Predicting student retention from behavior in an online orientation course

Shimin Kai, Juan Miguel L. Andres, Luc Paquette, Ryan S. Baker, Kati Molnar, Harriet Watkins, Michael Moore

Research output: Contribution to conferencePaper

Abstract

As higher education institutions develop fully online course programs to provide better access for the non-traditional learner, there is increasing interest in identifying students who may be at risk of attrition and poor performance in these online course programs. In our study, we investigate the effectiveness of an online orientation course in improving student retention in an online college program. Using student activity data from the orientation course, Engage, we make use of machine learning methods to develop prediction models of whether students will be retained and continue to register for program-specific courses in the eVersity program. We then discuss the implications of our findings on improvements that may be made to the existing orientation course to improve student retention in the program.

Original languageEnglish (US)
Pages250-255
Number of pages6
StatePublished - Jan 1 2017
Event10th International Conference on Educational Data Mining, EDM 2017 - Wuhan, China
Duration: Jun 25 2017Jun 28 2017

Conference

Conference10th International Conference on Educational Data Mining, EDM 2017
CountryChina
CityWuhan
Period6/25/176/28/17

Keywords

  • Online orientation course
  • Prediction modeling
  • Student retention

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Kai, S., Andres, J. M. L., Paquette, L., Baker, R. S., Molnar, K., Watkins, H., & Moore, M. (2017). Predicting student retention from behavior in an online orientation course. 250-255. Paper presented at 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, China.