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Learning Analytics with Blackboard

Learning Analytics with Blackboard . 28 August 2012. 7 March 2013. Dan Peters dan.peters@blackboard.com @ danspeters. “The Third Wave”. - Malcom Brown, Director of EDUCAUSE Learning Initiative. “The Third Wave”. - Malcom Brown, Director of EDUCAUSE Learning Initiative.

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Learning Analytics with Blackboard

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

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

  3. “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative Learning Management System 2000 2005 2010 2015

  4. “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative Web 2.0 Learning Management System 2000 2005 2010 2015

  5. “The Third Wave” - Malcom Brown, Director of EDUCAUSE Learning Initiative Web 2.0 Learning Management System 2000 2005 2010 2015

  6. 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 “Academic Analytics” *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. Data Instructor Attributes User Activity Data Student System Student Attributes Course Item Data Course Attributes Grade Center Results Final Grades Enterprise Level Analyses Trend Analyses Metrics and Correlations

  11. Predictions

  12. Predictions

  13. Predictions

  14. Predictions

  15. Predictions

  16. Views on Data • Dashboards • Dynamic Analysis • Reports

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

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

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