Towards a reference context model for adaptive learning

A curriculum sequence represents a match between learners’ preferences, needs, and surroundings from one side, and the learning content characteristics and the pedagogical requirements from the other side. The curriculum sequence adaptation problem (CSA) is considered as an important issue in adaptive and personalized learning field. It concerns the dynamic generation of a personal optimal learning path for a specific learner. This problem has gained an increased research interest in the last decade, and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents a novel GA-based approach modeled as an objective optimization problem to deal with this problem. The experimental results from simulations showed that the proposed GA could outperform particle swarm optimization (PSO) and a random search approach in many simulated datasets. Moreover, from a pedagogical perspective, positive learners’ feedback and high acceptance towards the proposed approach is indicated.

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Towards a reference context model for adaptive learning

Cited by 3 publications

References 19 publications

An enhanced genetic algorithm for solving learning path adaptation problem

An enhanced genetic algorithm for solving learning path adaptation problem

An improved constrained learning path adaptation problem based on genetic algorithm

A novel genetic algorithm for curriculum sequence optimization

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