The improvement of analytics in massive open online courses by applying data mining techniques†. Issue 4 (22nd September 2015)

• Record Type: Journal Article
• Authors: Maté, Alejandro; De Gregorio, Elisa; Cámara, José; Trujillo, Juan; Luján‐Mora, Sergio
• Other Names: Angeli Chrissanthi guestEditor.; Gil David guestEditor.
• Is Part Of: Expert systems. Volume 33:Issue 4(2016)
• Page Start: 374
• Page End: 382
• Publication Date: 2015-09-22
• Subjects: business intelligence -- analytics -- MOOC -- text mining; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12119
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The continuous increase in the number of open online courses has radically changed the traditional sector of education during the last years. These new learning approaches are very difficult to manage by using traditional management methods. This is one of the challenges in order to improve the new massive open online courses. In this paper, we propose a big data modelling approach, considering information from a big data analysis perspective, finding out which are the most relevant indicators in order to guarantee the success of the course. This novel approach is described along the paper using the case study of an open online course offered at our university. We describe the lessons learned in this work with the objective of providing general tools and indicators for other online courses. This will enhance the analysis and management of this kind of courses, contributing to their success.