| Abstract [eng] |
The key aim of using information technologies for learning is increasing the quality and effectiveness of learning, and improving learner’s and teacher’s work. Contemporary education is unimaginable without information technologies and utilizing their facilities. Learning objects (LO) and learning units (LU) are the examples of those possibilities. However, only a partially qualitative effect could be obtained without personalizing those components of e-learning and without finding personalized learning paths in LU. This research work is aimed to solving the problem of LU personalization, paying a special attention to finding personalized learning paths in LU. The finding of those paths is based on learners’ needs in terms of their learning styles. In this work, an adaptive LU personalization method is proposed. The method is based on Swarm Intelligence approach (Ant Colony Optimization), its adaptation and application in elearning context as well as its extension with a view to select optimal learning paths for learners according to their learning styles addressing both static and dynamic LU. In the work, computer simulations were also performed. These experiments have shown that the method created is suitable to personalizing learning paths according to learners’ learning styles. A prototype of e-learning system based on the method proposed was implemented and tested in practice. The experimental part of the research presents the statistical data obtained and describes the prototype, as well as summarizes the experimental results. According to the experimental results, recommendations based on the method proposed improve learners’ learning results and shorten their learning time. This fact indicates that the developed adaptive method for personalizing LU is practically applicable in e-learning and enhances the learning quality and efficiency. |
