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.
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1. Abel, F., Bittencourt, I.I., Costa, E., Henze, N., Krause, D., & Vassilev, J. (2010). Recommendations in Online Discussion Forums for E-Learning Systems. IEEE Transactions on Learning Technologies, 3(2), 165-176.
2. Agathangelou, P., Katakis, I., Kokkoras, F., & Ntonas K. (2014). Mining Domain-Specific Dictionaries of Opinion Words. In B. Benatallah, A. Bestavros, Y. Manolopoulos, A. Vakali & Y. Zhang (Eds.), Proceedings of the 15th International Conference on Web Information System Engineering (WISE 2014) (pp. 47-62). Thessaloniki, Greece, 12-14 October, 2014. LNCS 8786, Springer. doi: http://dx.doi.org/10.1007/978-3-319-11749-2_4
3. Anderson, T. (2004). Towards a theory of online learning. In T. Anderson & F. Elloumi (Eds.), Theory and practice of online learning (pp. 33-60). CA: Athabasca University Press.
5. Anderson, T., & Garrison, D.R. (1998). Learning in a networked world: New roles and responsibilities. In C. Gibson (Ed.), Distance learners in higher education (pp. 97-112). Madison: Atwood Publishing.
6. Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
7. Berland, M., Baker, R.S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. doi:10.1007/s10758-014-9223-7
11. Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. doi:10.1109/MIS.2013.30.
12. Carlos, A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons. ISBN 978-1-118-01094-5.
13. Carsten, U., Boreau, K., & Stepanyan, K. (2010). Who students interact with? A social network analysis perspective on the use of Twitter in Language Learning. In M. Wolpers, P. Kirschner, M. Scheffel & V. Dimitrova (Eds.), Proceedings of 5th European Conference on Technology Enhanced Learning (pp. 432-437). Sankt Augustin: Springer.
14. Choi, I., Land, S. M., & Turgeon, A. J. (2005). Scaffolding peer-questioning strategies to facilitate metacognition during online small group discussion. Instructional Science, 33, 483-511.
15. D’ Andrea, A., Ferri, F., & P. Grifoni (2009). An Overview of Methods for Virtual Social Network Analysis. In A. Abraham, A.E. Iassanien & V. Snášel (Eds.), Computational Social Network Analysis: Trends, Tools and Research Advances. Springer. ISBN 978-1-84882-228-3.
16. Groves, M., & O’Donoghue, J. (2009). Reflections of Students in Their Use of Asynchronous Online Seminars. Educational Technology & Society, 12(3), 143-149.
17. Hülsmann, T. (2009). Access and Efficiency in the Development of Distance Education and E-Learning. In U. Bernath, A. Szücs, A. Tait & M. Vidal (Eds.), Distance and E-Learning in Transition - Learning Innovation, Technology and Social Challenges (p. 121). London/Hoboken: ISTE Ltd and John Wiley & Sons, Inc.
18. Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K., (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium.
19. Karaiskakis, D., Kalles, D., & Hadzilacos, Th. (2008). Profiling Group Activity of Online Academic Workspaces: the Hellenic Open University case study. International Journal of Web-based Learning and Teaching Technology, 3(3), 1-15.
20. Kim, S.M., & Hovy, E.H. (2006). Identifying and Analyzing Judgment Opinions. Proceedings of the Human Language Technology / North American Association of Computational Linguistics conference (HLT-NAACL 2006). New York, NY. http://acl.ldc.upenn.edu/P/P06/P06-2063.pdf
21. Kostourakis, G., Panagiotakopoulos, C., & Vergidis, D. (2008). A contribution to the Hellenic Open University: Evaluation of the Pedagogical Practices and the use of ICT on Distance Education. International Review of Research in Open and Distance Learning, 9(2). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/424/1044
22. Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool publishers.
23. Lotsari, E., Verykios, V.S., Panagiotakopoulos, C., & Kalles, D. (2014). A Learning Analytics Methodology for Student Profiling. In A. Likas, K. Blekas & D. Kalles (Eds.), Artificial Intelligence: Methods and Applications, 8th Hellenic Conference on AI, SETN 2014, Ioannina, Greece, May 15-17, 2014. Proceedings (Volume 8445 of the series Lecture Notes in Computer Science pp. 300-312). Retrieved from http://link.springer.com/chapter/10.1007%2F978-3-319-07064-3_24
24. Manning, C. D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
25. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: capturing favorability using natural language processing. Proceedings of the 2nd International Conference on Knowledge Capture, 70-77.
26. Ortigosa, A., Martín, J.M., & Carro, R.M. (2014). Sentiment analysis in Facebook and its application to e-learning.
28. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).