I am participating in the #MOOC Learning Analytics and Knowledge 2012 #LAK12. I had a chance to read some of the articles from the course bibliography today. I found this article, “Academic Analytics” by John Campbell and Diana G. Oblinger (2007) very helpful in determining what predictors of student success could be useful in higher education.
Campbell and Oblinger acknowledge that higher education has entered an an era of heightened scrutiny as governments, accrediting agencies, students, parents, and donors call for new ways of monitoring and improving student success. They, along with many others, maintain that academic (or learning) analytics can help institutions address student success and accountability while better fulfilling their academic missions.
The authors define “academic analytics” as an engine to make decisions or guide actions. That engine consists of five steps: capture, report, predict, act, and refine.
I have been struggling with identifying the right sources of data. In the Campbell and Oblinger article, the table of “Types and Sources of Institutional Data” was helpful to me. Some of the factors to consider when thinking about data collection include:
- Demographic Data
- Academic Ability
- Academic Performance
- Academic History
- Participation Information
- Academic Effort
- Institutional Information
The article is a great read and I am looking forward to reading and learning more as the class progresses.
Campbell, & Oblinger, D. (2007). Academic analytics. Washington, DC: EDUCAUSE Center for Applied Research