Supporting Self-Regulated Learning and Student Success in Online Courses

Students who self-regulate are more likely to improve their academic performance, find value in their learning process, and continue to be effective lifelong learners. However, online students often struggle to self-regulate, which may contribute to lower academic performance. Likewise, less experienced online teachers who are in the process of implementing—or have implemented—a shift from in-person to distance learning may struggle to enable their students to employ effective self-regulation techniques. Supporting Self-Regulated Learning and Student Success in Online Courses examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of self-regulated learning models and interventions in online courses and discusses their implications. Covering key topics such as online course design, student retention, and learning support, this reference work is ideal for administrators, policymakers, researchers, academicians, practitioners, scholars, instructors, and students.

Danny Glick is a Research Affiliate at the University of California, Irvine's Online Learning Research Center where he explores ways to improve student persistence and performance in online courses using early warning systems and light-touch interventions. He is a former visiting scholar at the University of California, Irvine's School of Education where he investigated the effects of blended learning on the achievement of low-income students. Dr. Glick is also the Director of Pedagogical Implementation at Edusuft, a subsidiary of ETS, where he leads a team of EdTech implementation specialists. For the past 20 years, he has helped ministries of education and higher education institutions in 35 countries to shift from traditional instruction to online learning. Dr. Glick holds a PhD in Learning Technologies and a Master's degree in Curriculum & Instruction, and has presented and published on topics including early warning systems, targeted interventions, student persistence, and learning design. Chi Chang is a tenure-track assistant professor in the Office of Medical Education Research and Development and the Department of Epidemiology and Biostatistics in the School of Human Medicine at Michigan State University. She earned her PhD in Measurement and Quantitative Methods. She holds master's degrees in Biostatistics and Educational Administration and Policy, and has a background in teacher education, educational psychology, and counselling. Her research interests center on classification methodologies and cognitive diagnostic assessments. She evaluates the quality of parameter estimation among statistical methods under various conditions using simulation studies. Her psychometric research focuses on diagnosing students' cognitive skills and exploring methodologies to identify students' learning patterns, progress performance, clinical knowledge, and clinical skills. Chang's interests include multilevel modeling, finite mixture modeling, measurement invariance, and meta-analysis. Currently, her research is focused on applying neural network algorithms to optimize classification accuracy. She has projects applying pattern recognition and recurrent neural networks to multi-site program evaluation and medical education.