As theory and research in self-regulated learning (SRL) advance, debate continues about how to measure SRL as strategic, fine-grained, dynamic adaptations learners make during and between study sessions. Recognizing learners' perceptions are critical to the strategic adaptations they make during studying, this research examined the unique contributions of self-report data for understanding regulation as it develops over time. Data included (a) scores on the Regulation of Learning Questionnaire (RLQ) completed in the first and last few weeks of a 13-week course and (b) diary-like Weekly Reflections completed over eleven weeks. Participants were 263 undergraduate students in a course about SRL. First, exploratory factor analysis resulted in a five-factor model of the RLQ with factors labeled Task Understanding, Goal Setting, Monitoring, Evaluating, and Adapting. Second, latent class analysis of Time 1 and 2 RLQ scores revealed four classes: emergent regulators, moderate regulators, high regulators with emergent adapting, and high regulators. Finally, in-depth qualitative analysis of Weekly Reflections resulted in group SRL profiles based on a sub-sample of participants from each RLQ class. Qualitatively, these groups were labeled: unengaged regulators, active regulators, struggling regulators, and emergent regulators. Quantitative and qualitative SRL profiles were juxtaposed and similarities and differences discussed. This paper explicates and discusses the critical importance of sampling self-reports of SRL over time and tasks particularly in contexts where regulation is developing.

Task-specific goals play a critical role in self-regulated learning, yet little research has examined students' self-set goals for individual study sessions for authentic university tasks. We propose goals that are useful for guiding task engagement and evaluating progress are specific about time, actions, standards, and content. In Study 1, we examined characteristics of students' self-set goals. Five categories were created to describe students' goals. Students rarely included specific information regarding actions, standards, or content. In Study 2, we examined patterns of change in quality of self-set goals across a semester in which students were in a learning-to-learn course. Improvements in goal quality were either inconsistent or non-existent. Students' vague goals may indicate low motivation, incomplete understanding of the academic tasks they are assigned. Implications of vague goals for monitoring progress are discussed.

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Given the potential role of self-regulated learning (SRL) for enhancing practice and expertise development, we aimed to advance a valid and reliable athlete self-report measure of SRL for sport practice. We built on Toering et al. (2012a) initial SRL instrument along with Bartulovic et al. (2017) sport-specific modifications, and created new items to extend the conceptual breadth of the subscales. With a multi-sport sample of 482 athletes (Mage = 26.45, SD = 12.66; 55% female), two analytic phases tested (1) the factorial validity of the initial and the extended inventories, and (2) criterion validity, by examining how SRL scores distinguished skill groups ranging from local to international competitive levels. In Phase 1, the initial measurement model demonstrated psychometric concerns and we opted to pursue a refined model. The extended model demonstrated acceptable factorial validity but resulted in the fewest subscales. In Phase 2, subscales scores from all three models generally distinguished international-level senior (18 + years) athletes from lesser-skilled groups. Integrating the psychometric evidence and between-group effects across the initial, refined, and extended models, we conclude that the refined inventory, the Self-Regulated Learning for Sport Practice (SRL-SP) survey, is the preferred instrument.