1. bookVolume 18 (2015): Issue 2 (December 2015)
Journal Details
License
Format
Journal
eISSN
1027-5207
First Published
11 Dec 2014
Publication timeframe
2 times per year
Languages
English
access type Open Access

A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education

Published Online: 26 Jan 2016
Volume & Issue: Volume 18 (2015) - Issue 2 (December 2015)
Page range: 74 - 94
Journal Details
License
Format
Journal
eISSN
1027-5207
First Published
11 Dec 2014
Publication timeframe
2 times per year
Languages
English
Abstract

Online fora have become not only one of the most popular communication tools in e-learning environments, but also one of the key factors of the learning process, especially in distance learning, as they can provide to the students involved, motivation for collaboration in order to achieve a common goal. The purpose of this study is to analyse data related to the participation of postgraduate students in the online forum of their course at the Hellenic Open University. The content of the messages posted is analysed by using text mining techniques, while the network through which the students interact is processed through social network analysis techniques. Furthermore, sentiment analysis and opinion mining is applied on the same dataset. Our aim is to study students’ attitude towards the course and its features, as well as to model their sentiment behaviour over time, and finally to detect if and how this affected their overall performance. The combined knowledge attained from the aforementioned techniques can provide tutors with practical and valuable information for the structure and the content of the students’ exchanged messages, the patterns of interaction among them, the trend of sentiment polarity during the course, so as to improve the educational process.

Keywords

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