Knowing every student, K nowing their potential

1. Knowing every student, Knowing their potential

3. Why should we use data in our work

5. Without data, you are just another person with an opinion Andreas Schleicher. OECD, Head of Indicators and Analysis Division Winning is a game of inches. Humphrey Walters

6. Performance Comparisons

7. Performance Comparisons

8. Performance Comparisons

9. First Major Principle of Fair Evaluation What goes in affects what comes out

10. Performance Comparisons

11. Performance Comparisons

12. Second Major Principle of Fair Evaluation Essential to compare like with like

13. Lies, damn lies and statistics – Mark Twain He uses statistics as a drunken man uses lampposts - for support rather than illumination. (Andrew Lang) Statistics are no substitute for judgment. (Henry Clay)

14. Data Availability and Data Literacy Data Literacy High Low Data Availability

15. Data Literacy ON YOUR TABLES, DISCUSS: DO YOU HAVE Sufficient data: to enable the key questions and factors to be explored Sufficient access: to systems which enable key elements of data to be linked Sufficient experience and understanding: to find the smallest amount of data needed - and how best to present it Sufficient embedding: such that individuals have an appropriate view about the reliability of data Sufficient confidence: to be able to justify why we are NOT doing something as well as the things we have decided to do Sufficient humility: to enable our own assumptions to be challenged

16. Terminology Calculating what you would expect a group of pupils to achieve, based upon the progress of similar pupils last year, is ? A A target An estimate C B A guess Daft D

17. Terminology Calculating what you would expect a group of pupils to achieve, based upon the progress of similar pupils last year, is ? An estimate C

18. Past knowledge = estimate

20. Using Estimates with Students Your target grade is … I thought I could do better How do they expect me to achieve that? I can get that easily I’ll show them!

21. Using Estimates with Students If you make average progress, you might get a… Let’s look at the range of grades achieved by similar students last year …. …. what will you aim to achieve? Interesting .. Maybe I could do that …If one in five did that last year…?

22. What factors impact upon pupil achievement

23. What factors impact upon pupil achievement

24. UK KS2 to KS4 CVA

25. Simple Value added Achievement Time

26. In the UK, we take 589,000 pupils and look at the average of what happened Better than average = Positive Value Added Achievement Lower than average = Negative Value Added Time KS2 APS KS4 APS

27. Different models = different estimates Attainment Time Different characteristics are used in complex mathematical models to create estimates based on a number of characteristics... Different estimates are created.

28. Differences If two assessments are different One might be wrong They might BOTH be wrong They might be assessing different things

29. Triangulation Analysis B Analysis A Teachers Professional Judgement Basis for action Investigate Further Challenge Assumptions Check Accuracy

30. UK GCSE outcomes at age 16

31. What would/could this look like for Nashville? What would the output variable be? What would the input variable be?

32. What would/could this look like for Nashville?

39. What might you do to exceed average?

41. The only judgements that can be made… Statistically Below Statistically Average Statistically Above UCI UCI UCI LCI LCI LCI Mainstream secondary schools ranked Always check if the confidence intervals cross the magical 1000 median?