Abstract
This paper presents the model of units’ categorization, which aims to improve course materials’ difficulties and to maintain learners’ motivation. Moreover, it presents the application of the adapted genetic algorithm to compute the average and the maximum fitness function that reveal the pertinence of a new parameter. However, the purpose is to propose the most pertinent and adapted units to a learner. The relevance of a unit is quantified by the success of several learners and units’ belonging to the right pedagogical sequence. The results obtained by the implementation of the adapted genetic algorithm are based on the proposed model. Therefore, there is a thorough analysis of the convergence of the average fitness function with the maximum fitness function.
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Benabdellah, N.C., Gharbi, M., Bellafkih, M. (2017). Units’ Categorization Model: The Adapted Genetic Algorithm for a Personalized E-Content. In: Rocha, Á., Serrhini, M., Felgueiras, C. (eds) Europe and MENA Cooperation Advances in Information and Communication Technologies. Advances in Intelligent Systems and Computing, vol 520. Springer, Cham. https://doi.org/10.1007/978-3-319-46568-5_15
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DOI: https://doi.org/10.1007/978-3-319-46568-5_15
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