Paper Info
Title
Analysing The Predictive Power For Anticipating Assignment Grades In A Massive Open Online Course
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.
Year
DOI
Venue
2018
10.1080/0144929X.2018.1458904
BEHAVIOUR & INFORMATION TECHNOLOGY
Keywords
Field
DocType
MOOCs, prediction, learners' grades, indicators, learning analytics, edX
Data science,Learning analytics,Predictive power,Computer science,Anticipation,Knowledge management,Massive open online course,Java
Journal
Volume
Issue
ISSN
37
10-11
0144-929X
Citations 
PageRank 
References 
2
0.39
23
Authors
5
Name
Order
Citations
PageRank
Pedro De B. Marcos1286.12
Pedro J. Muñoz-Merino218922.13
Carlos Alario-Hoyos318130.68
Iria Estévez-Ayres412213.19
Carlos Delgado Kloos51121172.07