Identification of "at risk" students and retention
Many of the tools developed early on in LA centred around the identification of at risk students, and associated interventions aimed at improving their performance. This has led to the mistaken impression that LA is just about the identification of at risk students, but if we look at the paper by Sharkey and Ansari (2014) demonstrates that even 3 years ago the field was far more diverse.
- 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.
- Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), n1. Available at: http://er.educause.edu/articles/2010/3/signals-applying-academic-analytics
- Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). ACM.
- Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013).
Improving retention: predicting at-risk students by analysing clicking
behaviour in a virtual learning environment. In Proceedings of the third international conference on learning analytics and knowledge (pp. 145-149). ACM.
- The Open Learning Analytics Initiative's predictive analytics tools can be found here: https://confluence.sakaiproject.org/pages/viewpage.action?pageId=75671025