In the course of the last few years, many educational and research communities have been deeply invested in the development of learning analytics. Learning analytics measures the effectiveness and efficiency of learning environments, in order to understand the needs of learners and to improve the teaching process. The research presented in this paper uses Parallel Particle Swarm Optimization (PPSO) mechanism to analyze and predict a dynamic learning path for learners based on competence and meta-competence values observed in a learning environment. The proposed system is able to auto-configure and auto-customize itself to offer personalized and individualized instruction, and calculate an optimal learning pathway for learners. Furthermore, it provides on-demand and adaptive support for learners based on their needs. Experimental evaluations - carried out within a Java Programming course - demonstrate the effectiveness of the proposed system.