1 / 25

Academic Analytics: Using Data from Learning Tools to Improve Student Success

Academic Analytics: Using Data from Learning Tools to Improve Student Success. John P. Campbell Wednesday, March 5, 2008 8:10-9:00 am. Student Success.

jamesmmartin
Download Presentation

Academic Analytics: Using Data from Learning Tools to Improve Student Success

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Academic Analytics: Using Data from Learning Tools to Improve Student Success John P. Campbell Wednesday, March 5, 2008 8:10-9:00 am

  2. Student Success Student success is defined as an intentional experience that leads to a degree, intellectual and personal growth, and prepares a student for life and a career in a dynamic, global society. 2

  3. Forms of Success/Retention • Course • Program • Institution 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. Process of Analytics Capture Report Predict Act Refine Adapted from: Eckerson, W. W. (2006) Performance Dashboards: Measuring, Monitoring, and Managing Your Business. John Wiley & Sons, Inc. Hoboken, NJ 5

  6. Project Phases Analytics • Exploration with historical data • Pilot Project: Phase 1 • Pilot Project: Phase 2 • Pilot Project: Expansion • Collaborative Tools 6

  7. Exploration of Historical Data Aptitude Effort

  8. Exploration of Historical Data • Use of analytics to predict which students need help • Selection of CMS Users • 27,276 unique students • 597 courses – over 3,000 sections • 75 departments and 9 colleges 8

  9. Predictive Power – Overall Population • Students needing help: 65.7% • Students doing well: 87.4% • Overall: 79.3% 9

  10. Usage is Important 10

  11. Predictive Results 11

  12. Pilot Project: Phase 1 • Gateway Biology course • 300 students • “Interventions” based on the predictive results • Goal: to encourage students to use existing resources 12

  13. Phase One: “The Plan” • Weeks 1, 2, 3 – email messages to students • Week 4 – contact from instructor • Week 5 – contact from advisor 13

  14. Results • Highest Risk: • Most remained “at risk” • Still unlikely to take advantage of resources • Lower Risk: • Majority were able to leave the “at risk” status – as long as feedback continued • More likely to take advantage of resources 14

  15. Biology Resource Center Usage

  16. Grades

  17. Student Responses • “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.” 17

  18. Pilot Project: Phase 2 • Refine the messages – tough love for high risk students • Continual feedback 18

  19. Student Response “I've recently been utilizing the BRC and even my classmates (not cheating of course, just studying together) to help me understand what I need to have for each lab/homework assignment. I feel like I've been understanding the material better and my homework grades have been improving. I think I understand what I need to do to continue this rise, so hopefully you'll be able to see that I'm well on my way to better grades. Thank you for your support though. I'm glad that you've set up so may ways for us to be successful in this class.”

  20. Next Steps: Expansion • Biology – Phase 3 • Freshman Engineering – Engineering, Chemistry, and Physics courses • Refining the interventions and increasing student awareness 20

  21. Analytics and Collaboration Tools • Potential wealth of “engagement” data • Wimba actively evaluating the first series of steps – which data is likely to lead to results 21

  22. Social Networking Analysis • What can we say about learning networks? 22

  23. Lessons Learned • Privacy is the eye of the beholder • Move towards data-driven decision making is a time consuming process • Challenge in creating awareness • Sustainability • Obligation of knowing 23

  24. How to Start • Participate in the discussions • Focus on staff development • Inventory possible data sources • Build relationships for future collaboration • Create a pilot project 24

  25. Resources • Academic Analytics Overview: http://www.educause.edu/ir/library/pdf/PUB6101.pdf • EDUCAUSE Review: Academic Analytics: A New Tool for a New Era http://www.educause.edu/apps/er/erm07/erm0742.asp 25

More Related