OEB 11 Jeff's Keynote

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Jeff Borden's Keynote Online Educa Berlin. Join us on twitter @pearsonpltfrms to stay in touch. We look forward to meeting you there!

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  • Part of our descriptive analytics story: These are course paths for two different sections for the same course showing how the professor can influence behavior in a course. Details: Traffic moves clockwise from node to node Thickness of the line shows the relative frequency of that path Size and color of the node shows how common use of that feature is Course 1 shows heavy traffic between course home/content and threads and in reverse as well. The other features of the course are much less used as shown by the small node sizes. Course 2 shows shows a much more balanced utilization of the course tools in both node size and path thickness showing a more balanced course. Performance in course 2 was significantly better than in course 1. The simple visualization of a few courses gives us a lead to go on for something that may lead to correlations to student success. We could hypothesize that a more balanced and deeper use of the course tools leads to more student success in a course.
  • This is the same type graph for the same two courses, but it looks at thread interactions between users in a course. Here the professor is blue and the colors for the students indicates the final grade in the course with green being higher and red failing or dropping. Again you can see very clearly several things that jump out. Course 2 is obviously more interactive and the nodes are more tightly clustered. Several subgroups have sprung up where students are interacting with each other more in course 2 vs just student to professor in course 1. You can see a few students in each course who reached out through threads many times but didn’t get responses. That seems to trend with poor performance. Again, these are two sections of the same course with different professors that show clearly different student behavior. We also see again that these simple visualizations of sample data can show us potential correlations that we want to dig into more. That’s the power of visualization of descriptive analytics.
  • 2:50
  • This is the same type graph for the same two courses, but it looks at thread interactions between users in a course. Here the professor is blue and the colors for the students indicates the final grade in the course with green being higher and red failing or dropping. Again you can see very clearly several things that jump out. Course 2 is obviously more interactive and the nodes are more tightly clustered. Several subgroups have sprung up where students are interacting with each other more in course 2 vs just student to professor in course 1. You can see a few students in each course who reached out through threads many times but didn’t get responses. That seems to trend with poor performance. Again, these are two sections of the same course with different professors that show clearly different student behavior. We also see again that these simple visualizations of sample data can show us potential correlations that we want to dig into more. That’s the power of visualization of descriptive analytics.
  • OEB 11 Jeff's Keynote

    1. 1. Useful Ed Tech: What Teachers Want / What Education Needs
    2. 3. Dr. Jeff D Borden
    3. 4. Dr. Jeff D Borden
    4. 5. Learning Today
    5. 6. Agenda <ul><li>Tell, Show, Do, Review, Ask </li></ul><ul><li>Data </li></ul><ul><li>Personalization </li></ul>
    6. 7. Use A Learning Environment
    7. 8. Anywhere, Anytime Engagement
    8. 9. Utterli
    9. 10. Tell
    10. 11. Show
    11. 12. Do
    12. 13. Review
    13. 14. Ask
    14. 15. Remember Something…Forever!
    15. 16. Problem Solving vs Problem Finding
    16. 18. At Risk Reports Avg. = 5.0 Avg. Student Activity/Section at Day 10 (hrs) Avg. = 2.5 Dropped Students Completed Students
    17. 19. Retention Analysis Avg. Section Completion Rate = 80% Department Term Start Enrollments BUS Department Completion Rates: Term Start to Term End I II III IV MATH ENGL MGMT ACCT COMP OTHER HRM ECON HUM HIST AVI MKT ART LAW BIO SCI
    18. 20. Outcomes
    19. 21. Course Path
    20. 22. Social Interaction
    21. 23. Collaboration Graphs
    22. 24. Nicholas Christakis: Clusters
    23. 25. Nicholas Christakis
    24. 26. Yellow-Happy Blue-Sad Green-Mid
    25. 27. Social Interaction
    26. 28. Would Students Benefit From A New Cluster? Student Clusters
    27. 29. Data Mining - Amazon <ul><li>Individuals </li></ul><ul><li>Surveys </li></ul><ul><li>Purchase History </li></ul><ul><li>Wishlist </li></ul><ul><li>Often Viewed </li></ul><ul><li>-- </li></ul><ul><li>Purchase Categories </li></ul><ul><li>Amounts </li></ul><ul><li>Area of the Country </li></ul>
    28. 30. Data Mining – Amazon…You Might Also Like
    29. 31. Data Mining - Education <ul><li>Time </li></ul><ul><li>Activity </li></ul><ul><li>Grades </li></ul><ul><li>Outcomes </li></ul><ul><li>Completion / Retention Rates </li></ul><ul><li>-- </li></ul><ul><li>Teacher </li></ul><ul><li>Learning Assets </li></ul><ul><li>Assessments </li></ul><ul><li>Courses </li></ul>
    30. 34. Thank You! Dr. Jeff D Borden Chief Academic Officer [email_address]

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