among learners’ background, their learning behaviors, the latent mechanisms that drive their behaviors, and self-reported skill mastery in terms of nonacademic outcomes.

Early SRL theory studies were usually conducted in a strictly controlled psychological laboratory. However, one obvious trend is recent research on learning behavior has been to focus on real educational situations. The outbreak of COVID-19 led to dramatic changes in school teaching methods worldwide. These changes also provided a large-scale setting for collecting new empirical evidence to test this conceptual framework in an authentic online learning environment. For example, students might exhibit different modes or methods of learning in different disciplines, which feature different course designs and are based on different self-regulated learning models (Struyven et al., 2006; Nijhuis et al., 2008). These difference could even be found with regard to different groups in a single class because their learning strategies are diverse (e.g., deep learning vs. surface learning) (Winne & Hadwin, 1998).