Crowd-sourced learning in MOOCs: Learning analytics meets measurement theory

ACM International Conference Proceeding Series

Volumn 16-20-March-2015, Issue , 2015, Pages 151-155

  Milligan, Sandra ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

21st Century skills; Analytics Tools; Collaborative Learning; Crowd sourced learning; Learner Performance; Learning Analytics; Learning Progression; Measurement Theory; MOOC; On line Forums; Rasch Analysis

Indexed keywords

COMPUTER PROGRAMMING;

21ST CENTURY SKILLS; ANALYTICS TOOLS; COLLABORATIVE LEARNING; CROWD-SOURCED LEARNING; LEARNER PERFORMANCE; LEARNING ANALYTICS; LEARNING PROGRESSIONS; MOOC; ON-LINE FORUMS; RASCH ANALYSIS;

MEASUREMENT THEORY;

EID: 84955578898     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2723576.2723596     Document Type: Conference Paper

References (27)

1. Gillani, N. Learner communications in massively open online courses. University of Oxford, Oxford, 2013.
2. Gillani, N., Yasseri, T., Eynon, R. and Hjorth, I. Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs. Scientifc reports, nature.com, 4, 6447, (2014).
3. Creelman, A. The silent majority-why are MOOC forums so counterproductive. Retrieved from http://acreelman.blogspot.co.au/2013/09/the-silent-majority-why-are-mooc-forums.html, 2013.
4. Milligan, C., Littlejohn, A. and Margaryan, A. Patterns of Engagement in Connectivist MOOCs. Journal of Online Learning & Teaching, 9, 2 2013, 149-159.
5. Fournier, H., Kop, R. and Sitlia, H. The Value of Learning Analytics to Networked Learning on a Personal Learning Environment. ACM, in Proceedings of the 1st International Conference on Learning Analytics and Knowledge, Banff, Canada, 2011.
6. Waite, M., Mackness, J., Roberts, G. and Lovegrove, E. Liminal Participants & Skilled Orienteers: Learner Participation in a MOOC for New Lecturers. MERLOT Journal of Online Learning and Teaching, 9, 2 2013.
7. Ahn, J., Butler, B. S., Alam, A. and Webster, S. A. Learner Participation and Engagement in Open Online Courses: Insights from the Peer 2 Peer University. Journal of Online Learning & Teaching, 9, 2 2013), 160-171.
8. Chiu, M. M. and Fujita, N. Statistical Discourse Analysis of Online Discussions: Informal Cognition, Social Metacognition and Knowledge Creation. In Proceedings of the Learning Analytics and Knowledge Conference, ACM, Indianapolis, USA, 2014.
9. Brinton, C., Chiang, M., Jain, S., Lam, H., Liu Z and F, W. Learning about Social learning in MOOCs: from statistical analysis to generative model. Learning Technologies, IEEE Transactions on Learning Technologies, 99 2014.
10. Wen, M., Yang, D. and Rose, C. Sentiment Analysis in a MOOC Discussion Forums: What Does it tell us? Carnegie Mellon University, 2014.
11. Kop, R., Fournier, H. and Mak, J. S. F. A Pedagogy of Abundance or a Pedagogy to Support Human Beings? Participant Support on Massive Open Online Courses. International Review of Research in Open and Distance Learning, 12, 7 2011, 74-93.
12. Shea, P., Hayes, S., Smith, S., Vickers, J., Bidjerano, T., GozzaCohen, M., Jian, S. B., Pickett, A., Wilde, J., Tseng and Chi-Hua Online Learner Self-Regulation: Learning Presence Viewed through Quantitative Content-and Social Network Analysis. Athabasca University, 2013.
13. Siemens, G. Connectivism: A Learning Theory for the Digital Age. e-learn space, 2004.
14. Stewart, B. Massiveness + Openness = new literacies of Participation? MERLOT Journal of Online Learning and Teaching, 9, 2 2013, 10.
15. Stewart, B. Social Media Literacies and Perceptions of Value in Open Online Courses. Retrieved from http://portfolio.cribchronicles.com/social-media-literacies-and-perceptions-of-value-in-open-online-courses/, 2010.
16. Kop, R. The Challenges to 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 (03/01/ 2011), 19-38.
17. Griffin, P. and Care, E. Assessment and Teaching of 21st Century Skills: Methods and Approaches. Springer, 2015.
18. Wilson, M. On Choosing a Model for Assessemnt. Methods of Psychological Research Online, 8, 3 2003, 1-22.
19. Hambleton, R. K. Item Response Theory: a Broad Psychometric Framework for Measurement Advances. Psicothema, 6, 3 1994, 535-556.
20. Milligan, S. Learning Skills for the Digital Era. In Proceedings of the Science ofLearning Centre Big Day Out (Adelaide, Australia, 2014). Assessment Research Centre, University of Melbourne, Adelaide, 2014. .
21. Dreyfus, S. E. & Dreyfus, H. L. A Five stage Model of the Mental Activites Involved in Directed Skill Acquisition. Operations Research Centre, University of California, 1980.
22. Anderson, E. B. Sufficient statistics and latent trait models. Psychometrika, 42 1977, 69-81.
23. Andrich, D. A Rating Formulation for Ordered Response Categories. Psychometrica, 431978), 561-573.
24. Masters, G. N. A Rasch Model for Partial Credit Scoring. Psychometrica, 47 1982, 149-174.
25. Andrich, D. Rasch Model for Measurement. Sage Publicaions, Beverly Hills and London, 1988.
26. Wu, M., Adams, R. and Wilson, M. ACER conquest: generalised item response modelling software. ACER Press, Melbourne, 1998.
27. Wright, B. D. and Linacre, J. M. Reasonable mean-square fit values. Retrieved December 2013, from http://www.rasch.org/rmt/rmt83b.htm, 1994