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Leading Intervention 1

Leading Intervention 1. 17 th September 2009. CPD overview. LI1 17 th September 9-12 Finstall Role of intervention leader Sources and types of data twilight1 13 th October 4-6 Finstall Identifying pupils for intervention LI2 17 th November 9-4 Finstall

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Leading Intervention 1

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  1. Leading Intervention 1 17thSeptember 2009

  2. CPD overview LI1 17th September 9-12 Finstall • Role of intervention leader • Sources and types of data twilight1 13thOctober 4-6 Finstall • Identifying pupils for intervention LI2 17th November 9-4 Finstall • Tracking and spreadsheets • Looking at data in depth • Intervention models and approaches twilight2 8thDecember 4-6 Finstall • Resources for intervention

  3. CPD overview LI3 12th January 9-12 Pitmaston • Proactive rather than reactive intervention • Quality first teaching • Effective use of TA’s breakfast 11th February 8-10 WRFC, Warriors Centre • Behaviour and attendance as part of intervention LI4 21st June 9-12 Pitmaston • Evaluating the year • Planning ahead

  4. Role of Intervention Leader Track pupil progress Be a source of data and analysis, and advice Coordinate resources for staff and pupils Have an overview of intervention Monitor the impact Liaise with subject leaders, with pastoral leaders, with Strategy Manager, with school Data Manager etc

  5. Aims of the morning Be familiar with sources and types of data School data FFT RAISEonline Begin to identify pupils for intervention Begin to effectively analyse data

  6. Using data keymessages • Data collection does not in itself solve anything • Data provides questions not answers • Data analysis should be used to promote discussion, evaluation and planning • Analyses for different groups of pupils, and a range of indicators, help identify strengths or areas for development/intervention • Use the past to inform the future

  7. What’s the point? Historical data • Assessments at key points • Ongoing assessments Targets • Where should they be in the future? (externally set, internally set, adjusting for pupil progress) • Interim targets Monitoring • Comparing progress with targets (individuals and overall) and reacting Summaries • Comparisons of overall estimates/targets with actual results

  8. Sources of data & targets Projects Formal Teacher Assessments Mocks Traffic lighting School attendance Behaviour Raiseonline Expected progress rates Homeworks What are you collecting data for? What data do you need? Prioritise! ? FFT Tests Rewards and sanctions Ongoing Teacher Assessments SATs Exams School historical data Pupil self- assessments Lesson attendance

  9. Data calendar What data is collected centrally by the school? What data is collected by subject/pastoral teams? What decisions are made as a whole school? What decisions are made within subject/pastoral teams? Does data inform decisions? Are decisions based on data? Could the process be improved?

  10. Fischer Family Trust Aims to help schools make effective use of test and TA data Database of all matched pupils Includes past test and TA data Includes estimates of future attainment based on national progress patterns

  11. Using Prior Attainment and School Context Using Prior Attainment as an indicator of future performance, we know: • KS3/4 attainment is highly dependent on prior attainment • Girls make different progress to boys • Autumn born pupils have higher attainment than Summer born pupils • Pupils’ prior attainment in English often has a greater impact on subsequent progress than attainment in Maths or Science Taking account of School Context, we know: • Pupils from deprived backgrounds tend to make less progress (geodemographic data) • The spread of prior attainment for the cohort can have an impact on estimates of future achievement

  12. Factors

  13. Value Added Models 3 value-added models have been developed: Model PA (Prior Attainment) Prior Attainment Gender Month of Birth Model SE (Socio Economic) Prior Attainment Gender Month of Birth School Context Model SX (School EXtended) Prior Attainment Gender Month of Birth School Context Pupil Context

  14. Which estimate type?

  15. FFT Reports – www.fftlive.org

  16. So where do the estimates come from? Take eight ‘similar’ students at the end of Key Stage 2: All 8 students have the same overall prior attainment using an Autumn Package points score.

  17. The chances graph Average points score of 26 to 28 26<= KS2 Average Point Score <=28 Last year, 33% of students with average points score of 26-28 achieved grade C So… estimate of 55% chance of achieving grades A*-C …and estimate of 77% of achieving D+

  18. 26% 10% 4% 36% Gender Marks 48% 12% Subject differences 16% 18% Month of birth FFTfactors: Prior Attainment Model

  19. Probability A* - C Model Used Student Estimates Most likely level Level achieved by top 5% - 25% of similar pupils Prior Attainment Year 6 Test & TA

  20. Example Student Prior Attainment Estimates • Questions: • What would you expect this student to achieve at GCSE? • What targets would you set? • What other information would you need to reach a more informed decision?

  21. Example Student Prior Attainment Estimates Grade B or C

  22. Activity Highlighted sheet (Pupil Estimates Type D) • Estimated grades highlighted if close to boundary • High percentage chances in yellow • Low percentage chances in pink • Rest in blue Without additional action, how many A* - C would you expect? Which students would you target for intervention? What other questions would you want answered? What action would you take next?

  23. School Estimates CHANGE • Matched students only • A, B, D • Box: 3 year trend for this school • LA guidance is to use Type D to build in some challenge • Includes E, M 2 levels progress • Targets may be set above these • Also: Breakdown by gender, upper/ middle/lower, etc Range of Estimates

  24. Sowhere do these estimates come from? How many A* - C would you expect from this list? For the FFT school estimate: Add the percentages, divide by 100 26+10+4+36+48+12+16+18+24+6 = 200 200 ÷ 100 = 2 So A* - C estimate is 2 students out of the 10 What are the implications for intervention?

  25. Activity Highlighted sheet again What are the A* - C estimates for the sheet, using the % chances? What are the implications for intervention?

  26. Accessing the FFT data Website (www.fftlive.org), password from Data Manager • Updated automatically with validated data • ‘old’ estimates will be overwritten Know the school policy on the versions: • Which version are the estimates taken from? • Are they fixed for the year or key stage, or are they flexible?

  27. FFT Key messages Use the individual pupil estimates Use the school estimates Be aware of both when identifying pupils for intervention Use the ‘actuals’ reports to review the success of intervention and inform future action

  28. Raiseonline Match the cards How many can you match in 5 minutes? STOP!

  29. Estimates/Targets National expectations are set by DCSF Estimate is based on statistical evidence An estimate may be a likely outcome for a typical school, or a likely outcome for a school performing in the top 25% Prediction is based on past performance + professional knowledge of a pupil Target is based on prediction and builds in aspiration

  30. Raiseonline www.raiseonline.org Based on each pupil’s prior attainment Compares top 50% and 25% of schools Shows estimates (as targets) for pupils, groups, cohorts Allows moderation of the suggested pupil targets

  31. Two sets of pupil data National provided data School’s own data Updated by DCSF Updated/amended by the school Data Manager Used for the full PANDA report, available to Ofsted, SIPs, LA Can be shared with Ofsted, SIPs, LA; sharing requires school action Initially these are identical • Oct/Nov • Spring • July • Updates overwrite any school amendments • Amended pupil results • School defined pupil attributes and teaching groups • Optional test data • Question level data • Moderated pupil targets

  32. RAISEonline

  33. view all analyses The Report Wizard

  34. Click this link to save any report you find useful Change the file name if you wish, then Save Reports with grouping • Use the drop-down boxes to change • graph • year • subject • gender • other groupings

  35. Interactive VA graphs Click on data points to identify groups or pupils

  36. RAISEonline Key messages Use the individual pupil results Use the school results Compare with the national picture Use these to reflect on the success of previous intervention and hence to inform future action

  37. FFT v RAISEonline both use historic national pupil data use social context data provide summaries of attainment creates estimates of future attainment summaries largely at school level FFT RAISEonline database includes FFT estimates summaries include national data for comparison allows question-level analysis

  38. So… Use FFT pupil estimates, FFT school estimates to aid selection of pupils for intervention Use RAISEonline pupil & school tables & graphs to learn lessons from the past to inform future intervention Use school data and teacher knowledge to refine selection of pupils & choice of intervention package

  39. National Expectations Sets out DCSF expectations: KS1 to KS2 all L2 + 45% of L1  L4 All to make at least 1 level of progress All should make at least 2 levels of progress Reporting: % achieving L4+ in both Eng and Ma % making 2 levels of progress in Eng % making 2 levels of progress in Ma

  40. National Expectations Sets out DCSF expectations: KS2 to KS3 all L4 + 50% of L3  L5 (and increasing majority  L6) all L5 (in both Eng and Ma)  L6 (and increasing majority  L7) All to make at least 1 level of progress Reporting: % achieving L5+ in both Eng and Ma, and % L5+ in Sci % making 2 levels of progress in Eng % making 2 levels of progress in Ma

  41. National Expectations Sets out DCSF expectations: KS3 to KS4 30% of average L5 + all L6  5 A*-C (incl Eng and Ma) All L6 (in Eng and Ma) make 2 levels of progress in both Increasing majority of L5 in Eng and Ma make 2 levels of progress in both. Reporting: % of 5A*-C including Eng and Ma % making 2 levels of progress in Eng % making 2 levels of progress in Ma

  42. Setting targets Estimates should be used to SUPPORT planning and target setting: • FFT reports offer estimates not targets • Estimates help us to set targets • A teacher’s professional knowledge of the pupil is vital in target setting • Targets are not predictions • Targets should be aspirational • Targets need not be fixed • School policy may impact on target-setting

  43. So… Use the individual student estimates Use the subject estimates Use your department’s knowledge of the students Create moderated targets Use these when identifying students for intervention Use the ‘actuals’ reports to review the success of intervention and inform future action

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