<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

European Journal of Open, Distance and E-Learning

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Publicado en línea: 26 ene 2016

Páginas: 74 - 94

DOI: https://doi.org/10.1515/eurodl-2015-0014

Palabras clave
Social Network Analysis, Educational Data Mining, Learning Analytics, Sentiment analysis

© 2015 Vasileios Kagklis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Publicado en línea: 26 ene 2016

Páginas: 74 - 94

DOI: https://doi.org/10.1515/eurodl-2015-0014

Palabras claveSocial Network Analysis, Educational Data Mining, Learning Analytics, Sentiment analysis

© 2015 Vasileios Kagklis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Social Network Analysis, Educational Data Mining, Learning Analytics, Sentiment analysis