A LEARNER CENTRIC BLENDED E- LEARNING CONTENT RECOMMENDATION SYSTEM

Abstract

The growth of personal learning experiences has made it harder for students to find suitable learning resources. Individual guidelines have been used to facilitate learners' experiences in personal learning settings, and this technology will provide learners with appropriate learning content. This algorithm evaluates the multi-dimensional characteristics of the content, student ranking and the order and temporal patterns of the obtained information in a single model in order to increase the accuracy of its recommendations. There are two modules in the current solution. Late trends of obtaining resources are found and presented in two formats with the balanced association rule mining and a compact tree structure known as the sequence tree in the sequential-based suggestion module. The learner's preference tree is implemented to consider multifarious attributes of papers, ranking of learners and order of materials in the attribute-based module, after clustering learners with latent patterns by K-means algorithm. The hybrid approaches are blended, weighted and cascaded with effective suggestions for implementations. The tests demonstrate that the solution proposed exceeds evolutionary mechanisms in terms of accuracy, reminder and intra-list similitude. Improved consistency of the guidelines and a reduction of the sparseness dilemma are the key contributions by integrating qualitative data through order and temporal trends of the resources accessed, apprenticeship ratings and multidimensional materials