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WorldCist'18 - 6th World Conference on Information Systems and Technologies

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COCO: Semantic-Enriched Collection of Online Courses at Scale with Experimental Use Cases

With the proliferation in number and scale of online courses, several challenges have emerged in supporting stakeholders during their delivery and fruition. Machine Learning and Semantic Analysis can add value to the underlying online environments in order to meet a subset of such challenges (e.g. classification, retrieval, and recommendation). However, conducting reproducible experiments in such applications is still an open problem due to the lack of available datasets in Technology-Enhanced Learning (TEL), mostly small and local. In this paper, we propose COCO, a novel semantic-enriched collection including over 43K online courses at scale, 16K instructors and 2,5M learners who provided 4,5M ratings and 1,2M comments in total. This outruns existing TEL datasets in terms of scale, completeness, and comprehensiveness. Besides describing the collection and the structure, we depict and evaluate two potential use cases as meaningful examples of the large variety of multi-disciplinary studies made possible by having COCO.

Danilo Dessì
University of Cagliari
Italy

Gianni Fenu
University of Cagliari
Italy

Mirko Marras
University of Cagliari
Italy

Diego Reforgiato Recupero
University of Cagliari
Italy

 

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