Use of contextualized attention metadata for ranking and recommending learning objects | Zendy

Xavier Ochôa | Zendy; Erik Duval | Zendy

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Use of contextualized attention metadata for ranking and recommending learning objects

Author(s) -

Xavier Ochôa,

Erik Duval

Publication year - 2006

Publication title -

lirias (ku leuven)

Language(s) - English

Resource type - Conference proceedings

ISBN - 1-59593-524-X

DOI - 10.1145/1183604.1183608

Subject(s) - metadata , computer science , ranking (information retrieval) , information retrieval , learning object , scalability , learning to rank , rank (graph theory) , similarity (geometry) , recommender system , object (grammar) , world wide web , data science , artificial intelligence , database , image (mathematics) , mathematics , combinatorics

The tools used to search and find Learning Objects in different systems do not provide a meaningful and scalable way to rank or recommend learning material. This work propose and detail the use of Contextual Attention Metadata, gathered from the different tools used in the lifecycle of the Learning Object, to create ranking and recommending metrics to improve the user experience. Four types of metrics are detailed: Link Analysis Ranking, Similarity Recommendation, Personalized Ranking and Contextual Recommendation. While designed for Learning Objects, it is shown that these metrics could also be applied to rank and recommend other types of reusable components like software libraries.status: publishe

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