Abstract
Intelligent tutoring systems can improve student outcomes, but developing such systems typically requires significant expertise or prior data of students using the system. In this work we propose a new approach for automatically adaptively sequencing practice activities for an individual student. Our approach builds on progress for automatically constructing curriculum graphs and advancing a student through a graph using a multi-armed bandit algorithm. These approaches have relatively few hyperparameters and are designed to work well given limited or no prior data. We evaluate our method, which can be applied to a diverse range of domains, in our online game for basic Korean language learning and found promising initial results. Compared to an expert-designed fixed ordering, our adaptive algorithm had a statistically significant positive effect on a learning efficiency metric defined using in game performance.
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Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. IIS-1657176 and the BIGDATA award. It is also supported by the Stanford Human Centered AI HoffmanYee grant and the Graduate Fellowships for STEM Diversity. Additionally, we thank Brandon Cohen, Nicholas Teo, Evan Adler, Urael Xu, and Nicole Cheung for their work in creating Katchi.
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Mu, T., Wang, S., Andersen, E., Brunskill, E. (2021). Automatic Adaptive Sequencing in a Webgame. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_47
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DOI: https://doi.org/10.1007/978-3-030-80421-3_47
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