Various forms of Peer-Learning Environments are increasingly being used in-secondary education, often to help build repositories of student generated objects. However, large classes can result in an extensive repository, can make it more challenging for students to search for suitable objects both reflect their interests and address their knowledge gaps. Recommender for Technology Enhanced Learning (RecSysTEL) offer a potential solution this problem by providing sophisticated filtering techniques to help to find the resources that they need in a timely manner. Here, a new for Recommendation in Peer-Learning Environments (RiPLE) is. The approach uses a collaborative filtering algorithm based upon factorization to create personalized recommendations for individual that address their interests and their current knowledge gaps. The is validated using both synthetic and real data sets. The results are, indicating RiPLE is able to provide sensible personalized for both regular and cold-start users under reasonable about parameters and user behavior.
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Khosravi et al. (2017) studied this question.