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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)
Graesser, A.C., Person, N., Harter, D., Group, T.R.: others: Teaching tactics and dialog in AutoTutor. Int. J. Artif. Intell. Educ. 12, 257–279 (2001)
Chi, M.T., Siler, S.A., Jeong, H.: Can tutors monitor students’ understanding accurately? Cogn. Instr. 22, 363–387 (2004)
Chi, M., VanLehn, K., Litman, D., Jordan, P.: An evaluation of pedagogical tutorial tactics for a natural language tutoring system: a reinforcement learning approach. Int. J. Artif. Intell. Educ. 21, 83–113 (2011)
Vygotsky, L.: Interaction between learning and development. Read. Dev. Child. 23, 34–41 (1978)
Chounta, I.-A., McLaren, B.M., Albacete, P., Jordan, P., Katz, S.: Modeling the zone of proximal development with a computational approach. In: Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017) (2017)
Reber, A.S.: The Penguin Dictionary of Psychology. Penguin Press, London (1995)
Chaiklin, S.: The zone of proximal development in Vygotsky’s analysis of learning and instruction. Vygotsky’s Educ. Theory Cult. Context 1, 39–64 (2003)
Tzuriel, D.: Dynamic assessment of young children: educational and intervention perspectives. Educ. Psychol. Rev. 12, 385–435 (2000)
Poehner, M.E., Lantolf, J.P.: Bringing the ZPD into the equation: capturing L2 development during Computerized Dynamic Assessment (C-DA). Lang. Teach. Res. 17, 323–342 (2013)
Feuerstein, R., Rand, Y., Jensen, M.R., Kaniel, S., Tzuriel, D.: Prerequisites for assessment of learning potential: the LPAD model. Dyn. Assess., 35–51 (1987)
Luckin, R., Du Boulay, B.: others: Ecolab: The development and evaluation of a Vygotskian design framework. Int. J. Artif. Intell. Educ. 10, 198–220 (1999)
Corbett, A.T., Koedinger, K.R., Anderson, J.R.: Intelligent tutoring systems. Handb. Hum.-Comput. Interact. 5, 849–874 (1997)
Martin, B., Mitrovic, A., Koedinger, K.R., Mathan, S.: Evaluating and improving adaptive educational systems with learning curves. User Model. User-Adapt. Interact. 21, 249–283 (2011)
Hedegaard, M.: 10 The zone of proximal development as basis for instruction. In: Introduction to Vygotsky, p. 227 (2005)
Katz, S., Allbritton, D., Connelly, J.: Going beyond the problem given: How human tutors use post-solution discussions to support transfer. Int. J. Artif. Intell. Educ. 13, 79–116 (2003)
Katz, S., Albacete, P.L.: A tutoring system that simulates the highly interactive nature of human tutoring. J. Educ. Psychol. 105, 1126 (2013)
Evens, M., Michael, J.: One-on-one tutoring by humans and computers. Psychology Press, New York (2006)
Jordan, P., Albacete, P., Katz, S.: Exploring contingent step decomposition in a tutorial dialogue system. In: The 24th Conference on User Modeling, Adaptation and Personalization (UMAP)
Cen, H., Koedinger, K., Junker, B.: Comparing two IRT models for conjunctive skills. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 796–798. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69132-7_111
Chi, M., Koedinger, K.R., Gordon, G.J., Jordan, P., VanLehn, K.: Instructional factors analysis: a cognitive model for multiple instructional interventions. In: Proceedings of the 4th International Conference on Educational Data Mining, pp. 61–70 (2011)
Kiremire, A.R.: The application of the Pareto principle in software engineering. Consult. January 13, 2016 (2011)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-66610-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66609-9
Online ISBN: 978-3-319-66610-5
eBook Packages: Computer ScienceComputer Science (R0)