Abstract
This paper discusses how a dialogue-based tutoring system makes decisions to proactively scaffold students during conceptual discussions about physics. The tutor uses a student model to predict the likelihood that the student will answer the next question in a dialogue script correctly. Based on these predictions, the tutor will, step by step, choose the granularity at which the next step in the dialogue is discussed. The tutor attempts to pursue the discussion at the highest possible level, with the goal of helping the student achieve mastery, but with the constraint that the questions it asks are within the student’s ability to answer when appropriately supported; that is, the tutor aims to stay within its estimate of the student’s zone of proximal development for the targeted concepts. The scaffolding provided by the tutor is further adapted by adjusting the way the questions are expressed.
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Acknowledgments
We thank Sarah Birmingham and Scott Silliman. This research was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A150155 to the University of Pittsburgh.
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Albacete, P., Jordan, P., Lusetich, D., Chounta, I.A., Katz, S., McLaren, B.M. (2018). Providing Proactive Scaffolding During Tutorial Dialogue Using Guidance from Student Model Predictions. 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_4
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