The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

Abstract

The approach that we propose in this paper is part of the optimization of the learning path in the e-learning environment. It relates more precisely to the adaptation and the guidance of the learners according to, on the one hand, their needs and cognitive abilities and, on the other hand, the collective experience of co-learners. This work is done by an optimizer agent that has the specificity to provide to each learner the best path from the beginning of the learning process to its completion. The optimization of this approach is determined automatically and dynamically, by seeking the path that is more marked by success. This determination is concluding according to the vision of the pedagogical team and the collective experience of the learners. At the same time, we search of the path that is more adapted to the specificities of the learner regarding preferences, level of knowledge and learner history. This operation is accomplished by exploiting their profile for perfect customization, and the adaptation of ant colony algorithm for guidance tends towards maximizing the acquisition of the learner. The design of our work is unitary; it is based on the integration of individual, collective factors of the learner. Moreover, the results are very conclusive. They show that the proposed approach can efficiently select the optimal path and that it participates fully in the satisfaction and success of the learner.

On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

“…An adaptive online learning model proposed by Birjali M et al [25] is a learning path planning algorithm based on the combination of genetic and ant colony algorithms of MapReduce, which can provide learning goals and adaptively generate learning paths for each online learner. Considering the needs and cognitive abilities of learners and the collective experience of co-learners, Imane Kamsa et al [26] came up with a learning path algorithm of ant colony optimization that combines individual and group learning experience. The algorithm adaptively and dynamically finds the optimal path which is more suitable for learners in terms of learning preferences, knowledge levels and historical data on learning, and can improve learner performance and satisfaction.…”

“…According to formula (26), calculate the Euclidean norm of w i vector of each row in the eigenvector matrix V (e) h * m and that of w j vector of each row in the eigenvector matrix…”

Geração de Sequências Curriculares Adaptativas baseada no Perfil dos Alunos e Materiais Didáticos utilizando o Algoritmo NSGA-III

“…A Tabela 1 apresenta uma comparação entre os trabalhos relacionados utilizando o modelo apresentado. Em todos os trabalhos analisados, os objetivos para a personalização da entrega do conteúdo foram avaliados de maneira conjunta, seja utilizando a soma ponderada dos objetivos [Machado et al 2018, Kamsa et al 2018] ou utilizando a programação por metas para encontrar soluções que possam alcançar uma meta pré-determinada , Christudas et al 2018. Essas técnicas reduzem o problema de otimização multiobjetivo a um problema de otimização monoobjetivo.…”