Publication: Data-driven detection and characterization of communities of accounts collaborating in MOOCs
Authors
Jaramillo Morillo, Daniel ; Joksimovic, Srecko ; Kovanovic, Vitomir ; Muñoz Merino, Pedro J. ; Gasevic, Dragan ; Ruipérez Valiente, José Antonio
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Publisher
Elsevier
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DOI
https://doi.org/10.1016/j.future.2021.07.003
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info:eu-repo/semantics/article
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
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.
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Citation
Future Generation Computer Systems, 2021, Vol. 12, pp. 590-603
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