Analysing the predictive power for anticipating assignment grades in a massive open online course. Issue 10 (2nd November 2018)

• Record Type: Journal Article
• Authors: Moreno-Marcos, Pedro Manuel; Muñoz-Merino, Pedro J.; Alario-Hoyos, Carlos; Estévez-Ayres, Iria; Delgado Kloos, Carlos
• Is Part Of: Behaviour & information technology. Volume 37:Issue 10/11(2018)
• Page Start: 1021
• Page End: 1036
• Publication Date: 2018-11-02
• Subjects: MOOCs -- prediction -- learners' grades -- indicators -- learning analytics -- edX; Electronic data processing -- Periodicals; Human engineering -- Periodicals; Information technology -- Periodicals; 303.4833
• DOI: 10.1080/0144929X.2018.1458904
• Languages: English
• ISSNs: 0144-929X

Abstract

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.