1. “ I often say that what gets measured, gets done. Margaret Spellings david.moran@tribalgroup.com www.portal4isp.com ” 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 S-Rate A91% B73% C82% D55% E5% F99.20%

7. Performance Comparisons S-RateHospital Type A91%Orthopaedic B73%Accident and Emergency C82%General Surgery D55%Coronary Unit E5%Hospice F99.20%

8. Performance Comparisons S-RateHospital Type A91%Orthopaedic B73%Accident and Emergency C82%General Surgery D55%Coronary Unit E5%Hospice F99.20%Maternity

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

10. Performance Comparisons S-RateHospital Type A91%Orthopaedic B73%Accident and Emergency C82%General Surgery D55%Coronary Unit E5%Hospice F99.20%Maternity

11. Performance Comparisons S-RateHospital TypeGood Avg Poor A91%Orthopaedic97% 95% 93% B73%Accident and Emergency C82%General Surgery D55%Coronary Unit48% 43% 38% E5%Hospice F99.20%Maternity

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 IneffectiveEmbedded IgnoredDangerous LowHigh Data Availability Data Literacy High Low

15. Data Literacy ON YOUR TABLES, DISCUSS: DO YOU HAVE 1.Sufficient data: – to enable the key questions and factors to be explored 2.Sufficient access: – to systems which enable key elements of data to be linked 3.Sufficient experience and understanding: – to find the smallest amount of data needed - and how best to present it 4.Sufficient embedding: – such that individuals have an appropriate view about the reliability of data 5.Sufficient confidence: – to be able to justify why we are NOT doing something as well as the things we have decided to do 6.Sufficient humility: – to enable our own assumptions to be challenged

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

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

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’ll show them! I can get that easily

21. 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…? Using Estimates with Students

22. What factors impact upon pupil achievement

24. 16.678 + 0.0054*(KS1 APS squared) + 0.672 *KS1 APS + 0.033*(KS1 reading points - KS1 APS) + 0.271*(KS1 maths points - KS1 APS) + 0.2750 (if in care) - 0.681*IDACI score - 1.528 (if School Action) - 2.437 (if Action Plus or Statemented) - 0.509 (if joined at start of or during Y6) - 0.306 (if joined at start of or during Y5) - 0.227 (if joined at start of or during years 3 or 4) - 0.272 (if female) - 0.626*(age within year where 1 Sept= 1.00, 31 Aug = 0.00) + for EAL pupils only (2.173 + 0.0036*(KS1 APS squared) - 0.1762 *KS1 APS ) + ethnicity coefficient + for FSM pupils only ( - 0.327 + FSM/ethnicity interaction) UK KS2 to KS4 CVA

25. Simple Value added Time Achievement

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

27. Different models = different estimates Time Attainment 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 A Analysis B Teachers Professional Judgement Basis for actionInvestigate Further Check Accuracy Challenge Assumptions

30. UK GCSE outcomes at age 16

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

32. What would/could this look like for Nashville? LowBelowAvgAboveHigh 8%26%57%83%95%

39. What might you do to exceed average?

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