654
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Learner Groups in Massive Open Online Courses

References

  • Adamopoulos, P. 2013. What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. Milan: Thirty Fourth International Conference on Information Systems.
  • Aher, S. B. 2014. EM&AA: An algorithm for predicting the course selection by student in e-learning using data mining techniques. Journal of the Institution of Engineers (India): Series B 95 (1):43–54. doi:10.1007/s40031-014-0074-3
  • Arthur, D., & S. Vassilvitskii. (2007, January). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027–1035. Philadelphia, PA: Society for Industrial and Applied Mathematics.
  • Baker, R. S., A. T. Corbett, and K. R. Koedinger. 2004. Detecting student misuse of intelligent tutoring systems. In Intelligent tutoring systems, 531–40. Berlin, Germany: Springer.
  • Chrysostomou, K., S. Y. Chen, and X. Liu. 2009. Investigation of users’ preferences in interactive multimedia learning systems: A data mining approach. Interactive Learning Environments 17 (2):151–63. doi:10.1080/10494820801988315,
  • ClowD. 2013. MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge, 185–89. Leuven, Belgium: ACM.
  • Coleman, C. A., D. T. Seaton, and I. Chuang. 2015. Probabilistic use cases: Discovering behavioral patterns for predicting certification. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale, 141–48. New York, NY: ACM.
  • Dekker, G. W., M. Pechenizkiy, and J. M. Vleeshouwers. 2009. Predicting students drop out: A case study. inte rnational working group on educational data mining.
  • Ezen-Can, A., K. E. Boyer, S. Kellogg, and S. Booth. 2015. Unsupervised modeling for understanding MOOC discussion forums: A learning analytics approach. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 146–50. Poughkeepsie, NY: ACM.
  • Fasihuddin, H. A., G. D. Skinner, and R. I. Athauda. 2013. Boosting the opportunities of open learning (MOOCs) through learning theories. GSTF Journal on Computing (Joc) 3 (3):112. doi:10.7603/s40601-013-0031-z
  • Felder, R. M., and R. Brent. 2005. Understanding student differences. Journal of Engineering Education 94 (1):57–72. doi:10.1002/jee.2005.94.issue-1
  • Felder, R. M., and L. K. Silverman. 1988. Learning and teaching styles in engineering education. Engineering Education 78 (7):674–81.
  • Ferguson, R., and D. Clow. 2016. Consistent commitment: Patterns of engagement across time in Massive Open Online Courses (MOOCs). Journal of Learning Analytics 2 (3):55–80. doi:10.18608/jla.2015.23.5
  • Grünewald, F., C. Meinel, M. Totschnig, and C. Willems. 2013. Designing MOOCs for the support of multiple learning styles. In Scaling up learning for sustained impact, 371–82. Berlin, Germany: Springer.
  • Han, J., and M. Kamber. 2000. Data mining: Concepts and techniques (the Morgan Kaufmann Series in data management systems).
  • Hastings, C.Jr., F. Mosteller, J. W. Tukey, and C. P. Winsor. 1947. Low moments for small samples: A comparative study of order statistics. In The annals of mathematical statistics, 413–26. Institute of Mathematical Statistics.
  • Ho, A. D., J. Reich, S. Nesterko, D. T. Seaton, T. Mullaney, J. Waldo, and I. Chuang. 2014a. HarvardX and MITx: The first year of open online courses. HarvardX and MITx Working Paper No. 1.
  • Ho, A. D., J. Reich, S. O. Nesterko, D. T. Seaton, T. Mullaney, J. Waldo, and I. Chuang. 2014b. HarvardX and MITx: The first year of open online courses, fall 2012-summer 2013.
  • Jiang, S., M. Warschauer, A. E. Williams, D. O’Dowd, and K. Schenke. 2014. Predicting MOOC performance with week 1 behavior. In Proceedings of the 7th International Conference on Educational Data Mining, 273–75. July. London, UK.
  • Kizilcec, R. F., C. Piech, and E. Schneider. 2013. Deconstructing disengagement: Analyzing learner subpopulations in Massive Open Online Courses. In Proceedings of the third international conference on learning analytics and knowledge 170–79. Leuven, Belgium: ACM.
  • Li, N., W. W. Cohen, and K. R. Koedinger. 2013. Discovering student models with a clustering algorithm using problem content. Proceedings of the 6th International Conference on Educational Data Mining, Memphis, TN.
  • Li, N., Ł. Kidziński, P. Jermann, and P. Dillenbourg. 2015. MOOC video interaction patterns: What do they tell us? In Design for teaching and learning in a networked world, 197–210. Springer International Publishing.
  • Li, Y., Y. Zheng, J. Kang, and H. Bao. 2016. Designing a learning recommender system by incorporating resource association analysis and social interaction computing. In State-of-the-art and future directions of smart learning, 137–43. Springer Singapore.
  • Lin, S. H. 2012. Data mining for student retention management. Journal of Computing Sciences in Colleges 27 (4):92–99.
  • Lublin, J. 2003. Deep, surface and strategic approaches to learning, 806–25. Centre for Teaching and Learning. Dublin: UCD Dublin, nd.
  • Maimon, O., and L. Rokach. 2005. Decomposition methodology for knowledge discovery and data mining, 981–1003. Springer US.
  • Marton, F.D. Hounsell,, and N. Entwistle, eds. 1997. The experience of learning, 2nd ed. Edinburgh, UK: Scottish Academic Press.
  • Milligan, C., A. Littlejohn, and A. Margaryan. 2013. Patterns of engagement in connectivist MOOCs. Journal of Online Learning and Teaching 9 (2):149.
  • Mohamad, I. B., and D. Usman. 2013. Standardization and its effects on K-means clustering algorithm. Researcher Journal Applications Sciences Engineering Technological 6 (17):3299–303.
  • Montazer, G. A. 2011. Learners grouping in e-leaming environment using evolutionary fuzzy clustering approach. International Journal of Information and Communication Technology 3 (1):9–19.
  • Newble, D. I., and N. J. Entwistle. 1986. Learning styles and approaches: Implications for medical education. Medical Education 20 (3):162–75. doi:10.1111/medu.1986.20.issue-3
  • O’Reilly, U. M., and K. Veeramachaneni. 2014. Technology for mining the big data of MOOCs. Research & Practice in Assessment 9:29–37.
  • Oladipupo, O. O., and O. J. Oyelade. 2010. Knowledge discovery from students’ result repository: Association rule mining approach. International Journal of Computer Science and Security 4 (2):199–207.
  • Onah, D. F. O., and J. Sinclair. 2015. Massive Open Online Courses: An adaptive learning framework. 9th International Technology, Education and Development Conference, Madrid, Spain, 1258–66.
  • Özpolat, E., and G. B. Akar. 2009. Automatic detection of learning styles for an e-learning system. Computers & Education 53 (2):355–67. doi:10.1016/j.compedu.2009.02.018
  • Perera, D., J. Kay, I. Koprinska, K. Yacef, and O. R. Zaiane. 2009. Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering 21 (6):759–72. doi:10.1109/TKDE.2008.138
  • Rodrigo, M. M. T., E. A. Anglo, J. O. Sugay, and R. Baker. 2008. Use of unsupervised clustering to characterize learner behaviors and affective states while using an intelligent tutoring system. In Proceedings of International Conference on Computers in Education, 49–56.
  • Romero, C., and S. Ventura. 2007. Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33 (1):135–46. doi:10.1016/j.eswa.2006.04.005
  • Sabitha, A. S., D. Mehrotra, and A. Bansal. 2015. Delivery of learning knowledge objects using fuzzy clustering. In Education and information technologies, 1–21. New York, NY: Springer.
  • Sabitha, A. S., D. Mehrotra, A. Bansal, and B. K. Sharma. 2015. A naive bayes approach for converging learning objects with open educational resources. Education and Information Technologies 21 (6):1753–1767.
  • Sabitha, A. S., D. Mehrotra, and A. Bansal. 2014. A data mining approach to improve re-accessibility and delivery of learning knowledge objects: Interdisciplinary. Journal of E-Learning and Learning Objects 10:247–68.
  • Sinha, T. 2014a. Together we stand, together we fall, together we win: Dynamic team formation in Massive Open Online Courses. In Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the. 107–12. IEEE. February. Amrita University, Carmelaram, India.
  • Sinha, T. 2014b. Supporting MOOC instruction with social network analysis.arXiv. preprint arXiv:1401.5175.
  • Tang, J. K., H. Xie, and T. L. Wong. 2015. A big data framework for early identification of dropout students in MOOC. In Technology in education: Technology-mediated proactive learning, 127–32. Berlin, Germany: Springer.
  • Tardío, R., and J. Peral. 2015. Obtaining key performance indicators by using data mining techniques. In Advances in conceptual modeling, 144–53. : Cham, Switzerland. Springer International Publishing.
  • Tucker, C., Pursel, B., & Divinsky, A. (2014). Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes. In 2014 ASEE Annual Conference, Indianapolis, Indiana, Indianapolis, Indiana.
  • Wei, Z., and W. Wu. 2015. A peer grading tool for MOOCs on programming. In Intelligent computation in big data era, 378–85. Berlin, Germany: Springer-Verlag.
  • Zhang, Y., S. Oussena, T. Clark, and K. Hyensook. 2010. Using data mining to improve student retention in HE: A case study. ICEIS - 12th International Conerence on Enterprise Information Systems, 2010., 8–12 June, Portugal.
  • Zheng, Z., T. Vogelsang, and N. Pinkwart. 2014. The impact of small learning group composition on student engagement and success in a MOOC. Proceedings of Educational Data Mining, 7. Proceedings of the 8th International Conference on Educational Data Mining.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.