Large scale analysis of open MOOC reviews to support learners’ course selection

Gomez, Manuel J.; Calderón, Mario; Sánchez, Víctor; García Clemente, Félix J.; Ruipérez Valiente, José Antonio

Publication:
Large scale analysis of open MOOC reviews to support learners’ course selection

dc.contributor.author	Gomez, Manuel J.
dc.contributor.author	Calderón, Mario
dc.contributor.author	Sánchez, Víctor
dc.contributor.author	García Clemente, Félix J.
dc.contributor.author	Ruipérez Valiente, José Antonio
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2025-01-20T08:00:49Z
dc.date.available	2025-01-20T08:00:49Z
dc.date.issued	2022-12-30
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.abstract	The 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.format	application/pdf	es
dc.format.extent	12	es
dc.identifier.citation	Expert Systems with Applications, 2022, Vol. 210 : 118400
dc.identifier.doi	https://doi.org/10.1016/j.eswa.2022.118400
dc.identifier.issn	Print: 0957-4174
dc.identifier.issn	Electronic: 1873-6793
dc.identifier.uri	http://hdl.handle.net/10201/148782
dc.language	eng	es
dc.publisher	Elsevier	es
dc.relation	Sin financiación externa a la Universidad.	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0957417422015081	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Massive open online courses	es
dc.subject	Natural languaje processing	es
dc.subject	Sentiment analysis	es
dc.subject	Recommendation systems	es
dc.subject	Online education	es
dc.title	Large scale analysis of open MOOC reviews to support learners’ course selection	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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