Understanding communication patterns in MOOCs: Combining data mining and qualitative methods

Data Mining And Learning Analytics: Applications in Educational Research

Volumn , Issue , 2016, Pages 207-221

  Eynon, Rebecca ^a   Hjorth, Isis ^a   Yasseri, Taha ^a   Gillani, Nabeel ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Communication patterns; Data mining; Massive open online courses; Mixed method approach; Pragmatic paradigm; Qualitative methods; Social science techniques

Indexed keywords

BEHAVIORAL RESEARCH; BIG DATA; E-LEARNING;

COMMUNICATION PATTERN; MASSIVE OPEN ONLINE COURSE; MIXED METHOD; PRAGMATIC PARADIGM; QUALITATIVE METHOD;

DATA MINING;

EID: 85017445630     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781118998205.ch12     Document Type: Chapter

References (65)

1. Albert, R., Jeong, H., and Barabasi, A.-L. (2000). Error and Attack Tolerance of Complex Networks. Nature, 406(6794), 378-382.
2. Arguello, J., and Shaffer, K. (2015, April). Predicting Speech Acts in MOOC Forum Posts. In Ninth International AAAI Conference on Web and Social Media. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10526. Accessed April 19, 2016.
3. Ayers, E., Nugent, R., and Dean, N. (2009). A Comparison of Student Skill Knowledge Estimates. International Working Group on Educational Data Mining. Retrieved from http://files.eric.ed.gov/fulltext/ED539062.pdf. Accessed May 11, 2016.
4. Belanger, Y., and Thornton, J. (2013). Biolectricity: A Quantitative Approach. Duke Center for Instructional Technology. Retrieved from http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/6216/Duke_Bioelectricity_MOOC_Fal12012.pdf?sequence=1. Accessed April 19, 2016.
5. Brannen, J. (1992). Combining Qualitative and Quantitative Approaches: An Overview. In J. Brannen (Ed.) Mixing Methods: Qualitative and Quantitative Research (pp. 3-37). Brookfield, VT: Ashgate.
6. Breslow, L., Pritchard, D., DeBoer, J., Stump, G., Ho, A., and Seaton, D. (2013). Studying Learning in the Worldwide Classroom: Research into edX's First MOOC. Research and Practice in Assessment, 8, 13-25.
7. Cho, H., Gay, G., Davidson, B., and Ingraffea, A. (2007). Social Networks, Communication Styles, and Learning Performance in a CSCL Community. Computers & Education, 49(2), 309-329.
8. Chudzicki, C., Pritchard, D. E., and Chen, Z. (2015). Learning Experiments Using AB Testing at Scale. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (pp. 405-408). New York: ACM. http://doi.org/10.1145/2724660.2728703. Accessed April 19, 2016.
9. Clark, D. B., Sampson, V., Weinberger, A., and Erkens, G. (2007). Analytic Frameworks for Assessing Dialogic Argumentation in Online Learning Environments. Educational Psychology Review, 19(3), 343-374.
10. Davies, J., and Graff, M. (2005). Performance in e-Learning: Online Participation and Student Grades. British Journal of Educational Technology, 36(4), 657-663.
11. DeBoer, J., Breslow, L., Stump, G., and Seaton, D. (2013). Diversity in MOOC Students' Backgrounds and Behaviours in Relationship to Performance in 6.002x. In Proceedings from the Sixth Conference of MIT's Learning International Networks Consortium (LINC) (pp. 1-10). Cambridge, MA: Harvard University Press.
12. DeBoer, J., Ho, A. D., Stump, G. S., and Breslow, L. (2014). Changing "Course" Reconceptualizing Educational Variables for Massive Open Online Courses. Educational Researcher, 43(2), 74-84.
13. De Wever, B., Schellens, T., Valcke, M., and Van Keer, H. (2006). Content Analysis Schemes to Analyze Transcripts of Online Asynchronous Discussion Groups: A Review. Computers & Education, 46(1), 6-28.
14. Easley, D., and Kleinberg, J. (2010). Networks, Crowds, Markets: Reasoning about a Highly Connected World. Cambridge, UK: Cambridge University Press.
15. Ellingson, L. L. (2009). Engaging Crystallization in Qualitative Research: An Introduction. Thousand Oaks, CA: Sage.
16. Erkens, G., and Janssen, J. (2008). Automatic Coding of Dialogue Acts in Collaboration Protocols. International Journal of Computer-Supported Collaborative Learning, 3(4), 447-470.
17. Eynon, R., Fry, J., and Schroeder, R. (2008). The Ethics of Internet Research. In N. G. Fielding, R. M. Lee, and G. Blank (Eds.) The SAGE Handbook of Online Research Methods (pp. 23-41). London: Sage.
18. Eynon, R., Fry, J., and Schroeder, R. (2016). The Ethics of Learning and Technology Research. In C. Haythornthwaite and R. Andrews (Eds.) The SAGE Handbook of e-Learning. London: Sage.
19. Eynon, R., Hjorth, I., Gillani, N., and Yasseri, T. (2014). "Vote Me Up If You Like My Ideas!" Experiences of Learning in a MOOC. In 2014 ICA Pre-Conference Working Paper: Innovation in Higher Education: Building a Better Future? Retrieved from http://papers.ssrn.com/abstract=2439571. Accessed April 19, 2016.
20. Gillani, N., and Eynon, R. (2014). Communication Patterns in Massively Open Online Courses. The Internet and Higher Education, 23, 18-26.
21. Gillani, N., Eynon, R., Osborne, M., Hjorth, I., and Roberts, S. (2014a). Communication Communities in MOOCs. ArXiv Preprint, arXiv:1403.4640v2.
22. Gillani, N., Yasseri, T., Eynon, R., and Hjorth, I. (2014b). Structural Limitations of Learning in a Crowd: Communication Vulnerability and Information Diffusion in MOOCs. Scientific Reports, 4, 6447.
23. Goodman, P. S., and Dabbish, L. A. (2011). Methodological Issues in Measuring Group Learning. Small Group Research, 42(4), 379-404.
24. Gunawardena, C. N., Lowe, C. A., and Anderson, T. (1997). Analysis of a Global Online Debate and the Development of an Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing. Journal of Educational Computing Research, 17(4), 397-431.
25. Hammersley, M. (1992). Deconstructing the Qualitative-Quantitative Divide. In J. Brannen (Ed.) Mixing Methods: Qualitative and Quantitative Research (pp. 39-52). Brookfield, VT: Ashgate.
26. Hara, N., and Kling, R. (1999). Student Frustrations with a Web-Based Distance Education Course. First Monday, 4(12). 10.5210/fm.v4i12.710.
27. Haythornthwaite, C. (1996). Social Network Analysis: An Approach and Technique for the Study of Information Exchange. Library and Information Science Research, 18(4), 323-342.
28. Haythornthwaite, C. (2002). Building Social Networks Via Computer Networks: Creating and Sustaining Distributed Learning Communities. In K. A. Renninger and W. Shumar (Eds.) Building Virtual Communities: Learning and Change in Cyberspace (pp. 159-190). Cambridge, UK: Cambridge University Press.
29. Haythornthwaite, C. (2009). Crowds and Communities: Light and Heavyweight Models of Peer Production. In 42nd Hawaii International Conference on System Sciences, 2009: HICSS'09; Waikoloa, Big Island, Hawaii (pp. 1-10). Piscataway, NJ: IEEE.
30. Henderson, M., Johnson, N. F., and Auld, G. (2013). Silences of Ethical Practice: Dilemmas for Researchers Using Social Media. Educational Research and Evaluation, 19(6), 546-560.
31. Holme, P., Kim, B. J., Yoon, C. N., and Han, S. K. (2002). Attack Vulnerability of Complex Networks. Physical Review E, 65(5), 056109.
32. Illeris, K. (2003). Towards a Contemporary and Comprehensive Theory of Learning. International Journal of Lifelong Education, 22(4), 396-406.
33. James, N., and Busher, H. (2009). Online Interviewing. London: Sage.
34. Kanuka, H., and Anderson, T. (2007). Ethical Issues in Qualitative e-Learning Research, International Journal of Qualitative Methods, 6(2), 20-39.
35. Kermack, W. O., and McKendrick, A. G. (1972). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London Series A, 115, 700-721.
36. Kizilcec, R., Piece, C., and Schneider, E. (2013). Deconstructing Disengagement: Analyzing Learner Subpopulations in Massive Open Online Courses. In The Third Proceedings of the Learning Analytics and Knowledge Conference, Leuven, Belgium.
37. Kop, R. (2011). The Challenges of Connectivist Learning on Open Online Networks: Learning Experiences During a Massive Open Online Course. International Review of Research in Open and Distance Learning, 12(3), 19-38.
38. Kossinets, G., and Watts, D. (2006). Empirical Analysis of an Evolving Social Network. Science, 311, 88-90.
39. Krings, G., Karsai, M., Bernhardsson, S., Blondel, V. D., and Saramäki, J. (2012). Effects of Time Window Size and Placement on the Structure of an Aggregated Communication Network. EPJ Data Science, 1(4), 1-16.
40. Lamb, A., Smilack, J., Ho, A., and Reich, J. (2015). Addressing Common Analytic Challenges to Randomized Experiments in MOOCs: Attrition and Zero-Inflation. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 21-30). New York: ACM.
41. Lave, J., and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.
42. Markham, A., and Buchanan, E. (2012). Ethical Decision-Making and Internet Research 2.0: Recommendations from the AoIR Ethics Working Committee. Association of the Internet Research Ethics Working Committee.
43. Marshall, S. (2014). Exploring the Ethical Implications of MOOCs. Distance Education, 35(2), 250-262.
44. O'Connor, H., Madge, C., Shaw, R., and Wellens, J. (2008). Internet-Based Interviewing. In N. G. Fielding, R. M. Lee, and G. Blank (Eds.) The SAGE Handbook of Online Research Methods (pp. 271-289). London: Sage.
45. Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., ... Barabási, A.-L. (2007). Structure and Tie Strengths in Mobile Communication Networks. Proceedings of the National Academy of Sciences, 104(18), 7332-7336.
46. Palonen, T., and Hakkarainen, K. (2000). Patterns of Interaction in Computer-Supported Learning: A Social Network Analysis. In B. Fishman and S. O'Conner-Divelbiss (Eds.) Fourth International Conference of the Learning Sciences (pp. 334-339). Mahwah, NJ: Erlbaum.
47. Pardo, A., and Siemens, G. (2014). Ethical and Privacy Principles for Learning Analytics. British Journal of Educational Technology, 45(3), 438-450.
48. Preece, J., Nonnecke, B., and Andrews, D. (2004). The Top Five Reasons for Lurking: Improving Community Experiences for Everyone. Computers in Human Behavior, 20(2), 201-223.
49. Pring, R. (2001). The Virtues and Vices of an Educational Researcher. Journal of Philosophy of Education, 35(3), 407-421.
50. Psorakis, I., Roberts, S., Rezek, I., and Sheldon, B. (2011). Inferring Social Network Structure in Ecological Systems from Spatio-Temporal Data Streams. Journal of the Royal Society Interface, 9(76), 3055-3066.
51. Rabbany, R., Elatia, S., Takaffoli, M., and Zaïane, O. R. (2014). Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective. In Educational Data Mining (pp. 441-466). Switzerland: Springer International Publishing.
52. Raffaghelli, J. E., Cucchiara, S., and Persico, D. (2015). Methodological Approaches in MOOC Research: Retracing the Myth of Proteus. British Journal of Educational Technology, 46(3), 488-509.
53. Ravenscroft, A. (2003). From Conditioning to Highly Communicative Learning Communities: Implications of 50 Years of Research and Development in eLearning Interaction Design. Association for Learning Technology Journal, 11(3), 4-18.
54. Reich, J. (2014). Rebooting MOOC Research. Science, 347(6217), 30-31.
55. Seaton, D., Bergner, Y., Chuang, I., Mitros, P., and Pritchard, D. (2014). Who Does What in a Massive Open Online Course? Communications of the ACM, 57(4), 58-65.
56. Sfard, A. (1998). On Two Metaphors for Learning and the Dangers of Choosing Just One. Educational Researcher, 27(2), 4-13.
57. Siemens, G. (2005). Connectivism: A Learning for a Digital Age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10.
58. Slade, S., and Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1509-1528.
59. Stahl, G., Koschmann, T., and Suthers, D. (2006). Computer-Supported Collaborative Learning: An Historical Perspective. In R. K. Sawyer (Ed.) Cambridge Handbook of the Learning Sciences (pp. 409-426). Cambridge, UK: Cambridge University Press.
60. Tashakkori, A., and Teddlie, C. (1998). Mixed Methodology: Combining Qualitative and Quantitative Approaches (Applied Social Research Methods Series, Volume 46). London: Sage.
61. Vaquero, L. M., and Cebrian, M. (2013). The Rich Club Phenomenon in the Classroom. Scientific Reports, 3, 1174.
62. Veletsianos, G., Collier, A., and Schneider, E. (2015). Digging Deeper into Learners' Experiences in MOOCs: Participation in Social Networks Outside of MOOCs, Notetaking, and Contexts Surrounding Content Consumption. British Journal of Educational Technology, 46(3), 570-587.
63. Wen, M., Yang, D., and Rose, C. (2014, July). Sentiment Analysis in MOOC Discussion Forums: What does it tell us? In Proceedings of the Seventh International Conference on Educational Data Mining (EDM 2014) (pp. 130-137). Massachusetts: International Educational Data Mining Society.
64. Wesler, H. T., Smith, M., Fisher, D., and Gleave, E. (2008). Distilling Digital Traces: Computational Social Science Approaches to Studying the Internet. In N. G. Fielding, R. M. Lee, and G. Blank (Eds.) The SAGE Handbook of Online Research Methods (pp. 116-140). London: Sage.
65. Yang, D., Sinha, T., Adamson, D., and Rose, C. P. (2013, December). "Turn on, Tune in, Drop out": Anticipating Student Dropouts in Massive Open Online Courses. Proceedings of the 2013 NIPS Data-Driven Education Workshop, 11, 14.