Academic Analytics: A New Tool for a New Era
John P. Campbell, Peter B. DeBlois, and Diana G. Oblinger
EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40–57
This identifies several uses for analytics in education:
- To manage enrolment, using standardised exam scores, high school coursework, and other information to determine which applicants will be admitted.
- To inform fund-raising. By building a data warehouse containing information about alumni and friends, institutions can use predictive models to identify those donors who are most likely to give.
- To aid retention by identifying students most at risk of dropping out
- To assess which proactive interventions have the best influence on academic success and retention.
- To predict student success within a course
They also highlight three characteristics of successful academic analytics-based projects (link to referenced PDF):
- Leaders who are committed to evidence-based decision-making
- Administrative staff who are skilled at data analysis
- A flexible technology platform that is available to collect, mine, and analyze data
Within the OU, the IET Student Statistics department takes a leading role in analytics projects like these. Other departments, such as Communications, also make use of analytics data.
My focus is on learning analytics - how we can use online analytics to identify learning, conditions that support learning and behaviours that support learning.