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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Clustering Learning Objects for Improving Their Recommendation via Collaborative Filtering Algorithms
verfasst von : Henrique Lemos dos Santos, Cristian Cechinel, Ricardo Matsumura Araujo, Miguel-Ángel Sicilia
Erschienen in: Metadata and Semantics Research
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Collaborative Filtering can be used in the context of e-learning to recommend learning objects to students and teachers involved with the teaching and learning process. Although such technique presents a great potential for e-learning, studies related to this application in this field are still limited, mostly because the inexistence of available datasets for testing and evaluating. The present work evaluates a pre-processsing method through clustering for future use of collaborative filtering algorithms. For that we use a large data set collected from the MERLOT repository. The initial results point out that clustering learning objects before the use of collaborative filtering techniques can improve the recommendations performance.