Conceptual clustering: a new approach to student modeling in Intelligent Tutoring Systems

Yunia Reyes-González; Natalia Martínez-Sánchez; Adolfo Díaz-Sardiñas; Marisol de la Caridad Patterson-Peña

doi:10.17533/udea.redin.n87a09

Authors

Yunia Reyes-González University of Information Science
Natalia Martínez-Sánchez University of Information Science
Adolfo Díaz-Sardiñas University of Information Science
Marisol de la Caridad Patterson-Peña University of Information Science https://orcid.org/0000-0003-2372-3458

DOI:

https://doi.org/10.17533/udea.redin.n87a09

Keywords:

student modeling, intelligent tutoring systems, logical combinatorial pattern recognition, artificial intelligence

Abstract

Student modeling is a central problem in Intelligent Tutoring Systems design and development. In this way, the characteristic that distinguishes this type of system is the ability to determine as accurately and quickly as possible the student’s cognitive and affective-motivational state in order to personalize the educational process. Therefore, the fundamental problem is to select data structure to represent all relative information to student and to choose the procedure to make the diagnosis. This paper describes a model for knowledge engineering inherent to all intelligent tutoring system, using the LC-Conceptual clustering algorithm, from logical combinatorial pattern recognition. This algorithm builds the objects clusters based on their similarity, using a grouping criterion, and it also builds the property (or concept) that meets each group of objects.

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Author Biographies

Yunia Reyes-González, University of Information Science

MSc. and Assistant Professor of the Programming Department, Vice Dean of Research and Postgraduate Studies.

Natalia Martínez-Sánchez, University of Information Science

PhD. In Technical Sciences and Professor. Programming Department.

Adolfo Díaz-Sardiñas, University of Information Science

PhD. in Pedagogical Sciences and Professor, Programming Department.

Marisol de la Caridad Patterson-Peña, University of Information Science

Master in English Language Studies and Assistant Professor at the Language Center.

References

D. A. Ovalle and J. A. Jiménez, “Entorno Integrado de Enseñanza / Aprendizaje basado en Sistemas Tutoriales Inteligentes Ambientes Colaborativos,” Sistemas, Cibernética e Informática, vol. 1, no. 1, pp. 23–27, 2004.

N. Martínez, M. M. García, and Z. Z. García, “Modelo para diseñar sistemas de enseñanza-aprendizaje inteligentes utilizando el razonamiento basado en casos,” Revista Avances en Sistemas e Informática, vol. 6, no. 3, pp. 67–78, Dec. 2009.

T. J. M. Bench, Knowledge Representation: An Approach to Artificial Intelligence, 1st ed. San Diego,USA: Academic Press, 1990.

C. Li and J. Yoo, “Modeling Student Online Learning Using Clustering,” in 44th annual Southeast regional conference, Melbourne, Florida, 2006, pp. 186–191.

K. Chrysafiadi and M. Virvou, “Student modeling approaches: A literature review for the last decade,” Expert Syst. Appl., vol. 40, no. 11, pp. 4715–4729, Sep. 2013.

D. Medina, N. Martínez, Z. Z. García, M. Chávez, and M. M. García, “Putting Artificial Intelligence Techniques into a Concept Map to Build Educational Tools,” in Nature Inspired Problem-Solving Methods in Knowledge Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 617–627.

J. Shulcloper, “Reconocimiento lógico combinatorio de patrones: teoría y aplicaciones,” M.S. thesis, Universidad Central de Las Villas, Santa Clara, Cuba, 2009.

N. Martinez, M. M. Garcia, and J. E. Hurtado, “Model for designing Intelligent Tutorials Systems using Conceptual Maps and knowledge-based Systems,” IEEE Latin America Transactions, vol. 10, no. 6, pp. 2301–2308, Dec. 2012.

Y. Reyes and N. Martínez, “La toma de decisiones en los Sistemas Tutoriales Inteligentes utilizando el agrupamiento conceptual,” Rev. Cuba. Cienc. Informáticas, vol. 8, pp. 104–116„ Dec. 2014.

D. Ovalle, “Análisis funcional de la estrategia de aprendizaje individualizado adaptativo,” Proy. Investig., Universidad Nacional de Colombia, Medellín, 2007.

J. F. Martínez, “Herramientas para la Estructuración Conceptual de Espacios,” M.S. thesis, CIC, IPN, México, 2000.

R. S. Michalski, “Conceptual Clustering: A Theoretical Foundation and a Method for Partitioning Data into Conjunctive Concepts,” in Textes des exposes du Seminaire organise par l’Institute de Recherche d’Informatique et d’Automatique (IRIA), París, France, 1979, pp. 254–294.

A. Rodriguez and G. Sánchez, “An Algorithm for Computing Typical Testors Based on Elimination of Gaps and Reduction of columns,” Aerosp. and Electron. Syst., vol. 27, no. 8, p. 18, Dec. 2013.

J. Martínez and A. Guzmán, “The logical combinatorial approach to pattern recognition, an overview through selected works,” Pattern Recognit., vol. 34, no. 4, pp. 741–751, Apr. 2001.

J. Plasencia, F. Marrero, M. Nicado, and Y. Aguilera, “Procedimiento para la priorización de Factores Críticos de Éxito,” DYNA, vol. 84, no. 202, pp. 26–34, Jul. 2017.

R. Likert, “A technique for the measurement of attitudes,” Arch. Psychol., vol. 22, no. 140, pp. 5–55.

Y. Reyes, N. Martínez, and M. M. García, “El agrupamiento conceptual en el contexto de la teoría de los conjuntos Aproximados,” DYNA New Technol., vol. 2, no. 1, p. 12, Jan. 2015.