1. An introduction to the what, where, who, and what-for of Analytics

2. Contents (pg 1 of 7)  What is “Analytics”  Where is CCCOnline in terms of Learning Analytics?  What is the Desire2Learn Analytics product? What can it actually do?  What have other institutions done? Where are other institutions going?

3. What are “Learning Analytics” to us?  Analytics is processing data in some fashion that will help us do our jobs as administrators or instructors.  It is similar to and includes earlier fields/fads, such as “educational data mining”, but implies visualization of data so as to be made more useful to faculty and staff.

4. What is CCCOnline up to  Desire2Learn progress tracking Faculty in-attendance alerts Student no-show reports  Desire2Learn Analytics Behavior analysis

5. D2L Progress Tool  Not graphical, all tables

6. D2L Analytics – Faculty Portal  What are my students doing at a glance? Tool use Grade patterns

7. Quiz Consistency Analysis “Does my quiz measure just one thing?”

8. D2L Analytics Proper

9. D2L Analytics – data domains  Sessions – “When have they been in their course?”  Tool use – “When did they go into the discussions?”  Content access – “What have they read?” Difficulties with content  Grades Various gradebook designs  Quiz question grades

10. What are other institutions doing?  What is out there that we want to achieve as well?  Who is doing what?  Visualizing data ○ Standard reports - What happened? ○ Ad hoc reports - How many how often and were ○ Query/Drill down -Where exactly is the problem? ○ Alerts - What actions are needed? ○ Statistical Analytiss - Why is this happening? ○ Forecasting/Extrapoluation -What if these trends continue? ○ Predictive Modeling - What will happen next? ○ Optimization - What’s the best that can happen?

11. Katholieke Universiteit Leuven “Monitor Widget”  Visually compare your time in class or resources accessed with your peers.  “Am I doing what I should be in order to be successful?”

12. SNAPP Universities of Queensland and Wollongong, Australia University of British Columbia, Canada

13. University of Belgrade “LOCO-Analyst”

14. Local-Analyst Content Access & Analysis

15. Loco-Analyst Social Network Analysis

16. Minnesota State College and Universities “Accountability dashboard”

17. Predictive modeling

18. Signals  http://www.itap.purdue.edu/tlt/signals/sig nals_final/index.htm http://www.itap.purdue.edu/tlt/signals/sig nals_final/index.htm

19. Signals illustrated

20. Signals Faculty Dashboard  Student success at a glance  Prepare and dispatch custom intervention E-mails

21. American Public University System  For profit university serving over 80k online students.  Collects almost a hundred metrics based on student demographics, prior grades, and current course data.  Metrics are fed into a Neural Network that compares the metrics to grades in previous semesters, ranking the students from 1-80k in their chances of success.  The user can drill down to find out exactly what makes the network “think” a student will fail.

22. Recommendation Engine Fruanhofer Insituttion for Applied information Technology at FIT  Domain Ontology  + Usage patterns of prior users  + Identifying feature of “this” user – a search term, academic status, etc  = Recommended resources

23. Another example of a recommendation engine…

24. Semantic Analysis Open University, UK  Look into the content of posts to determine what style of communication it is. Challenges eg But if, have to respond, my view Critiques eg However, I’m not sure, maybe Discussion of resources eg Have you read, more links Evaluations eg Good example, good point Explanations eg Means that, our goals Explicit reasoning eg Next step, relates to, that’s why Justifications eg I mean, we learned, we observed Others’ perspectives eg Agree, here is another, take your point

25. Ultimate Goal  Modeling/Predicting success  Staging the most effective interventions  Improving instructor abilities  Improving students’ self awareness  Customized learning Learning Styles Cognitive Load  The hierarchy of student success through Action Analytics ○ Raising Awareness (Analytics IQ) ○ Data, Information, and Analytics Tools and Applications ○ Embedded Analytics in student success processes ○ Culture of performance measurement and improvement ○ Optimized student success

26. Dangers  “Analytics for learners rather than of learners” - Dragan Gasevic, Athabascau U.  Trapping students into limiting models of “good” behavior.  Disrupting and Transformative Innovation – Institutions resist change