The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs)

Author:

Hughes Glyn,Dobbins Chelsea

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Media Technology,Education,Social Psychology

Reference51 articles.

1. Alabi, H, Code, J, & Irvine, V. (2013). Visualizing learning analytics: designing a roadmap for success. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (Vol. 2013, pp. 951–959). Victoria: Association for the Advancement of Computing in Education (AACE). Retrieved from http://www.editlib.org/p/112075/ .

2. Anderson, A, Huttenlocher, D, Kleinberg, J, & Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the 23rd International Conference on World Wide Web (WWW ’14) (pp. 687–698). New York: ACM Press. doi: 10.1145/2566486.2568042 .

3. Ashman, H, Brailsford, T, Cristea, AI, Sheng, QZ, Stewart, C, Toms, EG, & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51(6), 819–832. doi: 10.1016/j.im.2014.04.003 .

4. Balakrishnan, G, & Coetzee, D. (2013). Predicting student retention in massive open online courses using Hidden Markov Models. Retrieved from http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-109.pdf .

5. Bayne, S, & Ross, J. (2014). The pedagogy of the massive open online course (MOOC): the UK view. Retrieved from http://www.heacademy.ac.uk/resources/detail/elt/the_pedagogy_of_the_MOOC_UK_view .

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