Linking early alert systems and student retention: a survival analysis approach

Author:

Villano Renato,Harrison Scott,Lynch Grace,Chen George

Publisher

Springer Science and Business Media LLC

Subject

Education

Reference27 articles.

1. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. In Proceedings of the Second International Conference on Learning Analytics and Knowledge. ACM. 267-270.

2. Baker, R. S., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies., 68(4), 223–241.

3. Baker, R. S., Lindrum, D., Lindrum, M. J., and Perkowski, D. (2015). Analyzing early at-risk factors in higher education e-learning courses. Retrieved 12/09/2015 from http://www.columbia.edu/~rsb2162/2015paper41.pdf

4. Caufield, M. (2013a). A simple, less mathematical wat to understand the course signals issue. Hapgood. Retrieved 10/07/2015, from http://hapgood.us/2013/09/26/a-simple-less-mathematical-way-to-understand-the-course-signals-issue/

5. Caufield, M. (2013b). Why the course signals math does not add up. Hapgood. Retrieved 08/12/2014 from http://hapgood.us/2013/09/26/why-the-course-signals-math-does-not-add-up/

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