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Context Aware Recommender Systems and Techniques in offering Smart Learning: A Survey and Future work

recommender systems (CARS) that have been designed to perform specific tasks in facilitating smart learning. Such context aware recommender systems have potentially played important roles in education, especially in recommending to a learner certain activities. In this research we reviewed existing context aware recommender systems used in facilitating pedagogical smart learning. Our survey paper addressed the following research questions; which contexts are used in smart learning, how the contexts are collected, recommended activities, data mining techniques, and future work in CARS for smart learning. In our findings we addressed the research questions with reference to smart learning. We identified that there are numerous context aware recommender systems; but despite all existing CARS in smart learning, there are several challenges and gaps that are still existing. Such challenges include: Absence of CARS that perfectly fits the ever changing learner’s needs and preferences, and lack of standard database for smart learning, among other issues highlighted in our future work.