A Bibliometric Review of Research on Self-Regulated Learning in Online Learning Contexts: Science Mapping the Literature. “Self-regulated learning in digitalized schools"

Self-regulated learning (SRL) refers to the ability to plan, monitor, and reflect on one's learning process and is considered an essential higher-order skill for human development in the 21st century (e.g., Álvarez et al., 2022; Pintrich, 2000). This is because SRL skills promote individual academic achievement (Li et al., 2018) and help humans adapt to changing environments by becoming lifelong learners (Taranto & Buchanan, 2020). For example, when a pandemic strikes the world, teaching is suddenly switched to online, and students and teachers are entirely removed from brick-and-mortar schools (Liebendörfer et al., 2022). They may have many unpredictable difficulties and challenges because they lack a supportive home learning environment and experience with online learning and teaching. The students' and teachers' ability to self-regulate their learning and teaching is critical in determining whether they can handle these challenges and difficulties (Zhang et al., 2022). Research has found that SRL performance differs in online and in-person settings (e.g., Adam et al., 2017). Although research on SRL in different learning environments exists, existing studies, especially in the review category, have either explored the relationship between SRL and student achievement (Li et al., 2018) or strategies to improve SRL (Lai & Hwang, 2021). However, limited comprehensive review research addresses SRL in online learning contexts. A complete overview of the hotspots and development trends in the research field of SRL in online learning contexts is of great significance for helping all education stakeholders promote theoretical research and practical innovation in SRL. This study analyzed data concerning SRL in online learning contexts from Social Sciences Citation Index (SSCI) through a bibliometric approach. This approach refers to a mathematical and statistical approach to presenting scientific knowledge through analyzing bibliographic data in a deductive and visual way (Börner et al., 2003). We used CiteSpace, referring to a bibliometric visualization software based on the Java computer programming language developed by Professor Chaomei Chen of the School of Information Science and Technology at Drexel University, USA, as the research tool (Chen, 2016), to address the following research questions: 1. What are the characteristics, hotspots, and knowledge base of self-regulated learning in online learning contexts? 2. What can research gaps be addressed in future self-regulated learning research?