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Interpretation of data

Interpretation of data

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Interpretation of data

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  1. Interpretation of data The Ferrers Specialist Arts College NQT and New Staff Training

  2. The family behind Research Machines (RM) A registered charity now working with the DCSF on data analysis Developed a range of analyses to support the processes of self-evaluation and target-setting. Use a database which now contains performance information on over 10 million pupils in England and Wales. Provide analyses and data which help LEAs and schools to make more effective use of pupil performance data. What is the Fischer Family Trust?

  3. Sharing the FFT ethos? • Data provides questions not answers • Data analysis should be used by schools to promote discussion, evaluation and planning • Analyses are available for different groups of pupils, and a range of indicators, to help identify strengths or areas for development/intervention • Use the past to inform the future

  4. ABCD? - FFT A Pupil-level prior attainment data • ·Overall Key Stage Score using BOTH Teacher Assessment and Test Data (‘fine graded’ level derived from marks) • Take into account variations in attainment between different subjects (E, M, S) – (not just ‘subject to subject’ because attainment in English can, for example, affect progress in mathematics) • Gender as a pupil-level indicator • Month of birth as a pupil-level indicator Pupil Prior Attainment Month of birth Gender

  5. ABCD? - FFT B-D • SOCIO-ECONOMIC models • % FSM entitlement, geodemographic data based on aggregated pupil postcodes and Mean Intake of year group • Types B, C and D Month of birth School context Gender Pupil Prior Attainment

  6. ABCD? Therefore, the 4 estimate types are as follows: • A PA based upon pupil prior attainment, gender and month of birth • B SE based upon pupil prior attainment, gender, month of birth and school context • C SE Uses Type B and then take into account the improvement needed for national or locally negotiated local authority targets • D SE Uses Type B but adjusted to be consistent with the ‘top 25th percentile’ schools (based on value added)

  7. FFTD • The Gold Standard? • Top 25% schools nationally • Ideal aim as part of high expectations for The Ferrers • FFTD data is what we have on the system • KS3 under FFT folder in staff shared area • KS4 also available here – even easier under EXAM DATA folder

  8. Using FFT to support pupils in school • The Fischer data supports prior attainment data by estimating future performance based on a number of characteristics. • It does not replace the wealth of performance data available to us, but utilises prior attainment to ‘feed-forward’.   • One approach • The Big Picture • The School Context • Individual pupils

  9. The Big Picture • We have access to LA estimates and the LA estimates vs actuals • LA estimates can provide an overview  based on prior attainment. Always remember that the estimates are based on the ‘feed-forward' effect from one Key Stage to the next. Low performance at one Key Stage may result in low estimates at the next. • LA estimates vs. actuals can provide an overview comparing the estimates with the final results. This will allow analysis of previous performance and show VALUE ADDED • Remember that light blue areas show significant high achievement, dark blue show the opposite • What impressions can I develop in relation to the targets set nationally?

  10. Things to consider – Local Authority • What were the areas of concern when the estimates and actual results were compared? • Are there any local and national strategies in place for this Key Stage? • What were the positive messages from the estimates and actuals? • How do we compare? • What intervention strategies lead to these successes? • What future intervention is needed?

  11. The school context - estimates

  12. School context – new DCSF groups

  13. Things to consider – whole school • What are the estimates suggesting, based on my knowledge of my school? • How do they compare with school targets? • Are there differences between estimates and targets set? Why? • Are there gender differences in performance for any particular areas? • What is being done to address these? • Why were there differences between teacher assessments and estimates?  • Were the teacher assessments lower than the outcomes for groups of pupils - if so why? • Did any groups of pupils perform less well than others - if so why? • Does it raise any further questions?

  14. Individual pupils - estimates

  15. Individual pupils – who are we concerned about? • List the pupils who you feel are vulnerable to making the expected progress, without referring to the estimates • what informed your choice of pupils? • are there any reasons for the pupils you have chosen? • what intervention is currently taking place for these pupils? • who else is involved with this group? • Look at the estimates for each of these pupils • what are the range of estimates? • are there any pupils with an estimate higher than 75%? • why are these pupils on your list? • if the estimate is low for one subject - e.g. English - what intervention is taking place ?

  16. Individual pupils – less and more able? • Look at the estimates for pupils below 40% and add them to the list if they are not already present • why were these pupils not in your original group? • are there any patterns within this additional group? • are these additional pupils involved in any form of intervention? • is the class teacher aware of any difficulties with any of the individuals? • Look at the estimates of pupils above 90% in both level sets • are these pupils challenged? • what strategies worked with these pupils that may help with other groups?

  17. Using a pupil progress self evaluation • Use the pupil estimates handout to note key points about • Patterns • Those ‘at risk’ • Intervention • Collaboration

  18. What’s next? • Trial the identification exercise in your year or curriculum area • Use information currently on SIMs • Pester for FFT reports in your faculty