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Session Type: Paper Session
This session explores self-regulated learning (SRL) through diverse methodological lenses and developmental stages, from preschool to postsecondary education. Papers examine how SRL manifests in writing via keystroke logging and goal orientation, how metacognitive prompts in middle school math support strategy use, and how domain-specific versus general strategies predict outcomes in active learning biology courses. Additional contributions include a longitudinal, multidimensional analysis of cognitive load, affect, and motivation, and a Bayesian latent class analysis revealing distinct self-regulatory profiles in preschoolers. Together, these studies offer novel insights into the cognitive and motivational mechanisms of SRL and highlight the importance of tailoring supports to learners’ developmental and disciplinary contexts.
Do Metacognitive-Supportive Exit Tickets in Middle School Math Classrooms Improve Students’ Self-regulated Learning? - Xinran Wang, Vanderbilt University; Jerry Nelluvelil, Harvard University; Madeleine Kingan, Harvard University; Rebecca Adler, Vanderbilt University; Lingfei Cao, Vanderbilt University; Cristina D. Zepeda, Vanderbilt University; Jon R. Star, Harvard University; Bethany Rittle-Johnson, Vanderbilt University; Kelley Durkin, Vanderbilt University
Epistemic Network Analysis and Multigroup Modeling Reveals Domain Specific Self-Regulated and Active Learning in Biology - Hanall Sung, University of Tennessee; Jeff A. Greene, University of North Carolina - Chapel Hill; Matthew L. Bernacki, University of North Carolina - Chapel Hill; Michael Abdul Ghani Berro, University of North Carolina - Chapel Hill; Linyu Yu, University of North Carolina - Chapel Hill; Robert D Plumley, University of North Carolina - Chapel Hill
Exploring self-reported achievement goal orientation as a predictor of keystroke logging behavior patterns interpreted via a lens of self-regulated learning - Sierra Outerbridge, University of Central Florida; Michelle Taub, University of Central Florida; Joel Schneier, University of Central Florida; LaVonda R. Walker, University of Central Florida
Factors Influencing Cognitive Load: Exploring Affective Elements, Motivational Constructs, and Regulatory Strategies in Learning - Rebecca B. Brockbank, Utah State University; David F. Feldon, Utah State University; Kaylee Litson, University of Houston
Individual Differences in Early Self-Regulatory Mechanisms: Bayesian Regularized Latent Class Analysis of Preschoolers' Learning Trajectories - YUNQI WANG, The Education University of Hong Kong; Chengrui Wang, Fudan University; DA GONG, The Education University of Hong Kong