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

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), 165-176.10.1109/TLT.2009.40Search in Google Scholar

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 15^th 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_4Search in Google Scholar

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.Search in Google Scholar

4. Anderson, P. (2007). What is Web 2.0? Ideas, technologies and implication for education. JISC Technology and Standards Watch. Retrieved 10-1-2014 from http://www.jisc.ac.uk/media/documents/techwatch/tsw0701b.pdfSearch in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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/s10758-014-9223-710.1007/s10758-014-9223-7Search in Google Scholar

8. Bradley, A.J., & McDonald, M.P. (2011, October 26). Social Media versus Knowledge Management [Blog post]. Harvard Business Review Blog. Retrieved 10-1-2014 from http://blogs.hbr.org/2011/10/social-media-versus-knowledge/Search in Google Scholar

9. Brindley, J.E., Walti, C., & Blaschke, L. M. (2009). Creating Effective Collaborative Learning Groups in an Online Environment. IRRODL, 10(3). Retrieved 10-01-2014 from http://www.irrodl.org/index.php/irrodl/article/view/675/127110.19173/irrodl.v10i3.675Search in Google Scholar

10. Cambria, E., & Hussain, A. (2012). Sentic Computing: Techniques, Tools, and Applications. Springer. Retrieved from http://sentic.net/sentic-computing.pdf10.1007/978-94-007-5070-8Search in Google Scholar

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.2013.30.10.1109/MIS.2013.30Search in Google Scholar

12. Carlos, A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons. ISBN 978-1-118-01094-5.Search in Google Scholar

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 5^th European Conference on Technology Enhanced Learning (pp. 432-437). Sankt Augustin: Springer.Search in Google Scholar

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, 483-511.10.1007/s11251-005-1277-4Search in Google Scholar

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.Search in Google Scholar

16. Groves, M., & O’Donoghue, J. (2009). Reflections of Students in Their Use of Asynchronous Online Seminars. Educational Technology & Society, 12(3), 143-149.Search in Google Scholar

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.Search in Google Scholar

18. Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K., (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium.Search in Google Scholar

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), 1-15.10.4018/jwltt.2008070101Search in Google Scholar

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.pdf10.3115/1220835.1220861Search in Google Scholar

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/424/104410.19173/irrodl.v9i2.424Search in Google Scholar

22. Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool publishers.10.2200/S00416ED1V01Y201204HLT016Search in Google Scholar

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, 8^th 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_2410.1007/978-3-319-07064-3_24Search in Google Scholar

24. Manning, C. D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.Search in Google Scholar

25. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: capturing favorability using natural language processing. Proceedings of the 2^nd International Conference on Knowledge Capture, 70-77.10.1145/945645.945658Search in Google Scholar

26. Ortigosa, A., Martín, J.M., & Carro, R.M. (2014). Sentiment analysis in Facebook and its application to e-learning.10.1016/j.chb.2013.05.024Search in Google Scholar

27. Ortony, A., Clore, G., & Collins, A. (1988). The Cognitive Structure of Emotions. Cambridge University Press. Retrieved from http://www.cogsci.northwestern.edu/courses/cg207/readings/Cognitive_Structure_of_Emotions_exerpt.pdf10.1017/CBO9780511571299Search in Google Scholar

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).Search in Google Scholar

29. Patriarcheas, K., & Xenos, M. (2009). Modelling of distance education forum: Formal languages as interpretation methodology of messages in asynchronous text-based discussion. Computers & Education, 52(2), 438-448. Retrieved from https://scholar.google.gr/citations?view_op=view_citation&hl=el&user=I3UJPkQAAAAJ&citation_for_view=I3UJPkQAAAAJ:u5HHmVD_uO8C10.1016/j.compedu.2008.09.013Search in Google Scholar

30. R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.orgSearch in Google Scholar

31. Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.10.1016/j.compedu.2007.05.016Search in Google Scholar

32. Sahu, C. (2008). An evaluation of selected pedagogical attributes of online discussion boards. Hello! Where are you in the landscape of educational technology? Proceedings: 25^th annual ASCILITE conference, Melbourne 2008. Retrieved 09-01-2014 from http://www.ascilite.org.au/conferences/melbourne08/procs/sahu.pdfSearch in Google Scholar

33. Siemens, G. (2010, August 25). What Are Learning Analytics? [Blog post]. Elearnspace. Retrieved from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/Search in Google Scholar

34. Siemens, G., & Baker, R.S.J. (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. LAK12, 2012.10.1145/2330601.2330661Search in Google Scholar

35. Smith, D., & Smith, K. (2014). The Case for ‘Passive’ Learning – The ‘Silent’ Community of Online Learners. European Journal of Open, Distance and E-Learning, 17(2), 85-98. Retrieved from http://www.eurodl.org/index.php?p=archives&year=2014&halfyear=2&article=64910.2478/eurodl-2014-0021Search in Google Scholar

36. Stevenson, R., Mikels, J., & James, T. (2007). Characterization of the Affective Norms for English Words by Discrete Emotional Categories. Behavior Research Methods, 39(4), 1020–1024. Retrieved from http://indiana.edu/~panlab/papers/SraMjaJtw_ANEW.pdf10.3758/BF03192999Search in Google Scholar

37. Tableau Desktop Software (2015). Retrieved from http://www.tableau.com/products/desktopSearch in Google Scholar

38. Takaffoli, M., & Zaïane, O.R. (2012). Social network analysis and mining to support the assessment of on-line student participation. ACM SIGKDD Explorations Newsletter, 13(2), 20-29.10.1145/2207243.2207247Search in Google Scholar

39. Turney, P. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceedings of the Association for Computational Linguistics, 417-424.Search in Google Scholar

40. Thomas, M.J.W. (2002). Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning, 18(3), 351-366.10.1046/j.0266-4909.2002.03800.xSearch in Google Scholar

41. Wen, M., Yang, D., & Penstein Rosé, C. (2014). Sentiment Analysis in MOOC Discussion Forums: What does it tell us? Educational Data Mining. Retrieved from http://www.cs.cmu.edu/~mwen/papers/edm2014-camera-ready.pdfSearch in Google Scholar