Creación automática de equipos de estudiantes universitariosuna experiencia desde la asignatura Inglés

  1. Haro Gavidia, Marcelo
  2. Chabla Galarza, Guisella
  3. Montalvo Robalino, Miguel
  4. Coello Chabla, David
  5. Novoa-Hernández, Pavel
Journal:
Revista Ciencia UNEMI

ISSN: 2528-7737,  1390-4272

Year of publication: 2016

Issue Title: Diciembre

Volume: 9

Issue: 21

Pages: 58-67

Type: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Revista Ciencia UNEMI

Sustainable development goals

Abstract

One of the main goals for Higher Education is to educate students to work in teams. Such a skill not only improves their social behavior in the community, but also the ability for solving complex problems. Usually, the process of making teams is carried out by professorof the subject, who has to take into account several criteria (e.g. the presence of leader, heterogeneity of the team according the level of knowledge, sex, among others). When the subject has just few students, this task becomes easy. However, in the case of classes with a large number of students, this task becomes complex and there is no warranty about the accomplishment of the considered criteria. In that sense, the present work proposes a computational solution that automatizes the task of student teams building. Specifically, it was approached as a multi-objective combinatorial optimization problem, which was solved using a Pareto Dominance-based algorithm.In order to validate the proposal we performed several computational experiments involving real case studies from the English subject of three careers at the Technical State University of Quevedo. Results show that the proposed approach is able to build balanced teams according to the considered criteria.

Bibliographic References

  • UNESCO. (1997). La educación encierra un tesoro: informe a la UNESCO de la Comisión Internacional sobre la Educación para el siglo XXI, presidida por Jacques Delors (p. 301). Correo de la UNESCO.
  • Pozo, J. I., Echeverría, M. P., & (coord.). (2009). Psicología del aprendizaje universitario: la formación en competencias (p. 232). Ediciones Morata.
  • Glinz, P. E. (2005). Un acercamiento al trabajo colaborativo. Revista Iberoamericana de Educación, 35(2), 1–13.
  • Hughes, R. L., & Jones, S. K. (2011). Developing and assessing college student teamwork skills. New Directions for Institutional Research, 2011(149), 53–64. doi:10.1002/ir.380
  • Novoa-Hernández, P., Novoa-Hernández, M. A., & Rivero-Peña, Y. (2013). Propuesta de técnicas evolutivas para la confección automática de tribunales de trabajos de diploma. Revista Cubana de Ciencias Informáticas, 7(4), 90–99.
  • Novoa-Hernández, P. (2015). Optimización evolutiva multi-objetivo en la planificación de controles a clase en la educación superior cubana. Computación y Sistemas, 19(2), 321–335.
  • Escalera Fariñas, K., Infante Abreu, A. L., André Ampuero, M., & Rosete Suárez, A. (2014). Uso de estrategias de paralelización en algoritmos metaheurísticos para la conformación de equipos de software. Revista Cubana de Ciencias Informáticas, 8(3), 90–99.
  • Rivero Peña, Y., Novoa-Hernández, P., & Fernández Ochoa, Y. (2015). 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, 6(1), pp–35.
  • Ahmed, F., Jindal, A., & Deb, K. (2011). Cricket Team Selection Using Evolutionary Multi-objective Optimization. In Proceedings of the Second International Conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II (pp. 71–78). Berlin, Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-27242-4_9
  • Wegener, I. (2005). Complexity Theory: Exploring the Limits of Efficient Algorithms (p. 308). Springer Berlin Heidelberg.
  • Teachman, J. D. (1980). Analysis of Population Diversity: Measures of Qualitative Variation. Sociological Methods & Research, 8(3), 341–362. doi:10.1177/004912418000800305
  • Mueller, J. H., Schuessler, K. F., & Costner, H. L. (1977). Statistical Reasoning in Sociology. Houghton Mifflin.
  • Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49. doi:http://dx.doi.org/10.1016/j.swevo.2011.03.001
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182–197. doi:10.1109/4235.996017
  • MATLAB. (2015). version 8.5.0 (R2015b). Natick, Massachusetts: The MathWorks Inc.
  • Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117. doi:http://dx.doi.org/10.1016/j.ins.2013.02.041
  • Villacorta, P. J., Masegosa, A. D., Castellanos, D., Novoa, P., & Pelta, D. A. (2011). Sensitivity analysis in the scenario method: A multi-objective approach. In Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on (pp. 867–872). doi:10.1109/ISDA.2011.6121766
  • Saravanan, R., Ramabalan, S., Ebenezer, N. G. R., & Dharmaraja, C. (2009). Evolutionary multi criteria design optimization of robot grippers. Applied Soft Computing, 9(1), 159–172. doi:http://dx.doi.org/10.1016/j.asoc.2008.04.001
  • Saadatseresht, M., Mansourian, A., & Taleai, M. (2009). Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 198(1), 305–314. doi:http://dx.doi.org/10.1016/j.ejor.2008.07.032
  • Shin, S.-Y., Lee, I.-H., Kim, D., & Zhang, B.-T. (2005). Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. Evolutionary Computation, IEEE Transactions on, 9(2), 143–158. doi:10.1109/TEVC.2005.844166
  • Woźniak, P. (2011). Preferences in multi-objective evolutionary optimisation of electric motor speed control with hardware in the loop. Applied Soft Computing, 11(1), 49–55. doi:http://dx.doi.org/10.1016/j.asoc.2009.10.015
  • Talbi, E. G. (2009). Metaheuristics: From Design to Implementation (p. 500). John Wiley & Sons.
  • Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization. Cybernetics, IEEE Transactions on, 44(12), 2391–2404. doi:10.1109/TCYB.2014.2307319
Data source: Dialnet