Top 5 things anatomy educators need to know about learning analytics

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Presentation to graduate students in Anatomical Sciences at the University of Colorado to prepare them for a career of higher education involving learning analytics. Concepts and tips are transferable to most other domains. Please contact author for original if you want to use in your course or class. Copyrighted under Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license.

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  • Assumes: You will teach in post-secondaryYou will intersect with practitioners who use anatomy (e.g surgeons, radiology)Visualization, Technology will continue to evolve
  • 5 mins
  • Moodle has 2 plug in “blocks”: block_analytics_recommendationsblock_graph_stats
  • Top 5 things anatomy educators need to know about learning analytics

    1. 1. 5 Things Anatomy Science Educators Should Know About Learning Analytics Dr. Janet Corral University of Colorado
    2. 2. Goal: Prepare you for analytics in your career as an educator & interfacing with clinical colleagues
    3. 3. #1 BE ABLE TO DEFINE & DISTINGUISH BETWEEN LEARNING ANALYTICS & ACADEMIC ANALYTICS
    4. 4. What are „Analytics‟? 0101110001101 0101011010100 0101011010101 0010111010101 0101010111101 0101101010110 1010110101010 1010101001010 1010101010001 0101010101010 0010101010100 1011101010010 1010101010111 0101010100101 0111010101010 1010001110101 0101010111010 1000101010111
    5. 5. What are “academic analytics”? The use of large administrative data sets (e.g. attendance, financial status, transcript scores, etc.) with statistical techniques and predictive modeling to improve decision making. (Campbell & Oblinger, 2007)
    6. 6. Learning Analytics Academic Analytics
    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. #2 LEARNING ANALYTICS MAY BE USED TO MONITOR COURSE, LEARNER, OR ACTIVITY PROGRESS
    9. 9. Dashboards – Course Level http://www.itap.purdue.edu/learning/tools/signals/
    10. 10. Dashboards – Course Level Credit: Moodle
    11. 11. Dashboard – Student Progress With gratitude to Forefront Math: www.forefrontmath.com
    12. 12. Dashboard – Activity Progress
    13. 13. Dashboard – Feedback to Learners Credit: Moodle
    14. 14. Recommendations on what or how to study Credit: University of Michigan
    15. 15. How emotions may impact online learning achievement Delight, Surprise (Rare) Frustration (less associated with poorer learning) Confusion, Engaged Concentration (Watch out for boredom!) Baker, D’Mello, Rodrigo, & Graesser, 2004
    16. 16. #3 ACADEMIC ANALYTICS MAY BE USED FOR COURSE EVALUATION OR TO PREDICT STUDENT SUCCESS
    17. 17. To assess student progress in course http://www.itap.purdue.edu/learning/tools/signals/
    18. 18. To link progress to outcomes
    19. 19. To understand gender impact Credit: University of Michigan
    20. 20. #4 ANALYTICS IS BEING USED IN CLINICAL DISCIPLINES
    21. 21. Bringing the Algorithms to the Data: Cloud–Based Benchmarking for Medical Image Analysis By: Allan Hanbury, Henning Müller, Georg Langs, Marc André Weber, Bjoern H. Menze, Tomas Salas Fernandez
    22. 22. Modeling health informatics capacity for neonatal intensive care patient journeys supported by interprofessional teams McGregor, Steadman, Pe rcival & James (2013)
    23. 23. Analytics can inform communication Credit: Civitas Learning
    24. 24. #5 ADAPTING TO CHANGE OVER YOUR CAREER IS IMPORTANT
    25. 25. Access Transparency Privacy Aggregation Retention & Destruction of Data Re-use
    26. 26. Many institutions are still figuring out how to best use analytics
    27. 27. Analytics, templates in eLearning tools may provide basic pedagogical guidance for faculty, and basic support for learners I am a teaching prosthetic.
    28. 28. Can we more deeply understand achievement? Baker, Hershkovitz, Rossi, Goldstein, & Gowder (In press)
    29. 29. We don’t yet know what visualizations are best. Expect different, expect more!
    30. 30. Analytics might apply to your research phenotype abnormality environmental impact
    31. 31. Questions?

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