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 Tracking and spreadsheets Looking at data in depth Intervention models and approaches twilight2 8 th December 4-6 Finstall Resources for intervention

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

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

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

Using data key messages 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

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

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

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

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

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

Factors Pupil FactorsPASESX Mean Test Level (Fine Grade) Mean TA Level Subject Variations Gender Month of Birth EAL FSM SEN Stage, Statemented Ethnicity Mobility (joined late / time in school) School FactorsPASESX Mean Intake Test Level Spread of Intake Test Level FSM Entitlement (Percentile Rank) Geodemographic Data (Percentile Rank)

Value Added Models Model PA ( P rior A ttainment) Model SE ( S ocio E conomic) 3 value-added models have been developed: Model SX ( S chool E X tended) Prior Attainment Gender Month of Birth Gender School Context Prior Attainment Month of Birth Gender School Context Prior Attainment Month of Birth Pupil Context

Which estimate type? AdvantagesDisadvantages Type A (PA) Similar pupils Prior Attainment is a major factor upon future performance Doesn’t take account of context. Doesn’t stretch pupils in advantaged schools Type B (SE) Similar pupils in similar schools Is a more accurate reflection of what actually happens No element of challenge Type D (SE + Challenge) Similar pupils in similar schools Stretches pupils in schools with high value-added May still be too low for schools in the top few % nationally for value- added

FFT Reports –

So where do the estimates come from? EnglishMathsScience Average Points Score Rebecca Mango44427 Jack Tomato44427 Tim Cumin44427 David Apricot44427 Abigail Garlic44427 Jane Apple35427 Edward Onion44427 Liam Peppercorn44427 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.

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

FFT factors: Prior Attainment Model Difference between students KS2 AttainmentA* -C Estimate EnMaScPts ScorePA Model Rebecca Mango % Jack Tomato % Tim Cumin % David Apricot % Abigail Garlic % Jane Apple % Edward Onion % Liam Peppercorn % Gender 26% 10% Month of birth 16% 18% Marks 4% 36% Subject differences 48% 12%

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

Example Student NC SS Y6 Y6 Test LevelsY6 Teacher Assessment EngMaSciEngMaSci % chance of achieving KS4 Grade% chance GFEDCBAA*A*-CPass 1% 2%10%31%36%17%3%87%99% 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?

Grade B or C Example Student NC SS Y6 Y6 Test LevelsY6 Teacher Assessment EngMaSciEngMaSci % chance of achieving KS4 Grade% chance GFEDCBAA*A*-CPass 1% 2%10%31%36%17%3%87%99% Prior Attainment Estimates

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?

School Estimates CHANGE Range of Estimates 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

So where do these estimates come from? Difference between pupils KS3 AttainmentA* - C Estimate EnMaScPts ScorePA Model Rebecca Mango Gender % 26% Jack Tomato % 10% Tim Cumin Marks % 4% David Apricot % 36% Abigail Garlic Subject differences % 48% Jane Apple % 12% Edward Onion Month of birth % 16% Liam Peppercorn % 18% Kim Bolton 24% Basil Don 6% How many A* - C would you expect from this list? For the FFT school estimate: Add the percentages, divide by = ÷ 100 = 2 So A* - C estimate is 2 students out of the 10 What are the implications for intervention?

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

Accessing the FFT data Website ( password from Data Managerwww.fftlive.org 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?

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

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

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

Raiseonline 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

Two sets of pupil data Initially these are identical National provided data School’s own data 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 Updated/amended by the school Data Manager Updated by DCSF Can be shared with Ofsted, SIPs, LA; sharing requires school action Used for the full PANDA report, available to Ofsted, SIPs, LA

RAISEonline

The Report Wizard view all analyses

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

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

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

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

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

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

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

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

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

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