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dc.contributor.authorGomez, Manuel J.-
dc.contributor.authorCalderón, Mario-
dc.contributor.authorSánchez, Víctor-
dc.contributor.authorGarcía Clemente, Félix J.-
dc.contributor.authorRuipérez Valiente, José A.-
dc.date.accessioned2025-01-20T08:00:49Z-
dc.date.available2025-01-20T08:00:49Z-
dc.date.issued2022-12-30-
dc.identifier.citationExpert Systems with Applications, 2022, Vol. 210 : 118400es
dc.identifier.issnPrint: 0957-4174-
dc.identifier.issnElectronic: 1873-6793-
dc.identifier.urihttp://hdl.handle.net/10201/148782-
dc.description© 2022 The Author(s). This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Published Manuscript version of a Published Work that appeared in final form in Expert Systems with Applications. To access the final edited and published work see https://doi.org/10.1016/j.eswa.2022.118400-
dc.description.abstractThe recent pandemic has changed the way we see education. During recent years, Massive Open Online Course (MOOC) providers, such as Coursera or edX, are reporting millions of new users signing up on their platforms. Though online review systems are standard among many verticals, no standardized or fully decentralized review systems exist in the MOOC ecosystem. In this vein, we believe that there is an opportunity to leverage available open MOOC reviews in order to build simpler and more transparent reviewing systems, allowing users to really identify the best courses out there. Specifically, in our research we analyze 2.4 million reviews (which is the largest MOOC reviews dataset used until now) from five different platforms in order to determine the following: (1) if the numeric ratings provide discriminant information to learners, (2) if NLP-driven sentiment analysis on textual reviews could provide valuable information to learners, (3) if we can leverage NLP-driven topic finding techniques to infer themes that could be important for learners, and (4) if we can use these models to effectively characterize MOOCs based on the open reviews. Results show that numeric ratings are clearly biased (63% of them are 5-star ratings), and the topic modeling reveals some interesting topics related with course advertisements, the real applicability, or the difficulty of the different courses.es
dc.formatapplication/pdfes
dc.format.extent12es
dc.languageenges
dc.publisherElsevieres
dc.relationSin financiación externa a la Universidad.es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMassive open online courseses
dc.subjectNatural languaje processinges
dc.subjectSentiment analysises
dc.subjectRecommendation systemses
dc.subjectOnline educationes
dc.titleLarge scale analysis of open MOOC reviews to support learners’ course selectiones
dc.typeinfo:eu-repo/semantics/articlees
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0957417422015081es
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2022.118400-
dc.contributor.departmentDepartamento de Ingeniería de la Información y las Comunicaciones-
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