Ant Colony Optimization for Adaptive Learning

-This paper aims to produce the best Learning Object (LO) for the learners in e-learning based on the learner characteristics. Delivery of optimal learning objects is a challenging task because the learning object (e.g. learning content, materials) should be suitable for learner’s knowledge. To forecast the best learning objects, the Ant Colony Optimization (ACO) with learner characteristics has been proposed. ACO is an important probabilistic technique used to find the good solutions for hard problems in a reasonable amount of computation time. For adaptive learning, ACO observed the most recurrent trails followed by the previous learner. Therefore, in this paper an advanced e-learning is proposed, which provides flexibility for the learners based on learner characteristics as some e-learning abandon to consider learner characteristics and desires. KeywordsE-learning, ACO, Learning Objects, Adaptive learning