1. Building Capacity for Analytics within the Academic Environment John P. Campbell Purdue University

2. Fostering an environment for exploration Building campus support Building capacity = building community 2 Building capacity…

3. 3 1. Keep it Simple, but Important AptitudeEffort

4. Mining data from systems that support teaching and learning to provide customization, tutoring, or intervention within the learning environment “Actionable intelligence” 4 Academic Analytics

5. Real time predictions of student success within a course Utilize existing data sets Minimize impact on the faculty member 5 Signals Program

6. Utilize historical data Avoid temptation – one question will always lead to another Select a project in which understanding the process is as important as the project impact 6 Keep it Simple

7. 2. Scale over time

8. 8 3. Visualize

9. Higher levels of B/C grades Lower levels of D/F grades Earlier drops Increased help-seeking behavior in students 9 4. Measure Progress

10. Measure Impact

11. Students’ Views “Really appreciate knowing how I'm doing before I get too far into the course.” “Your message was a "kick in the butt" that woke me up.” “You mean, if I get help, I'll do better, and it won't be counted against me?” “This biology lab is the hardest I've ever taken, but your message let me know that I need to get more help. Also, I can see that this lab is helping me in my biology lecture course, and in my chemistry lab.”

12. Student messages “Help facilities” Faculty, advisors “Actionable Intelligence” 12 5. Focus on Actions

13. Building Capacity People: skills and partnerships Technology: important, but not sufficient Models: balance between predictability and scalability Success has been more about “actions” as the result of analytics