A new personalized learning path generation method: ACO-Map

Personalized curriculum sequencing is an important issue to achieve learning goal especially in e-learning systems. The main challenge of the traditional teaching system is providing courses suitable to different learners with different knowledge background. Therefore, many researchers developed adaptive learning path systems in order to promote the effectiveness and performance of learning process. Furthermore, an optimal adaptive learning path can help the learners in reducing the cognitive overload and disorientation. In this paper, a novel two stages adaptive learning path algorithm, which is called ACO-Map is proposed. Discovering groups of learners according to their knowledge patterns is performed based on the results of pre-test, in first stage. Then in second stage ant colony optimization as a metaheuristic method is applied to find a learning path based on Ausubel Meaningful Learning Theory. The investigation emphasizes the association between the learning content and the knowledge level of each learner in adaptive learning. The output of this algorithm is an adaptive learning path for each group of learners according to their needs.