University of Veterinary Medicine Vienna Self-regulated learning at the workplace: State, trait, or development? Background: Health sciences students often encounter challenges when they begin learning and practicing in the workplace. Various research approaches and interventions have been developed to facilitate self-regulated learning in this context. To determine the most effective approach, a thorough understanding of the stability and variability of self-regulated learning in the workplace is necessary. Aim: This study analyzed the stable trait, autoregressive trait, and time-varying state components of selfregulated learning in the workplace. Sample: The sample comprised 188 undergraduates who were learning at a medical workplace for the first time and rotating weekly between various settings. Methods: Undergraduates completed a diary on 37 aspects of self-regulated learning for a duration of 10 weeks. Data were analyzed using STARTS models. Results: The aggregate results showed that 28 % of the total variance in self-regulated learning in the workplace was accounted for by the stable trait component, 22 % by the autoregressive trait component, and 50 % by the time-varying state component. These component ratios vary across different areas (cognition, motivation, emotion, and context) and levels (learning process level and metalevel) of self-regulated learning. Conclusions: To enhance health sciences students' self-regulated learning in the workplace, it is recommended to focus on research and interventions to provide situation-specific supportive framework conditions while acknowledging individual differences and considering phase-specific interventions. Further research is required to determine whether these findings can be applied to other rotational intervals and subject areas. en TEXT journal article Wissenschaftlicher Artikel Articolo di rivista Text journal article Text Wissenschaftlicher Artikel Testo Articolo di rivista Higher-Education Students Motivation Model Intervention Personality Transition Stability Error 2025 2025 Evelyn Steinberg Stephan Marsch Takuya Yanagida Laura Dörrenbächer-Ulrich Arowotosuna E. Smith Christopher Pfeiffer Petra Bührle Lukas Schwarz Ulrike Auer Franziska Perels Elsevier