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Enhancing School Improvement Through Self-Evaluation Using FFT Live Insights

Explore the transformative power of FFT Live in self-evaluation for schools. Led by Mike Treadaway, Director of Research at Fischer Family Trust, this comprehensive overview delves into key analyses and value-added reports that inform school improvement strategies. Discover essential methodologies for calculating grade estimates, utilizing ordinal regression while addressing accuracy and fairness in evaluating diverse teaching groups. Understand the implications of these insights on students' performance across subjects and how data-driven decisions can lead to significant educational advancements.

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Enhancing School Improvement Through Self-Evaluation Using FFT Live Insights

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  1. Self-Evaluation and School ImprovementUsing FFT Live Mike Treadaway Director of Research Fischer Family Trust

  2. Contents

  3. FFT Live – Key Analyses Secondary

  4. FFT Live – Key Reports – Value Added

  5. FFT Live – Key Reports - Estimates

  6. FFT Live – Developments in 2010

  7. KS4 (and KS5) Subject VA and Estimates Value Added To Single Grade or Not to Single Grade? Estimates

  8. Single Grade Estimates: Database • Single Grade estimate in the database • Calculated for subject groups

  9. Single Grade Estimates: Database

  10. FFTLive • FFTLive introduced a full range of probabilities

  11. FFTLive

  12. FFTLive – Single Grade • Highlight the highest probability

  13. Narrowing to the Middle • Let’s imagine a class of 10 pupils with exactly the same estimates to Billy Onion • Highlighted grade exported to the school MIS • The subject teacher sees.....

  14. Narrowing to the Middle Estimates should be averaged across the group

  15. KS4 Development Report

  16. Est-1 : Explaining the mystery

  17. Est-1 : Pros and Cons

  18. Adding up highest probability grades Too few A*/A and F/G grades

  19. Calculating Points (from ordinal regression) Issues – Accuracy and Fairness Particularly if used in context of evaluating progress of different teaching groups

  20. Analysing Subject VA • A common approach is to use ordinal regression • Issues with this are: • Fails (U grades) • Linearity • Granularity • Intervals

  21. Fails and Linearity • KS2 Average English Level • Similar pattern for over 80% of GCSE subjects at KS4 • Using linear regression introduces significant errors for A*, A and G grades

  22. Granularity • Regression Analysis (OLS) works well where inputs AND outputs are on a continuous scale • Inputs are OK (fine grades) • Outputs are not – they are in clusters (grades) • If we had e.g. UMS points … but we don’t! Mathematics (random sample of 1000 records) KS2 -> KS3 KS2 -> KS4

  23. Intervals Is the difference between A*/A, C/D and F/G grades the same? Yes No Not sure, but probably no Yes 60% <10% 30% Responses are what we find whenever we ask subject leaders this question We can debate whether or not their ‘gut feelings’ are justified If, though, we can find a method of analysis which doesn’t care whether or not grade intervals are equal………

  24. Solution Nominal Regression Outputs are chances not estimated points

  25. Likely Developments : Grades for Estimates

  26. Estimates - Example

  27. Estimates - TM

  28. Estimates - TQ

  29. Estimates - HTM

  30. Exports

  31. Issue 7 : Specific Subjects and Subject Types at KS4

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