Personalized learning is a system that customizes the learning based on potential strength, need, skill and interest of a learner by providing instructions on pedagogies, curriculum, and environment. Today, Learners do exhaustive search on conventional search engines to retrieve relevant material. It is obligatory to enhance the personalized learning and offer the results based on the level of learning aptitude. To meet this objective, the work is intended to anticipate a hybrid ABC-PSO algorithm. ABC (Artificial Bee Colony) facilitates to explore the relevant content and follows best point set theorem to improve the convergence speed. PSO (Particle Swarm Optimization) facilitates to enhance the exploitation capability for searching novel candidates. To improve the searching capability, a puzzled search operating technique is taken in the optimal elucidation of the present iteration search. Experiment is carried on Yandex data set to evaluate its performance with an accuracy of 93.3% compared to state-of-art algorithms.
Volume 11 | 04-Special Issue
Pages: 157-167