Technology, Knowledge and Learning

, Volume 21, Issue 1, pp 21–32 | Cite as

Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention

Digital Learning

Abstract

Predicting which students enrolled in graduate online education are at-risk for failure is an arduous yet important task for teachers and administrators alike. This research reports on a statistical analysis technique using both static and dynamic variables to determine which students are at-risk and when an intervention could be most helpful during a semester. Time-series clustering analysis of online teacher education classes revealed that prediction is possible after the 10th week capturing over 78 % of at-risk students. Visual analysis of dynamic student activities shares a number of striking commonalities consistent with EKG charting. The potential exists for instructors to recognize simple graphic patterns that identify and formatively address these issues with their students. Next phases of research will apply further validation of both the models attempted and additional predictor variables.

Keywords

Online education Student retention Formative assessment 

References

  1. Aman, R. R. (2009). Improving student satisfaction and retention with online instruction through systematic faculty peer review of courses, Unpublished doctoral dissertation, Oregon State University.Google Scholar
  2. Balfanz, R., Herzog, L., & Mac Iver, D. J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223–235.CrossRefGoogle Scholar
  3. Baran, E., & Correia, A. (2014). A professional development framework for online teaching. TechTrends, 58(5), 95–101.CrossRefGoogle Scholar
  4. Baran, E., Correia, A., & Thompson, A. (2011). Transforming online teaching practice: Critical analysis of the literature on the roles and competencies of online teachers. Distance Education, 32(3), 421–439.CrossRefGoogle Scholar
  5. Cain, M., Phillip, S., Ting, S. R., Gonzalez, L. M., Johnson, J., & Galy, E., et al. (2013). An exploration of students’ experiences of learning in an online primary teacher education program. Journal of Online Learning & Teaching, 9(3).Google Scholar
  6. Chiocchio, F., & Lafrenière, A. (2009). A project management perspective on student’ s declarative commitments to goals established within asynchronous communication. Journal of Computer Assisted learning, 25(3), 294–305.CrossRefGoogle Scholar
  7. Cohen, G. L., Garcia, J., Apfel, N., & Master, A. (2006). Reducing the racial achievement gap: A social-psychological intervention. Science, 313(5791), 1307–1310.CrossRefGoogle Scholar
  8. Cohen, G. L., Garcia, J., Purdie-Vaughns, V., Apfel, N., & Brzustoski, P. (2009). Recursive processes in self-affirmation: Intervening to close the minority achievement gap. Science, 324(5925), 400–403.CrossRefGoogle Scholar
  9. Cole, B., Matheson, K., & Anisman, H. (2007). The moderating role of ethnic identity and social support on relations between well-being and academic performance1. Journal of Applied Social Psychology, 37(3), 592–615.CrossRefGoogle Scholar
  10. Crawford-Ferre, H. G., & Wiest, L. R. (2012). Effective online instruction in higher education. The Quarterly Review of Distance Education, 13(1), 11–14.Google Scholar
  11. Dray, B. J., Lowenthal, P. R., Miszkiewicz, M. J., Ruiz-Primo, M. A., & Marczynski, K. (2011). Developing an instrument to assess student readiness for online learning: A validation study. Distance Education, 32(1), 29–47.CrossRefGoogle Scholar
  12. Falloon, G. (2011). Making the connection: Moore’s theory of transactional distance and its relevance to the use of a virtual classroom in postgraduate online teacher education. Journal of Research on Technology in Education, 43(3), 187–209.CrossRefGoogle Scholar
  13. Frankola, K. (2001). Why Online Learners Drop Out. Workforce, 80(10), 53.Google Scholar
  14. Glezakos, T. J., Tsiligiridis, T. A., & Yialouris, C. P. (2014). Piecewise evolutionary segmentation for feature extraction in time series models. Neural Computing and Applications, 24(2), 243–257.CrossRefGoogle Scholar
  15. Harrell, I. L. (2008). Increasing the success of online students. Inquiry, 13(1), 36–44.Google Scholar
  16. Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), 19–42.Google Scholar
  17. Herbert, M. (2007). Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration, 10, 1.Google Scholar
  18. Heyman, E. (2010). Overcoming student retention issues in higher education onlineprograms. Online Journal of Distance Learning Administration, 8, 4.Google Scholar
  19. Ishitani, T. T. (2008). How to explore timing of intervention for students at risk of departure. New Directions for Institutional Research, 2008(137), 105–122.CrossRefGoogle Scholar
  20. Kayongo-Male, D., & Lee, M. B. (2004). Macro and micro factors in ethnic identity construction and educational outcomes: Minority university students in the People’s Republic of China. Race Ethnicity and Education, 7(3), 277–305.CrossRefGoogle Scholar
  21. Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593–618.CrossRefGoogle Scholar
  22. Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43, 72–83.CrossRefGoogle Scholar
  23. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599.CrossRefGoogle Scholar
  24. Mazzolini, M., & Maddison, S. (2003). Sage, guide or ghost? The effect of instructor intervention on student participation in online discussion forums. Computers & Education, 40(3), 237–253.CrossRefGoogle Scholar
  25. Pan, W., Guo, S., Alikonis, C., & Bai, H. (2008). Do intervention programs assist students to succeed in college? A multilevel longitudinal study. College Student Journal, 42(1), 90–98.Google Scholar
  26. Patterson, B., & McFadden, C. (2009). Attrition in online and campus degree programs. Online Journal of Distance Learning Administration, 12, 2.Google Scholar
  27. Perry, B., Boman, J., Care, W. D., Edwards, M., & Park, C. (2008). Why do students withdraw from online graduate nursing and health studies education? Journal of Educators Online, 5(1), n1.Google Scholar
  28. Pullan, M. (2013). Using robotics to improve retention and increase comprehension in introductory programming courses. Journal of Educational Technology Systems, 42(2), 141–150.CrossRefGoogle Scholar
  29. Terrell, S. R., Snyder, M. M., & Dringus, L. P. (2009). The development, validation, and application of the Doctoral Student Connectedness Scale. The Internet and Higher Education, 12(2), 112–116.CrossRefGoogle Scholar
  30. Webb, E., Jones, A., Barker, P., & van Schaik, P. (2004). Using e-learning dialogues in higher education. Innovations in Education and Teaching International, 41(1), 93–103.CrossRefGoogle Scholar
  31. Winerman, L. (2012). Reading, writing, and self-esteem. Utne Reader: The Best Of The Alternative Press, 169, 16–17.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Brett E. Shelton
    • 1
  • Jui-Long Hung
    • 1
  • Sarah Baughman
    • 1
  1. 1.Department of Educational TechnologyBoise State UniversityBoiseUSA

Personalised recommendations