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



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Ruipérez Valiente, José Antonio and Jaramillo Morillo, Daniel and Joksimović, Srećko and Kovanović, Vitomir and Muñoz-Merino, Pedro J. and Gašević, Dragan (2021) Data-driven detection and characterization of communities of accounts collaborating in MOOCs. Future Generation Computer Systems, 125 . pp. 590-603. ISSN 0167-739X

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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.

Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2021)

Uncontrolled Keywords:Learning analytics; Educational data mining; Collaborative learning; Massive open online courses; Artificial intelligence
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:69377
Deposited On:22 Dec 2021 12:37
Last Modified:18 Oct 2022 09:05

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