Abstract
We describe a machine-learning system for supporting teachers through the selection of homework assignments. Our system uses behavioural cloning of teacher activity to generate personalised homework assignments for students. Classroom use is then supported through additional mechanisms to combine these predictions into group assignments. We train and evaluate our system against 50,065 homework assignments collected over two years by the Isaac Physics platform. We use baseline policies incorporating expert curriculum knowledge for evaluation and find that our technique improves on the strongest baseline policy by 18.5% in Year 1 and by 13.3% in Year 2.
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Acknowledgements
This paper reports on research supported by Cambridge Assessment, University of Cambridge. We thank members of the Isaac Physics team, our colleagues in the ALTA Institute, and the three anonymous reviewers for their valuable feedback.
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Moore, R., Caines, A., Rice, A., Buttery, P. (2019). Behavioural Cloning of Teachers for Automatic Homework Selection. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_28
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