Educational Data and Learning Analytics in KazNU MOOCs Platform
DOI:
https://doi.org/10.26577/JMMCS-2018-3-520Keywords:
MOOCs, learning analytics, educational data, online learning, blended learningAbstract
The initial hype around massive open online courses (MOOCs) already subsided, but the number of new learners in MOOCs platforms is still growing. Due to low completion rates in the MOOCs compared to enrolled students it is important to establish and validate quality standards for these courses. Employing of educational data and learning analytics to improve lesson plans and course delivery become an innovative approach for teachers, curriculum developers and policy makers in education. Learning analytics of online courses can be also used for enhancement of classroom teaching by blending online and face-to-face learning models.
This work presents some observations about the behavior of students, obtained by analyzing the data generated during delivery of 13 MOOCs. Besides classification of learners by analysis their activity data, other interesting characteristics about platform learners like demographic, gender and level of education are described. The results indicate that the quality of interpersonal interaction within a course relates positively and significantly to student scores.
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[3] Shah D. "EdSurge, Monetization Over Massiveness: Breaking Down MOOCs by the Numbers in 2016", (2016).
[4] Jordan K. Initial Trends in Enrolment and Completion of Massive Open Online Courses. The International Review of Research in Open and Distributed Learning, 15(1): 133–160, (2014).
[5] O’Reilly U.-M., Veeramachaneni K. Technology for mining the big data of MOOCs. Research & Practice in Assessment, 9(2): 29—37, (2014)
[6] Jordan K. "MOOC completion rates: The Data, accessed June 15, 2018, http://www.katyjordan.com/MOOCproject.html
[7] Reich J. "MOOC Completion and Retention in the Context of Student Intent", accessed July 15, 2018, https://er.educause.edu/articles/2014/12/mooc-completion-and-retention-in-the-context-of-student-intent
[8] ChristensenG.,SteinmetzA.,etal."TheMOOCPhenomenon:WhoTakesMassiveOpenOnlineCoursesandWhy?"(2013). accessed July 15, 2018, http://dx.doi.org/10.2139/ssrn.2350964
[9] Newman J., Oh S. "8 Things You Should Know About MOOCs(2014), accessed July 15, 2018, http://www.chronicle.com/interactives/moocs_stats
[10] Onah D.F.O., Sinclair J., Boyatt R. Dropout Rates of Massive Open Online Courses: Behavioural Patterns. In: EDULEARN14 Proceedings, (2014):5825–5834, accessed July 15, 2018, https://warwick.ac.uk/fac/sci/dcs/people/research/csrmaj/daniel_onah_edulearn14.pdf
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[12] Patru M., Balaji V. Making Sense of MOOCs: A Guide for Policy-Makers in Developing Countries. Paris, UNESCO (2016), accessed July 15, 2018, http://unesdoc.unesco.org/images/0024/002451/245122E.pdf
[13] Williams J.J., Williams A. Using interventions to improve online learning. In: Proc. of the Neural Information Processing Systems, Workshop on Data Driven Education, (2013)
[14] Shi C., Fu S., et al. VisMOOC: Visualizing Video Clickstream Data from Massive Open Online Courses. IEEE Pacific Visualization Symposium, Hangzhou, China, (2015)
[15] Kennedy G., Coffrin C., et al. Predicting success: how learners’ prior knowledge, skills and activities predict MOOC performance. In: Proc. of the Fifth International Conference on Learning Analytics And Knowledge, ACM, (2015):136- 140.
[16] Kizilcec R., Piech C., Schneider E. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proc. of the Int. Conf. LAK ’13, (2013):170—179.
[17] AndersonA.,HuttenlocherD.,etal.Engagingwithmassiveonlinecourses.In: Proc. of the Int. Conf. WWW’14,(2014):687- –697.
[18] Taylor C., Veeramachaneni K., O’Reilly U.-M. "Likely to stop? Predicting stopout in massive open online coursesaccessed July 15, 2018, http://arxiv.org/abs/1408.3382
[19] "Harvard Dataverse: HarvardX-MITx Person-Course Academic Year 2013 De-Identified dataset, version 2.0(2014), accessed July 15, 2018, http://dx.doi.org/10.7910/DVN/26147
[20] Northcutt C.G., Ho A.D., Chuang I.L. "Detecting and Preventing “Multiple-Account” Cheating in Massive Open Online Courses(2015), accessed July 15, 2018, https://arxiv.org/abs/1508.05699v3
[21] "CAROL Learner Dataaccessed July 15, 2018, http://datastage.stanford.edu/
[22] Ren Zh., Rangwala H., Johri A. "Predicting Performance on MOOC Assessments using Multi-Regression Models(2016), accessed July 15, 2018, https://arxiv.org/abs/1605.02269v1
[23] "Al-Farabi KazNU’s MOOCs platformaccessed July 15, 2018, http://open.kaznu.kz
[24] Hill P. "Emerging student patterns in MOOCs: A (revised) graphical view [Blog post](2013), accessed July 15, 2018, http://mfeldstein.com/emerging-student-patterns-in-moocs-a-revised-graphical-view/
[25] Wang Y., Baker R. Content or platform: Why do students complete MOOCs? MERLOT Journal of Online Learning and Teaching, 11(1): 17–30, (2015), accessed July 15, 2018, http://jolt.merlot.org/vol11no1/Wang_0315.pdf
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How to Cite
Alimzhanov, Y. S., & Mansurova, M. Y. (2018). Educational Data and Learning Analytics in KazNU MOOCs Platform. Journal of Mathematics, Mechanics and Computer Science, 99(3), 106–115. https://doi.org/10.26577/JMMCS-2018-3-520
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Computer Science
