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Data for Target Setting and Monitoring

Peter Hendry: CEM Consultant. Data for Target Setting and Monitoring. Course: Using CEM data in Practice Day 2 Session 3 Wed 30 th May 2012. Peter.Hendry@cem.dur.ac.uk. Data for Target Setting and Monitoring. What type of predictive data should be used to set the targets?

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Data for Target Setting and Monitoring

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  1. Peter Hendry: CEM Consultant Data for Target Setting and Monitoring Course: Using CEM data in Practice Day 2 Session 3 Wed 30th May 2012 Peter.Hendry@cem.dur.ac.uk

  2. Data for Target Setting and Monitoring What type of predictive data should be used to set the targets? • Points and/or Grades • Nationally standardised baseline • Independent sector standardised baseline (MidYIS only) • Prior value-added (MidYIS, Yellis and Alis) • Chances graphs

  3. Case study 1: setting targets. • Uses valid and reliable data e.g chances graphs • Involves sharing data with the students • Gives ownership of the learning to the student • Enables a shared responsibility between student, parent(s)/guardian, and the teacher • Encourages professional judgement • Leads to the teachers working smarter and not harder • Leads to students being challenged and not ‘over supported’, thus becoming independent learners…

  4. CASE STUDY No. 1 Value Added 2009

  5. CASE STUDY No. 1

  6. CASE STUDY No. 1

  7. CASE STUDY No. 1 Prediction/expected grade: 5.4 grade B/C Most likely grade

  8. CASE STUDY No. 1 Prediction/expected grade: 6.2 grade B Most likely grade

  9. CASE STUDY No. 1

  10. CASE STUDY No. 1

  11. Alis predictive data

  12. Alis predictive data

  13. Alis predictive data

  14. Point ‘predictions’ to GCSE (National) Student 1 Student 2 Student 3 Student 4 Student 5 Point ‘predictions’ to GCSE (Independent) Student 1 Student 2 Student 3 Student 4 Student 5 • Compare the ‘predictions’ for National and Independent sector. • What pattern do you notice?

  15. Student 4 Prediction/expected grade: 5.0 grade C

  16. INDEPENDENT SECTOR Student 4 Prediction: 5.8 grade B

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