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Self-Evaluation and School Improvement Using FFT Live

Self-Evaluation and School Improvement Using FFT Live. Mike Treadaway Director of Research Fischer Family Trust. Contents. FFT Live – Key Analyses. Secondary. FFT Live – Key Reports – Value Added. FFT Live – Key Reports - Estimates. FFT Live – Developments in 2010.

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Self-Evaluation and School Improvement Using FFT Live

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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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