Abstract
In this paper, we propose a computational approach to model the Zone of Proximal Development (ZPD) using predicted probabilities of correctness while students engage in reflective dialogue. We employ a predictive model that uses a linear function of a variety of parameters, including difficulty and student knowledge, as students use a natural-language tutoring system that presents conceptual reflection questions after they solve high-school physics problems. In order to operationalize our approach, we introduce the concept of the “Grey Area”, that is, the area of uncertainty in which the student model cannot predict with acceptable accuracy whether a student is able to give a correct answer without support. We further discuss the impact of our approach on student modeling, the limitations of this work and future work in systematically and rigorously evaluating the approach.
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Acknowledgements
This research was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A150155 to the University of Pittsburgh. The opinions expressed are those of the authors and do not necessarily represent the views of the Institute or the U.S. Department of Education.
We thank Sarah Birmingham, Dennis Lusetich, and Scott Silliman for their contributions.
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Chounta, IA., Albacete, P., Jordan, P., Katz, S., McLaren, B.M. (2017). The “Grey Area”: A Computational Approach to Model the Zone of Proximal Development. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_1
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