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

Autor/inn/en	Kagklis, Vasileios; Karatrantou, Anthi; Tantoula, Maria; Panagiotakopoulos, Chris T.; Verykios, Vassilios S.
Titel	A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education
Quelle	In: European Journal of Open, Distance and E-Learning, 18 (2015) 2, S.74-94 (21 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1027-5207
Schlagwörter	Case Studies; Distance Education; Electronic Learning; Foreign Countries; Open Universities; Opinions; Student Attitudes; Social Networks; Data Processing; Data Analysis; Conferences (Gatherings); Online Courses; Computer Mediated Communication; Synchronous Communication; Discussion Groups; Discussion (Teaching Technique); Educational Improvement; Teacher Student Relationship; Information Technology; Greece + Suchen Sie Ihr Suchwort? Case study; Fallstudie; Case Study; Distance study; Distance learning; Fernunterricht; Ausland; Offene Universität; Lehrmeinung; Schülerverhalten; Social network; Soziales Netzwerk; Datenverarbeitung; Auswertung; Online course; Online-Kurs; Computerkonferenz; Teaching improvement; Unterrichtsentwicklung; Teacher student relationships; Lehrer-Schüler-Beziehung; Informationstechnologie; Griechenland
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. (As Provided).
Anmerkungen	European Distance and E-Learning Network. Available from: EDEN Secretariat, c/o Budapest University of Technology and Economics, Egry Jozsef u 1, H-1111, Hungary. Tel: +36-1-463-1628; Fax: +36-1-463-1858; e-mail: secretariat@eurodl.org; Web site: http://www.eurodl.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01