Recommending suitable learning paths according to learners' preferences: Experimental research results

The paper deals with the problem of personalising learning units with the main focus on finding personalised learning paths in learning units. Finding suitable learning paths is based on students' needs in terms of their learning styles. It has been shown that learning path in static and dynamic learning units can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model, mainly by adapting ant colony optimisation method based on collaboration and pheromones. In the paper, experimental results of applying the proposed approach in practise are presented. The results of empirical experiment have shown that learning in the proposed prototype of e-learning system applying created recommending method improves students' learning results and saves their learning time. This fact indicates that the developed adaptive method for personalising learning units is practically applicable in e-learning and enhances the learning quality.