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A Fractal-based Decision Engine for Teaching & Learning

This presentation by Dr. David Kokorowski explores a revolutionary fractal-based decision engine designed to improve teaching and learning by analyzing student behavior. The engine employs diagnostic analytics to identify at-risk students through their response patterns. By differentiating between persistent and anti-persistent behaviors, it allows educators to intervene effectively. Insights from pilots demonstrate the potential to unveil previously invisible at-risk students and improve engagement through timely alerts, motivating students to seek help and enhancing academic performance.

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A Fractal-based Decision Engine for Teaching & Learning

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  1. A Fractal-based Decision Engine for Teaching & Learning • David Kokorowski, PhD • VP Product Management • Pearson • EDI2014

  2. The Two Sigma Problem

  3. Answer-Specific Feedback

  4. Socratic Hints

  5. Auto-scored Answertypes

  6. Diagnostic Analytics

  7. A Simple Model of Student Behavior • ✓ - Correct answer is a step to the right • ✗ - Incorrect answer is a step to the left • Net Score = # steps to the right – # steps to the left

  8. Fractal Dimension = 1.94 • A student showing • random walk behavior • A student not showing • random walk behavior • Fractal Dimension = 1.60 • Distinguishing Response Patterns • simulated

  9. Distinguishing Response Patterns • Straight line • Plane • Fractal Dimension • 2 • 1 • Less irregularity in the response pattern • i.e., Persistent behavior • (an increase in net score at one instance is more likely to be followed by an increase at a future instance) • High irregularity in the response pattern • i.e., Anti-Persistent behavior • (an increase in net score at one instance is more likely to be followed by a decrease at a future instance)

  10. Random Walk • A student with fractal dimension 1.32

  11. Random Walk • A student with fractal dimension 1.81

  12. Random Walk • A student with fractal dimension 1.60

  13. A Tale of Two Students • Both students have the same net score

  14. Long runs of smoothness followed by long runs of irregularity, then a tipping point (onset). • Short runs of smoothness followed by short runs of irregularity. • The Struggling Student can be Identified • alert • onset • Responses Submitted • Responses Submitted • start of course • mid-course • start of course • mid-course

  15. Towards Predictive Analytics

  16. Spring 2013 Pilot — 1300 students

  17. Alerts identify previously invisible • at-risk students • Despite high homework scores (86-95%), students that were identified as high alert, most often got a final course grade of C. • ➔Algorithm identifies what was a previously invisible population of at-risk students.

  18. Fall 2013 Pilot - 1500 students, 8 schools • 73% of instructors agreed alerts were accurate • Instructors intervened with students whose grades were average, but who had high alert levels — expanding office hours & reviews; adding Adaptive Follow-up assignments • Students reported being motivated by the EA notification to attend office hours and reviews

  19. A Fractal Based Decision Engine for Intervention • Patent Awarded: January 2014 • Inventors: • Dr. William Galen • Dr. Rasil Warnakulasooriya

  20. Towards a Two Sigma Solution

  21. Next Steps [Or just skip to the next slide…]

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