Analysing the predictive power for anticipating assignment grades in a massive open online course

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dc.contributor.author Moreno-Marcos, Pedro Manuel
dc.contributor.author Muñoz Merino, Pedro José
dc.contributor.author Alario-Hoyos, Carlos
dc.contributor.author Estévez Ayres, Iria Manuela
dc.contributor.author Delgado Kloos, Carlos
dc.date.accessioned 2021-03-25T11:27:16Z
dc.date.available 2021-03-25T11:27:16Z
dc.date.issued 2018-04-04
dc.identifier.bibliographicCitation Moreno Marcos, P. M., Muñoz Merino, P. J., Alario Hoyos, C., Estévez Ayres, I. & Delgado Kloos, C. (2018). Analysing the predictive power for anticipating assignment grades in a massive open online course. Behaviour & Information Technology, 37(10–11), pp. 1021–1036.
dc.identifier.issn 0144-929X
dc.identifier.uri http://hdl.handle.net/10016/32229
dc.description.abstract The learning process in a MOOC (Massive Open Online Course) can be improved from knowing in advance learners' grades on different assignments. This would be very useful to detect problems with enough time to take corrective measures. In this work, the aim is to analyse how different course scores can be predicted, what elements or variables affect the predictions and how much and in which way it is possible to anticipate scores. To do that, data from a MOOC about Java programming have been used. Results show the importance of indicators over the algorithms and that forum-related variables do not add power to predict grades, unlike previous scores. Furthermore, the type of task can vary the results. Regarding the anticipation, it was possible to use data from previous topics but with worse performance, although values were better than those obtained in the first seven days of the current topic.
dc.description.sponsorship EACEA through 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 Consejeria de Educacion, Juventud y Deporte, Comunidad de Madrid (Madrid Regional Government), through the eMadrid Excellence Network (S2013/ICE-2715), and by the Ministry of Economy and Competitiveness in Spain, projects RESET (TIN2014-53199-C3-1-R), SNOLA (TIN2015-71669-REDT) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the Agencia Estatal de Investigacion (State Research Agency) in Spain, and the European Regional Development Fund (FEDER). It has also been supported by the Ministry of Education, Culture and Sport in Spain, under an FPU fellowship (FPU016/00526).
dc.format.extent 16
dc.language.iso eng
dc.publisher Taylor & Francis
dc.rights © Taylor & Francis 2018
dc.subject.other Moocs
dc.subject.other Prediction
dc.subject.other Learners' grades
dc.subject.other Indicators
dc.subject.other Learning analytics
dc.subject.other Edx
dc.title Analysing the predictive power for anticipating assignment grades in a massive open online course
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1080/0144929X.2018.1458904
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2715
dc.relation.projectID Gobierno de España. TIN2014-53199-C3-1-R
dc.relation.projectID Gobierno de España. TIN2017-85179-C3-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1021
dc.identifier.publicationissue 10-11
dc.identifier.publicationlastpage 1036
dc.identifier.publicationtitle Behaviour & Information Technology
dc.identifier.publicationvolume 37
dc.identifier.uxxi AR/0000022277
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Educación, Cultura y Deporte (España)
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