A Multiobjetive Approach to Recommend Learning Paths for Group of Users
DOI:
https://doi.org/10.5753/rbie.2019.27.03.336Keywords:
Learning path recommendation, Multi-objective optimization, Combinatorial optimizationAbstract
Defining learning paths is a crucial issue for effective learning. This article proposes the application of an evolutionary algorithm (NSGA-II) to optimize the recommendation process of learning paths for a group of individuals taking into account multiple criteria: satisfaction of the team members and time spent in the accomplishment of the activity. The proposed method was applied in two scenarios: an online course and presencial course. The method was evaluated regarding the performance, being compared to the exhaustive and random approaches; And regarding the pedagogical aspect, being compared with random and self selected methods. The results showed the potential of the proposed method from the computational point of view as well as the pedagogical point of view.
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Copyright (c) 2019 Péricles B. C. de Miranda, Rafael Ferreira, Mayara S. O. Castro, Giuseppe Fiorentino Neto, Samuel J. Souza, Lucas A. Santos, Lizandra L. S. B. Silva
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
