An Evolutionary Multi-Objective Approach to the Problem of Building College Student Groups

  • Iván Jacho Sánchez Universidad Técnica Estatal de Quevedo
  • Lorena Arboleda Universidad Técnica Estatal de Quevedo
  • Olga Cedeño Universidad Técnica Estatal de Quevedo
  • Eduardo Samaniego Universidad Técnica Estatal de Quevedo
  • Pavel Novoa Universidad Técnica Estatal de Quevedo
Keywords: Group building, evolutionary multi-objective optimization, metaheuristics

Abstract

The creation of working groups of students in education is a common process that is often developed by the teacher intuitively. However, such a process is actually a complex task since various students and criteria must be taken into account. In general, these criteria are often in conflict because they are a reflection of the educational interests of teachers and on the other hand, the individual preferences of students. In this sense, this paper has as general goal: to propose a mathematicalcomputational solution that efficiently automatizes, in terms of computational time and solution quality, the creation of working groups of college students. The results obtained from two real scenarios of the Universidad Tecnica Estatal de Quevedo indicate that the proposal is an effective alternative to the traditional model.

DOI  

Downloads

Download data is not yet available.

References

UNESCO and J. Delors, La educación encierra un tesoro: informe a la UNESCO de la Comisión Internacional sobre la educación para el siglo XXI, presidida por Jacques Delors. Correo de la UNESCO, 1997.

M. H. Gavidia, G. C. Galarza, M. M. Robalino, D. C. Chabla, and P. Novoa-Hernández, ‘Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura Inglés’, Cienc. Unemi, vol. 9, no. 21, pp. 58–67, 2016.

P. E. Glinz, ‘Un acercamiento al trabajo colaborativo’, Rev. Iberoam. Educ., vol. 35, no. 2, pp. 1–13, 2005.

R. L. L. Hughes and S. K. K. Jones, ‘Developing and assessing college student teamwork skills’, New Dir. Institutional Res., vol. 2011, no. 149, pp. 53–64, 2011.

P. Novoa-Hernández, M. A. Novoa-Hernández, and Y. Rivero-Peña, ‘Propuesta de técnicas evolutivas para la confección automática de tribunales de trabajos de diploma’, Rev. Cuba. Ciencias Informáticas, vol. 7, no. 4, pp. 90–99, 2013.

P. Novoa-Hernández, ‘Optimización evolutiva multi-objetivo en la planificación de controles a clase en la educación superior cubana’, Comput. y Sist., vol. 19, no. 2, pp. 321–335, 2015.

K. Escalera Fariñas, A. L. Infante Abreu, M. André Ampuero, and A. Rosete Suárez, ‘Uso de estrategias de paralelización en algoritmos metaheurísticos para la conformación de equipos de software’, Rev. Cuba. Ciencias Informáticas, vol. 8, no. 3, pp. 90–99, 2014.

Y. Rivero Peña, P. Novoa-Hernández, Y. Fernández Ochoa, P. Novoa Hernández, and Y. Fernández Ochoa, ‘La optimización evolutiva multi objetivo en la confección de equipos de desarrollo de software: una forma de lograr la calidad en el producto final’, Enfoque UTE, vol. 29, no. 1, pp. 35–44, 2015.

F. Ahmed, A. Jindal, and K. Deb, Cricket team selection using evolutionary multi-objective optimization, vol. 7077 LNCS, no. PART 2. Berlin, Heidelberg: Springer-Verlag, 2011.

I. Wegener, Complexity Theory: Exploring the Limits of Efficient Algorithms. Springer Berlin Heidelberg, 2004.

J. H. Mueller, K. F. Schuessler, and H. L. Costner, Statistical Reasoning in Sociology. Houghton Mifflin, 1977.

D. A. Van Veldhuizen and G. B. Lamont, ‘Multiobjective Evolutionary Algorithms: Analyzing the state of the art’, Evol. Comput., vol. 2, no. 1, pp. 125–147, 2002.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, Evol. Comput. IEEE Trans., vol. 6, no. 2, pp. 182–197, Apr. 2002.

I. Boussaïd, J. Lepagnot, and P. Siarry, ‘A survey on optimization metaheuristics’, Inf. Sci. (Ny)., vol. 237, pp. 82–117, 2013.

P. J. Villacorta, A. D. Masegosa, D. Castellanos, P. Novoa, and D. A. Pelta, ‘Sensitivity analysis in the scenario method: A multi-objective approach’, in International Conference on Intelligent Systems Design and Applications, ISDA, 2011, pp. 867–872.

R. Saravanan, S. Ramabalan, N. G. R. Ebenezer, and C. Dharmaraja, ‘Evolutionary multi criteria design optimization of robot grippers’, Appl. Soft Comput. J., vol. 9, no. 1, pp. 159–172, 2009.

M. Saadatseresht, A. Mansourian, and M. Taleai, ‘Evacuation planning using multiobjective evolutionary optimization approach’, Eur. J. Oper. Res., vol. 198, no. 1, pp. 305–314, 2009.

C. Zambrano-Vega, M. Cárdenas-Zea, and R. Aguirre-Pérez, ‘Un enfoque Multi-Objetivo a la optimización del Alineamiento Múltiple de Secuancias (MSA)’, Lat. Am. J. Comput., vol. 3, no.1, pp. 43–51, 2016.

S.-Y. Shin, I.-H. Lee, D. Kim, and B.-T. Zhang, ‘Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing’, IEEE Trans. Evol. Comput., vol. 9, no. 2, pp. 143–158, Apr. 2005.

P. Woźniak, ‘Preferences in multi-objective evolutionary optimisation of electric motor speed control with hardware in the loop’, Appl. Soft Comput., vol. 11, no. 1, pp. 49–55, 2011.

MATLAB, version 8.5.0 (R2015b). Natick, Massachusetts: The MathWorks Inc., 2015.

E. Talbi, Metaheuristics: from design to implementation, vol. 2009. John Wiley & Sons, 2009.

P. Novoa-Hernández, C. C. Corona, and D. A. Pelta, ‘A software tool for assisting experimentation in dynamic environments’, Appl. Comput. Intell. Soft Comput., vol. 2015, p. 5, 2015.

Published
2017-07-12
How to Cite
[1]
I. Jacho Sánchez, L. Arboleda, O. Cedeño, E. Samaniego, and P. Novoa, “An Evolutionary Multi-Objective Approach to the Problem of Building College Student Groups”, LAJC, vol. 4, no. 1, p. 8, Jul. 2017.
Section
Research Articles for the Regular Issue

Most read articles by the same author(s)