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Xavier Ochoa
Researcher at New York University
Publications - 126
Citations - 2911
Xavier Ochoa is an academic researcher from New York University. The author has contributed to research in topics: Learning analytics & Learning object. The author has an hindex of 24, co-authored 121 publications receiving 2523 citations. Previous affiliations of Xavier Ochoa include Escuela Superior Politecnica del Litoral & Open University of Catalonia.
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Journal ArticleDOI
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges
Katrien Verbert,Nikos Manouselis,Xavier Ochoa,Martin Wolpers,Hendrik Drachsler,Ivana Bosnić,Erik Duval +6 more
TL;DR: In this article, the authors present a context framework that identifies relevant context dimensions for TEL applications and present an analysis of existing TEL recommender systems along these dimensions, based on their survey results, they outline topics on which further research is needed.
Quantitative Analysis of Learning Object Repositories
Xavier Ochoa,Erik Duval +1 more
TL;DR: In this article, the authors conducted a detailed quantitative study of the publication process of learning objects in repositories and found that the amount of learning object is distributed among repositories according to a power law and the repositories mostly grow linearly.
Journal ArticleDOI
Relevance Ranking Metrics for Learning Objects
Xavier Ochoa,Erik Duval +1 more
TL;DR: An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric.
Journal ArticleDOI
Quantitative Analysis of Learning Object Repositories
Xavier Ochoa,Erik Duval +1 more
TL;DR: The main findings are that the amount of learning objects is distributed among repositories according to a power law, the repositories mostly grow linearly, and the amountof learning objects published by each contributor follows heavy-tailed distributions.
Journal ArticleDOI
Automatic evaluation of metadata quality in digital repositories
Xavier Ochoa,Erik Duval +1 more
TL;DR: A set of scalable quality metrics for metadata based on the Bruce & Hillman framework for metadata quality control is presented and it is found that several metrics, especially Text Information Content, correlate well with human evaluation and that the average of all the metrics are roughly as effective as people to flag low-quality instances.