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Empowering MOOCs Through Course Certifying Agency Framework

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Applied Computing and Information Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 695))

Abstract

The arrival of Massive Open Online Course (MOOC) was hailed by many learners around the globe. More universities are willing to offer their top notch professor’s courses as MOOC. However, when utilizing the knowledge from MOOCs, learners need to go through a number of hurdles—getting course completion certification (MS Global Learning Consortum: An example of LIP accessibility information, IMS Global Learning Consortium Inc., 2001, [10]) from various providers, get recognition of the knowledge level of a MOOC in the related domain, and make it searchable for various purposes. In this paper, we propose a MOOC course certifying agency framework, which merges learners’ profiles from various MOOC providers so consolidated profiles are available in one place. Standards such as IMS LIP and Dublin Core (Feigenbaum and Prud’Hommeaux in SPARQL by example: a tutorial, Cambridge Semantics, 2011, [5]) are adopted and expanded to describe relate MOOC course profiles, learner profiles, learning goals, and related skill sets. It enables matching of qualified learner profiles for a job position and/or to identify a set of related MOOC course profiles for some learning goal. The potential employers look for a matching skill set from converged learner profiles through the agency. Each skill for the position goes through a mapping procedure with a corresponding MOOC course profile. After mapping skills to corresponding MOOCs, the framework searches for the converged profiles. The result is the list of learners who match or almost match a given job description.

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Correspondence to Yeong-tae Song .

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Song, Yt., Wang, Y., Yoon, Y. (2017). Empowering MOOCs Through Course Certifying Agency Framework. In: Lee, R. (eds) Applied Computing and Information Technology. Studies in Computational Intelligence, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-319-51472-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-51472-7_13

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