Academic Analytics: A New Tool for a New Era

I've followed a link in OUseful Info to Educause, where I'm looking at
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):

1. Leaders who are committed to evidence-based decision-making
2. Administrative staff who are skilled at data analysis
3. 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.

Building on previous experience

Tony Hirst helpfully Twittered a Google search term for locating his posts on Google Analytics and his uncourse
allintitle: "course analytics" site:ouseful.open.ac.uk

He began (24 October 2007) with a focus on:
"four distribution (rather than average) measures that are useful for analysing user behaviour on non-ecommerce websites:
* Visitor loyalty - how often has each user visited the site over a given period;
* Visitor recency - of all the people who have visited the site, how many have visited in the last N days;
* Length of visit - how long do visitors stay on site;
* Depth of visit - how many pages on the site are seen on each visit"

His focus (25 Oct) was on "website analytics can be used applied to online course websites in order to gain a better understanding of online study habits and the bahaviour of students taking an online course." The length of visit figure gave an idea of how long students were long to spend online studying course material (approx 30 mins in this case).

On 26 Oct he focused on timing of visits. Students on the course appeared to be less likely to visit on a Saturday, and seemed to be online more at lunchtimes and in the early to mid-evening. Apart from these daily and weekly patterns, there were also spikes associated with deadlines.

This is a small dataset (around 100 students) in the context of the OU, and they were studying an online computer course which makes them likely to be atypical in terms of computer use. Still, it gives some broad hypotheses - students will prefer online material provided in up to 30-minute chunks and they are more likely to be available for collaborative activities in lunchtimes and evenings.