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Exploring Error State

Exploring Error State in "Time-on-Task" Calculations at Scale

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How does keystroke-derived time-on-task predict student performance and can error state be used to improve predictive power?

Time-on-task has been shown to predict student performance in Computer Science courses, making it a useful tool for teachers to identify which students need extra support. We attempt to add more granularity to our time-on-task metric by leveraging the exit code of the previous compile/run attempt to subdivide student time-on-task into time in an error state and time in an error-free state. We compare the predictive power of (1) keystroke-derived time-on-task, (2) error-free time-on-task, (3) error time-on-task, and (4) the ratio between error-free and error state time-on-task against assignment grades.

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Mohit Chandarana and Elise Deitrick. 2023. Exploring Error State in "Time-on-Task" Calculations at Scale. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 1336.

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