Data-driven detection and characterization of communities of accounts collaborating in MOOCs

Jaramillo Morillo, Daniel; Joksimovic, Srecko; Kovanovic, Vitomir; Muñoz Merino, Pedro J.; Gasevic, Dragan; Ruipérez Valiente, José Antonio

Publication:
Data-driven detection and characterization of communities of accounts collaborating in MOOCs

dc.contributor.author	Jaramillo Morillo, Daniel
dc.contributor.author	Joksimovic, Srecko
dc.contributor.author	Kovanovic, Vitomir
dc.contributor.author	Muñoz Merino, Pedro J.
dc.contributor.author	Gasevic, Dragan
dc.contributor.author	Ruipérez Valiente, José Antonio
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2025-01-21T09:40:51Z
dc.date.available	2025-01-21T09:40:51Z
dc.date.issued	2021-12
dc.description	© 2021 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 Future Generation Computer Systems. To access the final edited and published work see https://doi.org/10.1016/j.future.2021.07.003
dc.description.abstract	Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.
dc.format	application/pdf	es
dc.format.extent	14	es
dc.identifier.citation	Future Generation Computer Systems, 2021, Vol. 12, pp. 590-603
dc.identifier.doi	https://doi.org/10.1016/j.future.2021.07.003
dc.identifier.issn	Print: 0167-739X
dc.identifier.issn	Electronic: 1872-7115
dc.identifier.uri	http://hdl.handle.net/10201/148895
dc.language	eng	es
dc.publisher	Elsevier	es
dc.relation	Authors want to acknowledge support from PROF-XXI project, Spain (609767-EPP-1-ES-EPPKA2-CBHE-JP), the European Commission and the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926).	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0167739X21002570?via%3Dihub
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	Learning analytics	es
dc.subject	Educational data mining	es
dc.subject	Collaborative learning	es
dc.subject	Massive Open Online Courses	es
dc.subject	Artificial Intelligence	es
dc.title	Data-driven detection and characterization of communities of accounts collaborating in MOOCs	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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