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Swimming in Data – Radically Restructuring Classes Around Assessment

Swimming in Data – Radically Restructuring Classes Around Assessment. Jeffery S. Thomas, PhD, PE Assistant Teaching Professor Civil, Architectural & Environmental Engineering. http://classes.mst.edu/ide120/lessons/failure/plastic/tension_3/index2.html. Goal 1

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Swimming in Data – Radically Restructuring Classes Around Assessment

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  1. Swimming in Data – Radically Restructuring Classes Around Assessment Jeffery S. Thomas, PhD, PEAssistant Teaching Professor Civil, Architectural & Environmental Engineering

  2. http://classes.mst.edu/ide120/lessons/failure/plastic/tension_3/index2.htmlhttp://classes.mst.edu/ide120/lessons/failure/plastic/tension_3/index2.html

  3. Goal 1 convert my classes into teaching laboratories - presentation format - media - homework - exams - textbook Goal 2 dramatic increase in learning and people passing Learning analytics is the measurement, collection, analysis and reporting of data about learners, and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. - George Siemens

  4. Conclusions… • Most students like the new format • Measurements are stabilizing, trending • Performance in next class is consistent • Final exam performance is consistent • across class formats • across instructors • but no dramatic improvement yet

  5. Mechanics of Materials • Fall 2012 • 110 students • 65 hours of measured • engagement per student • Does not include: • LMS • textbook • MecMovies • homework done • outside of class • etc

  6. Hours of Measured Engagement Per Student in Mechanics of Materials

  7. Structural Analysis • Fall 2012 • 37 students • 63 hours of measured • engagement per student • Does not include: • LMS • textbook • homework done • outside of class • etc

  8. Let’s look at the class sites…

  9. Content Utilization for 2012All Users, 365 Days

  10. Content Utilization for Fall 2012Missouri versus All Users

  11. Let’s look at Google Analytics…

  12. Mechanics of Materials

  13. Materials Testing

  14. Real-Time Viewer

  15. Real-Time Viewer

  16. In-Page Analytics

  17. Let’s look at exams… 622 calculation questions (algorithmic) 421 conceptual questions (Bloom) 220 categories 132 enabling objectives 12 terminal objectives Diploma Respondus Blackboard EQATWileyPLUS

  18. Performance Index for Each Question

  19. Performance Index for Each Objective

  20. Performance Index for Each Chapter

  21. Ranked Categories

  22. Student Performance Maps

  23. Student Performance Maps

  24. Student Performance Maps

  25. Other measurements… Student satisfaction Attendance vs exam scores Amount of writing on exams Major, gender, class rank High school performance GPA, ACT, MBTI, FE Section size DFW Total students per semester Percentage of names memorized HW questions per semester Exam questions per semester Type of exam questions Class days and times

  26. MOOCs… ...might produce massive dynamic metadata and analytics that can feed back into the quality of on-campus instruction. ...138 million assessment transactions, and over 2.8 million weighted association rules between content resources... Ten Years Later: Why Open Educational Resources Have Not Noticeably Affected Higher Education, and Why We Should Care, G. Kortemeyer, Educause Review Online, Jan/Feb 2013

  27. Questions? • jthomas@mst.edu • web.mst.edu/~jthomas • Acknowledgements • Timothy Philpot, Douglas Carroll, Richard Hall, Malcolm Hays • Missouri S&T Educational Technology • Missouri S&T Educational Research Mini-Grant Program • Missouri S&T eFellows Program • Missouri S&T Office of the Vice Provost for Academic Affairs • NSF Grant 0837339 • many student assistants

  28. Most students like the format • Measurements are stabilizing, trending • Performance in next class is consistent • Final exam performance is consistent • across class formats and instructors • but no dramatic improvement yet • next step is formative quizzes Summary

  29. Outline • Resource usage • Satisfaction & pass rates • Exam performance • Misc. measurements Satisfaction & Pass Rates

  30. Inverted Classes • - watch videos outside of class • - do homework during class • - do experiments during class - flexibility - classroom space - learning analytics

  31. campus average

  32. campus average

  33. Outline • Resource usage • Satisfaction & pass rates • Exam performance • Misc. measurements Exam Performance

  34. Effect of Format • same instructor, author and resources • no significant difference between mean final exam scores • high GPA students do 5 pts better in inverted format • low GPA students do 2 pts worse in inverted format

  35. Effect of Instructor & Format • three Mechanics of Materials instructors • one Structural Analysis instructor • same author and problem solutions • no significant difference

  36. Does (optional) attendance matter?

  37. How much do students need to write down?

  38. What about gender and major?

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