A learning analytics methodology for understanding social interactions in MOOCs

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dc.contributor.author Moreno-Marcos, Pedro Manuel
dc.contributor.author Alario-Hoyos, Carlos
dc.contributor.author Muñoz Merino, Pedro José
dc.contributor.author Estévez Ayres, Iria Manuela
dc.contributor.author Delgado Kloos, Carlos
dc.date.accessioned 2020-10-08T12:10:43Z
dc.date.available 2020-10-08T12:10:43Z
dc.date.issued 2019-10-01
dc.identifier.bibliographicCitation IEEE Transactions on Learning Technologies (2019), 12(4), pp.: 442 - 455.
dc.identifier.issn 1939-1382
dc.identifier.uri http://hdl.handle.net/10016/31149
dc.description.abstract One of the characteristics of MOOCs (Massive Open Online Courses) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer learners behaviour and outcomes. It is not feasible for teachers to process all forum messages and automated tools and analysis are required. Although there are some tools for analysing learners interactions, there is a need for methodologies and integrated tools that help to interpret the learning process based on social interactions in the forum. This work presents the 3S (Social, Sentiments, Skills) learning analytics methodology for analysing forum interactions in MOOCs. This methodology considers a temporal analysis combining the social, sentiments and skill dimensions that can be extracted from forum data. We also introduce LAT'S, a Learning Analytics tool for edX / Open edX related to the three dimensions (3S), which includes visualisations to guide the proposed methodology. We apply the 3S methodology and the tool to a MOOC on Java programming. Results showed, among others, the action-reaction effect produced when learners increase their participation after instructor's events. Moreover, a decrease of positive sentiments over time and before deadlines of open-ended assignments was also observed and that there were certain skills which caused more troubles (e.g., arrays and loops). These results acknowledge the importance of using a learning analytics methodology to detect problems in MOOCs.
dc.description.sponsorship This work was supported in part by the Erasmus+ Programme of the European Union, projects MOOC Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), SHEILA (562080-EPP-1- 2015-BE-EPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1- ES-EPPKA2-CBHE-JP), by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715); in part by the Spanish Ministry of Science, Innovation and Universities, projects RE-SET (TIN2014-53199-C3-1-R), SNOLA (TIN2015-71669-REDT), and Smartlet (TIN2017-85179-C3-1-R); in part by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER); and in part by the Spanish Ministry of Science, Innovation, and Universities, under an FPU fellowship (FPU016/00526).
dc.format.extent 13
dc.language.iso eng
dc.publisher IEEE Computer Society
dc.rights ©2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
dc.subject.other Discussion forums
dc.subject.other Distance learning
dc.subject.other Learning environments
dc.subject.other Visualization
dc.title A learning analytics methodology for understanding social interactions in MOOCs
dc.type article
dc.description.status Publicado
dc.subject.eciencia Educación
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TLT.2018.2883419
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/561533
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/586120/LALA
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/562080/SHEILA
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2715
dc.relation.projectID Gobierno de España. TIN2014-53199-C3-1-R/RESET
dc.relation.projectID Gobierno de España. TIN2015-71669-REDT/SNOLA
dc.relation.projectID Gobierno de España. TIN2017-85179-C3-1-R/Smartlet
dc.relation.projectID Gobierno de España. FPU016/00526
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 442
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 455
dc.identifier.publicationtitle IEEE Transactions on Learning Technologies
dc.identifier.publicationvolume 12
dc.identifier.uxxi AR/0000022296
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder European Commission
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