LearningAnalytics      Dr. Janet Corral     University of Colorado
What?!
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Big Data for Business
Overview• Definitions• Case Studies  – Data  – Visualization  – Prediction• Challenges & Opportunities• Where do we go fro...
What are „Analytics‟?         0101110001101         0101011010100         0101011010101         0010111010101         0101...
What are “learning analytics”? Measurement, collection, analysi  s, and reporting of data about    learners and their cont...
Learning    AcademicAnalytics   Analytics
Health sciences education            LMS           Exams          ePortfolio           Lecture           Capture          ...
10100011111010101111010101001011       10101011101011101010011010101000DATA   00110101011110101010100101010100       10101...
LMS + Google Analytics = ?    With gratitude to the Entrada Project: http://www.entrada-project.org/
With gratitude to the Entrada Project: http://www.entrada-project.org/
Podcasting                                               Hemopathology                                                    ...
What Does The Data Mean?•   Clicks•   Navigation•   Assumptions•   Interpretation•   Confusion•   Predictors and causes•  ...
Better than surveys?Google Analytics: Operating System
VISUALIZATION
Transforming Data
Dashboards
NYU Educational Data Warehouse   With gratitude to NYU Educational Data Warehouse: alex.support@med.nyu.edu
Consider:  • Visualization  • Reporting  • Timelines  • Expectations  • Policies
LEARNINGANALYTICSFOR …LEARNING            Image credit: http://www.fromoldbooks.org/pictures-of-            old-books/page...
Feedback for Faculty           http://www.itap.purdue.edu/learning/tools/signals/
Feedback for Teachers    With gratitude to Forefront Math: www.forefrontmath.com
Feedback for Teachers
Feedback for Learners
K-12 School Trends
Virtual Patients   Corral, J. (2012). Learning with Virtual Patients.
The Gold Standard Path       Corral, J. (2012). Learning with Virtual Patients.
Hmm…Successes                      LimitationsFrequency by individual node   Click = ?Pathways by individual user    Insig...
DISCUSSION
PREDICTION
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How do youselect thebest futureMD?
With Gratitude to Drs. Harold Reiter and Kelly Dore, McMaster University                            http://fhs.mcmaster.ca...
Size Matters    UCSF Per Year       Denver Public Schools82 dentistry students     81,438 students167 medical students    ...
Quantitative dominance?
CHALLENGES &OPPORTUNITIES
Challenges & Opportunities                    Data•   Size               •   Security•   Sharing            •   Cross-inst...
Challenges & OpportunitiesTools• Variety• Algorithms• Transparency• Openness• Focus• Scale
Challenges & Opportunities                       People•   Multiple skill sets•   Collaboration•   Education•   Ownership•...
Challenges & Opportunities             Educational Impact•   Success•   Ownership•   Ethics•   Privacy•   Return on Invest...
Teaching with and about analytics                      Carrier Status                       Disease Risk                  ...
Thank you! Janet Corral @edtechcorral janet.corral@ucdenver.edu
Learning analytics UCSF Keynote
Learning analytics UCSF Keynote
Learning analytics UCSF Keynote
Learning analytics UCSF Keynote
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  • Moodle has 2 plug in “blocks”: block_analytics_recommendationsblock_graph_stats
  • 1. Don’t confuse predictors and causes. 2. What about social learning processes?
  • Share in pairs
  • Computer based Assessment for PERsonal characteristics (CASPER) a web-based assessment of interpersonal skills and decision-makingCompared to the Autobiographical Submission, CASPer is significantly more reliable, predicts much more validly for subsequent performance, and requires less applicant time.
  • Helping our students prepare for the future of personalized medicine
  • Learning analytics UCSF Keynote

    1. 1. LearningAnalytics Dr. Janet Corral University of Colorado
    2. 2. What?!
    3. 3. 01011100011010101011010100010101101010100101110101010101010111101010110101011010101101010101010101001010101010101000101010101010100010101010100101110101001010101010101110101010100101011101010101010100011101011010101
    4. 4. Big Data for Business
    5. 5. Overview• Definitions• Case Studies – Data – Visualization – Prediction• Challenges & Opportunities• Where do we go from here?
    6. 6. What are „Analytics‟? 0101110001101 0101011010100 0101011010101 0010111010101 0101010111101 0101101010110 1010110101010 1010101001010 1010101010001 0101010101010 0010101010100 1011101010010 1010101010111 0101010100101 0111010101010 1010001110101 0101010111010 1000101010111
    7. 7. What are “learning analytics”? Measurement, collection, analysi s, and reporting of data about learners and their contexts Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Sh um, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
    8. 8. Learning AcademicAnalytics Analytics
    9. 9. Health sciences education LMS Exams ePortfolio Lecture Capture Admissions
    10. 10. 10100011111010101111010101001011 10101011101011101010011010101000DATA 00110101011110101010100101010100 10101010101010101100110110101010 10111101010100001011110101110010 11000010101011101111100010101101 00010110100011111010101111010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101
    11. 11. LMS + Google Analytics = ? With gratitude to the Entrada Project: http://www.entrada-project.org/
    12. 12. With gratitude to the Entrada Project: http://www.entrada-project.org/
    13. 13. Podcasting Hemopathology Anemia 60 60Viewing Time (in mins) Viewing Time (in mins) 50 50 40 40 30 30 20 20 10 10 0 0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s1 s2 s3 s4 s5 s6 s7 s8 Students Students Hematopoesis Intro to Coagulation 70 70 60 60Viewing Time (in mins) 50 Viewing Time (in mins) 50 40 40 30 30 20 20 10 10 0 0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s1 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 Students Students With gratitude to UC Denver META Unit: http://goo.gl/g90rs
    14. 14. What Does The Data Mean?• Clicks• Navigation• Assumptions• Interpretation• Confusion• Predictors and causes• Data + behavioural practices
    15. 15. Better than surveys?Google Analytics: Operating System
    16. 16. VISUALIZATION
    17. 17. Transforming Data
    18. 18. Dashboards
    19. 19. NYU Educational Data Warehouse With gratitude to NYU Educational Data Warehouse: alex.support@med.nyu.edu
    20. 20. Consider: • Visualization • Reporting • Timelines • Expectations • Policies
    21. 21. LEARNINGANALYTICSFOR …LEARNING Image credit: http://www.fromoldbooks.org/pictures-of- old-books/pages/img_7378-stack-of-books/
    22. 22. Feedback for Faculty http://www.itap.purdue.edu/learning/tools/signals/
    23. 23. Feedback for Teachers With gratitude to Forefront Math: www.forefrontmath.com
    24. 24. Feedback for Teachers
    25. 25. Feedback for Learners
    26. 26. K-12 School Trends
    27. 27. Virtual Patients Corral, J. (2012). Learning with Virtual Patients.
    28. 28. The Gold Standard Path Corral, J. (2012). Learning with Virtual Patients.
    29. 29. Hmm…Successes LimitationsFrequency by individual node Click = ?Pathways by individual user Insight into thinking processesData we didn’t have before Insight into decision making Individual or collective use?
    30. 30. DISCUSSION
    31. 31. PREDICTION
    32. 32. 01011100011010101011010100010101101010100101110101010101010111101010110101011010101101010101010101001010101010101000101010101010100010101010100101110101001010101010101110101010100101011101010101010100011101011010101
    33. 33. How do youselect thebest futureMD?
    34. 34. With Gratitude to Drs. Harold Reiter and Kelly Dore, McMaster University http://fhs.mcmaster.ca/mdprog/casper.html
    35. 35. Size Matters UCSF Per Year Denver Public Schools82 dentistry students 81,438 students167 medical students Facebook500 pharmacy students University of Pheonix 1 billion monthly active users 400,000 students per year
    36. 36. Quantitative dominance?
    37. 37. CHALLENGES &OPPORTUNITIES
    38. 38. Challenges & Opportunities Data• Size • Security• Sharing • Cross-institutional• Compatibility • Policies• Algorithms • Transparency• Storage • Openness• Focus
    39. 39. Challenges & OpportunitiesTools• Variety• Algorithms• Transparency• Openness• Focus• Scale
    40. 40. Challenges & Opportunities People• Multiple skill sets• Collaboration• Education• Ownership• Ethics• Privacy• Transparency
    41. 41. Challenges & Opportunities Educational Impact• Success• Ownership• Ethics• Privacy• Return on Investment• Multiple skill sets• Collaboration• Education
    42. 42. Teaching with and about analytics Carrier Status Disease Risk Drug Response Research
    43. 43. Thank you! Janet Corral @edtechcorral janet.corral@ucdenver.edu

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