Abstract
Knowledge Tracing, and its recent deep learning variants, have made substantial progress in modeling student knowledge acquisition through interactions with coursework. In this paper, we present a modification to Deep Knowledge Tracing to model student progress on coding assignments in large-scale computer science courses. The model takes advantage of the computer science education context by encoding students’ iterative attempts on the same problem and allowing free-form code input. We implement a workflow for collecting data from Jupyter Notebooks and suggest future research possibilities for real-time intervention.
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Acknowledgements
Thank you to UC Berkeley Data 8 course staff, the UC Berkeley Division of Data Sciences, the Machine Learning in Education course, the Jupyter team, and the OkPy team for their support throughout this process.
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Swamy, V. et al. (2018). Deep Knowledge Tracing for Free-Form Student Code Progression. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_65
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DOI: https://doi.org/10.1007/978-3-319-93846-2_65
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