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Dropout prediction in a massive open online course using learning analytics
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, Tel Aviv University, Israel ; , Tel Aviv University, Israel

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Washington, DC, United States Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

Abstract

Analysis of the data collected in Massive Open Online Courses (MOOCs) allows for the assessment of massive learning processes and behavior. Many criticize MOOCs for their high rate of dropout. In this study, a model was developed for early identification of learners at risk of dropping out. Due to various motivations for MOOC registration, dropout is defined as termination of participation before achieving the learner aims and purposes. This model is based on learning behavior variables and monthly alerts, which indicate patterns of activity and behavior that may lead to dropout. Five types of learners with similar behavior were identified; non-active learners, video-based learners, video and assignment-based learners, assignment-oriented learners, and active learners. A statistically significant model resulting from a linear regression analysis, explains 45% of the learner achievement variance. Early recognition of dropouts may assist in identifying those who require support.

Citation

Cohen, A. & Shimony, U. (2016). Dropout prediction in a massive open online course using learning analytics. In Proceedings of E-Learn: World Conference on E-Learning (pp. 616-625). Washington, DC, United States: Association for the Advancement of Computing in Education (AACE). Retrieved December 16, 2018 from .

Keywords

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References

  1. Belanger, Y., & Thornton J. (2013). Bioelectricity: A quantitative approach. Duke University’s first MOOC. Duke University Libraries. Retrieved from http://dukespace.lib.duke.edu/dspace/handle/10161/6216.
  2. Ben‐Tsur D. (2007). Affairs of state and student retention: an exploratory study of the factors that impact student retention in a politically turbulent region. British Journal of Sociology of Education, 28(3), 317-332, .
  3. Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D., & Seaton, D.T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research& Practice in Assessment, 8(1), 13-25.‏
  4. Campbell, J., DeBlois, P., & Oblinger, D. (2007). Academic analytics. A New Tool for a New Era. EDUCAUSE Review, 42(4), 42–57.
  5. Clow, D. (2013). MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 185-189). ACM.‏
  6. Cohen, A. (2015). Web-Based Analysis of Student Activity for Predicting Dropout. Paper for the EDEN 2015 Annual Conference-Expanding Learning Scenarios, Barcelona, Spain.
  7. Cohen, A., & Nachmias, R. (2011).What can instructors and policymakers learn about web-supported learning through web-mining. Internet and Higher Education, 14(2), 67-76, DOI:10.1016/J.iheduc.2010.07.008.
  8. Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Seoul: Korean National Open University. Retrieved from http://sirjohn.ca/wordpress/wp DASHDASH
  9. Horstschräer, J., & Sprietsma, M. (2015). The effects of the introduction of Bachelor degrees on college enrollment and dropout rates. Education Economics, 23(3), 296-317,
  10. Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium. Lohr, S.
  11. Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop "early warning system" for educators: A proof of concept. Computers& Education, 54(2), 588-599, DOI:10.1016/J.compedu.2009.09.008.
  12. Nistor, N., & Neubauer, K. (2010). From participation to dropout: Quantitative participation patterns in online university courses. Computers& Education, 55(2), 663-672, doi:10.1016/J.compedu.2010.02.026.
  13. Onah, D.F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. EDULEARN14 Proceedings, 5825-5834.‏
  14. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146, .
  15. Soffer, T., & Cohen, A. (2015). Implementation of Tel Aviv University MOOCs in Academic Curriculum: A pilot study. International Review of Research in Open and Distributed Learning, 16 (1), 80-97.
  16. Zutshi, S., O'Hare, S., & Rodafinos, A. (2013). Experiences in MOOCs: The perspective of students. American Journal of Distance Education, 27(4), 218-227.

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