Abstract
Understanding how students allocate their time to different learning behaviors, especially those that distinguish students’ performances, can yield significant implications for the design of intelligent tutoring systems (ITS). Time on task is a typical indicator of students’ self-regulated learning (SRL) and student engagement. In this paper, we analyze log file data to identify patterns in the behavior durations of 62 medical students in BioWorld, an ITS that supports them in regulating their diagnostic reasoning while solving complex patient cases. Results demonstrated that task complexity mediated the relationship between students’ allocation of time and diagnostic performance outcomes. The high-performing students showed different patterns of time management with low-performing students when solving both simple and complex cases. Moreover, the durations of behaviors predicted students’ performance in clinical reasoning.
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Li, S., Zheng, J., Poitras, E., Lajoie, S. (2018). The Allocation of Time Matters to Students’ Performance in Clinical Reasoning. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_11
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