Fuzzy Logic Based Context Aware Recommender for Smart E-learning Content Delivery

There has been a huge growth of data in education due to integration of ICT in educational and the evolution of E-learning, to bring about technology enabled learning. This huge data in education has subjected learners to information overload in education, without a well-known mechanism to enable learners select their relevant learning content from the huge educational data. This has subjected learners, in most of the times, to take a lot of time accessing data which could not be relevant to their particular learning needs. This research therefore creates a model which will recommend relevant learning content to a learner, from the huge available data. In designing the model we employed context awareness recommender approach. The model will collect the learner's context. It will then apply type-1 fuzzy logic data mining method to recommend relevant learning content to the learner; based on the learner's assesment score and the time taken to complete the assesment test. The model was tested using a primary designed dataset stored in the database. The model gave recommendations that were commensurate with the assesment test marks and the time taken to complete the exams. Thus the higher the assessment score, the higher the crisp output; while the higher the time taken to complete the exams, the lower the crisp output.