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Learning Analytics:
Education Administration
     Consideration

      Dr. Janet Corral

         @edtechcorral
What are ‘Analytics’?
         0101110001101
         0101011010100
         0101011010101
         0010111010101
         0101010111101
         0101101010110
         1010110101010
         1010101001010
         1010101010001
         0101010101010
         0010101010100
         1011101010010
         1010101010111
         0101010100101
         0111010101010
         1010001110101
         0101010111010
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Academic Analytics

• Mining data associated with the
  administration of education



                 See Campbell & Oblinger, 2007
What is “learning analytics”?




       Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Sh
       um, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
What is medical education gathering?
                 LMS




                Exams



               ePortfolio



                Lecture
                Capture



               Admissions
What are we assuming?
•   Clicking
•   Active vs passive choices
•   Learning vs. Interest – “What is there?”
•   Tool is ready to learn with and from
•   Obedience
•   Analytical tools readiness
What are we interpreting?
• Activity in some form…
  – Usage
  – Learning
• With some interpretation…
  – Inference
  – Assumptions
  – Hope
Analysis of Learning:
       What are we conceptualizing?
•   Positivist, deterministic
•   Intervention, Treatment
•   Complex
•   Dynamic
•   Co-influencing
•   Participatory, Proximal, Distal
•   Activity
•   Expertise
Transparency and Literacy
• Of data collection
• Of reporting the data back to user (faculty, student)
• Helping user (faculty, student) make sense of the
  data
Just “our” systems?
• Across institutional and personal web
  applications (Retalis et al, 2006)
  – Limited view of learning
  – Expanded view of learning




                          Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., S
                          hum, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
Ethics
• Stealth assessment
• Reported forms
• Data ownership, access, expiration
WHERE WE MIGHT LEAD
Integrating Data Sets

                     Logbook
     Simulation




                  Exams




   Competency or Expertise?
Data Ownership




    http://www.healthcareitnews.com/news/patients-sue-walgreens-making-money-their-data
Analytics Literacy & Skills
             As a Student
             • Formative progress
             • Summative evaluation
             • Predictive
             • Algorithms, Transparency
             • Meaning


 Student
Education
               As a Doctor
               • EVBM
               • Rx
               • Predictive medicine
Patient Literacy & Skills




http://www.slideshare.net/PewInternet/2012-1-12-12-rise-of-epatients-providence-st-
joseph-medical-centerpptx
Analytics Literacy & Skills

                     Health literacy –
                          EVBM




 Patient
                                          Health literacy -
                                             Provider




Education

                                         Privacy




             Ownership/Advocate
Participate!
Thank you!

          Janet Corral
Faculty, Educational Informatics

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Learning analytics: educational administration considerations

  • 1. Learning Analytics: Education Administration Consideration Dr. Janet Corral @edtechcorral
  • 2. What are ‘Analytics’? 0101110001101 0101011010100 0101011010101 0010111010101 0101010111101 0101101010110 1010110101010 1010101001010 1010101010001 0101010101010 0010101010100 1011101010010 1010101010111 0101010100101 0111010101010 1010001110101 0101010111010 1000101010111
  • 3. Academic Analytics • Mining data associated with the administration of education See Campbell & Oblinger, 2007
  • 4. What is “learning analytics”? Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Sh um, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
  • 5. What is medical education gathering? LMS Exams ePortfolio Lecture Capture Admissions
  • 6. What are we assuming? • Clicking • Active vs passive choices • Learning vs. Interest – “What is there?” • Tool is ready to learn with and from • Obedience • Analytical tools readiness
  • 7. What are we interpreting? • Activity in some form… – Usage – Learning • With some interpretation… – Inference – Assumptions – Hope
  • 8. Analysis of Learning: What are we conceptualizing? • Positivist, deterministic • Intervention, Treatment • Complex • Dynamic • Co-influencing • Participatory, Proximal, Distal • Activity • Expertise
  • 9. Transparency and Literacy • Of data collection • Of reporting the data back to user (faculty, student) • Helping user (faculty, student) make sense of the data
  • 10. Just “our” systems? • Across institutional and personal web applications (Retalis et al, 2006) – Limited view of learning – Expanded view of learning Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., S hum, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
  • 11. Ethics • Stealth assessment • Reported forms • Data ownership, access, expiration
  • 13. Integrating Data Sets Logbook Simulation Exams Competency or Expertise?
  • 14. Data Ownership http://www.healthcareitnews.com/news/patients-sue-walgreens-making-money-their-data
  • 15. Analytics Literacy & Skills As a Student • Formative progress • Summative evaluation • Predictive • Algorithms, Transparency • Meaning Student Education As a Doctor • EVBM • Rx • Predictive medicine
  • 16. Patient Literacy & Skills http://www.slideshare.net/PewInternet/2012-1-12-12-rise-of-epatients-providence-st- joseph-medical-centerpptx
  • 17. Analytics Literacy & Skills Health literacy – EVBM Patient Health literacy - Provider Education Privacy Ownership/Advocate
  • 19. Thank you! Janet Corral Faculty, Educational Informatics

Editor's Notes

  1. Limited view of learning (e.g. discussion postings, sharing links)Expanded view of learning (e.g. students don’t use the LMS exclusively; learning happens across personal and institutional contexts)
  2. Educate students on where data obtained, how mined, how used. Also show them how it might be used to assess their own students (teachers), is changing EVBM (MDs), might change how Pts are treated (MDs),