Bibliometric analysis of cognitive regulation models in science education: Trends, key contributors, and research impact

This study presents a bibliometric analysis of cognitive regulation models in science education, focusing on STEM (Science, Technology, Engineering and Mathematics) contexts between 2004 and 2024. Using the Scopus database and VOSviewer (version 1.6.17) and Bibliometrix (RStudio version 4.4.1), we identified Self-Regulated Learning (SRL) as a key theme, strongly linked to motivation, self-efficacy and academic performance. Emerging areas—such as emotional regulation and digital learning environments—highlight the field’s response to challenges such as the COVID-19 pandemic. Key findings emphasise that integrating learning analytics, blended instruction, and formative assessments can enhance SRL outcomes in both traditional and online settings. However, emotional regulation and SRL application in large-scale online platforms remain underexplored. We conclude that personalised feedback and metacognitive strategies are vital for improving learners’ self-regulation. Future studies should focus on emotional regulation, technology-driven interventions, and interdisciplinary approaches to foster student success in STEM.