Abstract
Imagine a platform in which the teacher can access to identify patterns in the learning styles of students attached to their course, and in turn this will allow you to know which pedagogical techniques to use in the teaching process - learning to increase the probability of success in your classroom?. What if this tool could be used by students to identify the teacher that best suits their learning style?. Yes, was the tool able to improve its prediction regarding academic performance as time passes? It is obvious that this would require specialized software in the handling of large data. This research-development aims to answer these questions, proposing a design methodology of a student pattern recognition tool to facilitate the teaching-learning process through Knowledge Data Discovery (Big Data). After an extensive document review and validation of experts in various areas of knowledge, the methodology obtained was structured in four phases: identification of patterns, analysis of the teaching-learning process, Knowledge Data Discovery and Development, implementation and validation of software.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sánchez Guzmán, D.: Agentes Inteligentes; Diseño e Implementación para la Enseñanza de la Física, Tesis Doctoral en Tecnología Avanzada, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México (2009)
Mondragón Becerra, R.: Exploraciones sobre el Soporte Multi-Agente BDI en el Proceso de Descubrimiento de Conocimiento en Bases de Datos, Tesis de Maestría en Inteligencia Artificial. Departamento de Inteligencia Artificial, Universidad Veracruzana, México (2015)
Reyes Saldaña, J.F., García Flores, R.: El proceso de descubrimiento de conocimiento de bases de datos. Revista Ingenierías, vol. VIII, no. 26, pp. 37–47 (2015)
Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012)
Gómez Arenas, L.I.: Evaluación Comparativa de Herramientas para la Minería de Datos y sus Aplicaciones. Instituto Tecnológico de León, Guanajuato (2015)
Cristobal, R., Sebastian, V., Mykola, P., Baker, R.: Handbook of Educational Data Mining. CRC Data Mining and Knowledge Discovery Series, 1st edn. CRC Press, Chapman & Hall, Boca Raton (2010)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann Publishers, Burlington (2016). (Series Editor J. Gray)
Romero Morales, C., Ventura Soto, S., Hérvas Martínez, C.: Estado actual de la aplicación de la minería de datos a los sistemas de enseñanza basada en web. In: Actas del III Taller Nacional de Minería de Datos y Aprendizaje, TAMIDA2005, pp. 49–56 (2015)
Luan J.: Aplicaciones de Minería de datos en la Educación Superior. IBM Press and IBM Corporation, Estados Unidos de America (2012))
Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent Works. WOLNM & ESIME Zacatenco, Instituto Politécnico Nacional, México (2013)
La Red Martínez, D.L., Karanik, M., giovannini, M., Pinto, N.: Perfiles de Rendimiento Académico: Un Modelo basado en Minería de datos. Campus Virtuales, vol. IV, no. 1, pp. 12–30 (2015). www.revistacampusvirtuales.es. Consultado el 12 Nov 2015
Thakuriah, P.V., Tilahun, N.Y., Zellner, M.: Big data and urban informatics: innovations and challenges to urban planning and knowledge discovery. In: Thakuriah, P., Tilahun, N., Zellner, M. (eds.) Seeing Cities Through Big Data, pp. 11–45. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-40902-3_2
Roiger, R.J.: Data Mining: A Tutorial-Based Primer. CRC Press, Boca Raton (2017)
Khan, A., Uddin, S., Srinivasan, U.: Understanding chronic disease comorbidities from baseline networks: knowledge discovery utilising administrative healthcare data. In: Proceedings of the Australasian Computer Science Week Multiconference, p. 57. ACM (2017)
Bajorath, J.: Compound Data Mining for Drug Discovery. Bioinformatics: Structure, Function, and Applications, vol. II, pp. 247–256 (2017)
Bandaru, S., Ng, A.H.C., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: part A-survey. Expert Syst. Appl. 70, 139–159 (2017)
Chen, C., et al.: KDD 2016-Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (2016)
Zobaa, A.F., Vaccaro, A., Lai, L.L.: Guest editorial enabling technologies and methodologies for knowledge discovery and data mining in smart grids. IEEE Trans. Ind. Inf. 12(2), 820–823 (2016)
Jiang, H., et al.: Research on pattern analysis and data classification methodology for data mining and knowledge discovery. Int. J. Hybrid Inf. Technol. 9(3), 179–188 (2016)
Mendoza, A.A.M., Acosta, R.J.H.: Propuesta para la predicción del rendimiento académico de los estudiantes de la Universidad del Atlántico, basado en la aplicación del análisis discriminante. In: WEEF 2013 Cartagena, August 2013
Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.): PAKDD 2017. LNCS (LNAI), vol. 10234. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7
Cañon, M., Jimenez, S.: Enfrentando resultados programa de ingeniería de sistemas de la USB con las pruebas Saber Pro. Revista Investigación y Desarrollo en TIC, vol. 3, no. 1 (2017)
Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística, vol. 9, no. 1, pp. 93–106 (2016). (85–97 English)
Viloria, A., Mercedes, G.-A.: Statistical adjustment module advanced optimizer planner and sap generated the case of a food production company. Indian J. Sci. Technol. 9(47), 1–5 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Viloria, A., Lis-Gutiérrez, JP., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J. (2018). Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-93803-5_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93802-8
Online ISBN: 978-3-319-93803-5
eBook Packages: Computer ScienceComputer Science (R0)