Using text mining to evaluate student interaction in virtual learning environments

• Published in: 2018 IEEE World Engineering Education Conference (EDUNINE) pp. 1 - 6
• Main Authors: Buenano-Fernandez, Diego, Villegas-Ch, W., Lujan-Mora, Sergio
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2018
• Subjects: Education; Electronic mail; Massive Open Online Course; MOOC; Opinion mining; Supervised learning; Text mining; Twitter
• DOI: 10.1109/EDUNINE.2018.8450969

The field of education has been affected by globalization and the constant increase of online courses. The high number of students enrolled in these learning environments and their constant interaction with platforms generate a large amount of data that is difficult to handle with traditional methods of data analysis. The permanence of students in these courses poses challenges aimed at raising their level of commitment and motivation. Several articles with this approach have been identified in the literature analyzed in this work. Some of them are related to the application of text mining techniques aimed at analyzing the interaction of students in these environments. This interaction is based on entries included in discussion forums, emails or interaction in social networks. In this article, we explore the interaction of students through text mining techniques in different student interaction environments in a massive open online course (MOOC). The research focuses on the calculation and analysis of the frequency of terms, the analysis of concordances and groupings in n-grams.