1. Learning Analytics with Blackboard 28 August 2012 7 March 2013 Dan Peters dan.peters@blackboard.com @danspeters

2. 2 “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative

3. 3 200520002015 Learning Management System 2010 “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative

4. 4 200520002015 Learning Management System Web 2.0 2010 “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative

5. 5 200520002015 Learning Management System Web 2.0 2010 “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative

6. “Academic Analytics” Refers to a collective set of “business intelligence” activities to support the mission of the institution Includes: –Data warehousing –Reporting –Predictive modeling Modeling is based on program and population specific factors designed to improve: –Retention –Performance *Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42 (4), 40-42

7. What Do We Need For Learning Analytics? Data Predictive Modeling – (Questions and Results) Reports/Views of Data Continual process Best Practices Defined Metrics Business Rules Derived Information

8. Where to Begin? Will data REALLY optimize educational experience? Uncertainty about where to start No established industry best practice about what to measure No established industry best practice around methodology Organizational Culture, Learning Culture and Status Quo Enterprise concern about what the data will show Competing priorities and lack of incentive for collaboration between different groups Siloed data across the enterprise sure doesn’t help - 2011 Online Educa Berlin, Ellen Wagner, Sage Road Solutions, LLC

9. Questions Are students engaged in their courses? How does level of activity influence grades? Can we identify and interact with “at-risk” students before they fail? Can we motivate students through comparison? What are the correlations between use of certain LMS tools and student success? Are we meeting our adoption goals?

10. Where Learning Data Typically Live ERPs and SISs Demographics, financials, operations Macro level transactions Learning Management System (LMS) Learning transactions Learning outcomes Latent data End of Course Survey Perceptual data

11. Data Grade Center Results Course Attributes Course Item Data Student Attributes Final Grades Student System Instructor Attributes User Activity Data Enterprise Level Analyses Trend Analyses Metrics and Correlations

12. Predictions

17. Views on Data Dashboards Dynamic Analysis Reports

18. 18 Fundamental of Analytics

19. Is student activity a valid indicator for student success?

20. Fundamental of Analytics Is student activity a valid indicator for student success? How do I measure student engagement?

21. Fundamental of Analytics Is student activity a valid indicator for student success? How do I measure student engagement? What are the correlations between course design, tool usage, and student performance?

22. Fundamental of Analytics Is student activity a valid indicator for student success? How do I measure student engagement? What are the correlations between course design, tool usage, and student performance? Is my LMS adoption rate growing as predicted?

23. Information Needs Vary DATA TO HELP ME On Demand Easy to Access and Easy to Digest But There Are Common Themes

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27. Improve decision making. Improve institutional performance. About Blackboard Analytics for Learn: www.blackboardanalytics.com www.blackboardanalytics.com