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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 Learning analytics UCSF Keynote Presentation Transcript

  • LearningAnalytics Dr. Janet Corral University of Colorado
  • What?!
  • 01011100011010101011010100010101101010100101110101010101010111101010110101011010101101010101010101001010101010101000101010101010100010101010100101110101001010101010101110101010100101011101010101010100011101011010101
  • Big Data for Business
  • Overview• Definitions• Case Studies – Data – Visualization – Prediction• Challenges & Opportunities• Where do we go from here?
  • What are „Analytics‟? 0101110001101 0101011010100 0101011010101 0010111010101 0101010111101 0101101010110 1010110101010 1010101001010 1010101010001 0101010101010 0010101010100 1011101010010 1010101010111 0101010100101 0111010101010 1010001110101 0101010111010 1000101010111
  • 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)
  • Learning AcademicAnalytics Analytics
  • Health sciences education LMS Exams ePortfolio Lecture Capture Admissions
  • 10100011111010101111010101001011 10101011101011101010011010101000DATA 00110101011110101010100101010100 10101010101010101100110110101010 10111101010100001011110101110010 11000010101011101111100010101101 00010110100011111010101111010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101 00101110101011101011101010011010 10100000110101011110101010100101 01011000010101011101111100010101
  • 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 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
  • What Does The Data Mean?• Clicks• Navigation• Assumptions• Interpretation• Confusion• Predictors and causes• Data + behavioural practices
  • 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/pages/img_7378-stack-of-books/
  • 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 Insight into thinking processesData we didn’t have before Insight into decision making Individual or collective use?
  • DISCUSSION
  • PREDICTION
  • 01011100011010101011010100010101101010100101110101010101010111101010110101011010101101010101010101001010101010101000101010101010100010101010100101110101001010101010101110101010100101011101010101010100011101011010101
  • How do youselect thebest futureMD?
  • With Gratitude to Drs. Harold Reiter and Kelly Dore, McMaster University http://fhs.mcmaster.ca/mdprog/casper.html
  • 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
  • Quantitative dominance?
  • CHALLENGES &OPPORTUNITIES
  • Challenges & Opportunities Data• Size • Security• Sharing • Cross-institutional• Compatibility • Policies• Algorithms • Transparency• Storage • Openness• Focus
  • Challenges & OpportunitiesTools• Variety• Algorithms• Transparency• Openness• Focus• Scale
  • Challenges & Opportunities People• Multiple skill sets• Collaboration• Education• Ownership• Ethics• Privacy• Transparency
  • Challenges & Opportunities Educational Impact• Success• Ownership• Ethics• Privacy• Return on Investment• Multiple skill sets• Collaboration• Education
  • Teaching with and about analytics Carrier Status Disease Risk Drug Response Research
  • Thank you! Janet Corral @edtechcorral janet.corral@ucdenver.edu