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2017 | Supplement | Chapter

The “Grey Area”: A Computational Approach to Model the Zone of Proximal Development

Authors : Irene-Angelica Chounta, Patricia Albacete, Pamela Jordan, Sandra Katz, Bruce M. McLaren

Published in: Data Driven Approaches in Digital Education

Publisher: Springer International Publishing

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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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next chapter Machine and Human Observable Differences in Groups’ Collaborative Problem-Solving Behaviours
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Metadata
Title
The “Grey Area”: A Computational Approach to Model the Zone of Proximal Development
Authors
Irene-Angelica Chounta
Patricia Albacete
Pamela Jordan
Sandra Katz
Bruce M. McLaren
Copyright Year
2017
Book
Data Driven Approaches in Digital Education

Print ISBN: 978-3-319-66609-9

Electronic ISBN: 978-3-319-66610-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66610-5_1

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