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Building Capacity for Analytics within the Academic Environment

Building Capacity for Analytics within the Academic Environment. John P. Campbell Purdue University. Building capacity…. Fostering an environment for exploration Building campus support Building capacity = building community. 2. 1. Keep it Simple, but Important. Aptitude. Effort . 3.

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Building Capacity for Analytics within the Academic Environment

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

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

  3. 1. Keep it Simple, but Important Aptitude Effort 3

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

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

  6. Keep it Simple • 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

  7. 2. Scale over time

  8. 3. Visualize 8

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

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

  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

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