On the right track: decoding self-regulated learning in young students’ log data with the digital train track task

Assessing self-regulated learning (SRL)—the interplay between monitoring and control behavior—remains challenging, particularly in young learners. The unobtrusive assessment with log data to investigate SRL offers a promising method to deepen the understanding of the SRL process of young students. Despite the significant potential of log data to enhance the measurement of SRL, recent research encounters new challenges of operationalization, transparency, generalizability, validity, and reproducibility. This study introduces an innovative instrument, the digital train track task (TTT), for assessing SRL with log data in young learners, focusing on monitoring and controlling behavior. Log data of 85 primary school students (second to fifth grades, aged 7–13 years) performing one simple and one complex TTT were analyzed. As a novel method, finite state machines (FSM) were applied to extract SRL-related actions and states from the log data. To evaluate and explore the potential of the digital TTT, monitoring, and control behavior during simple and complex tasks were compared, employing frequency-based statistical analysis and transition graphs. Additionally, the log data were multimethodically linked with think-aloud data. The results revealed differences in monitoring and control behavior during the simple and the complex tasks regarding frequency, duration, and transitions between the SRL-related states. Extracted SRL-related states from log data and corresponding think-aloud data showed significant correlations. Adding to the growing body of log data research, this study offers an innovative task to validly assess the metacognitive self-regulation processes of young learners during problem-solving. The transparent, theory-based operationalization of SRL in this study, taking into account recent demands for SRL log data research, allows better reproducibility and transfer and adds to the generalizability of findings from SRL log data research.