1. Data for Target Setting and Monitoring Course: Using CEM data in Practice Day 2 Session 3 Wed 30 th May 2012 Peter Hendry: CEM Consultant 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. Value Added 2009 CASE STUDY No. 1

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

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

11. Alis predictive data

13. SurnameForenameAv GCSE Prediction Closest Grade Prior Value Added Prediction Adjusted PredictionGrade Final ‘target’ 1 7.7127.1A*A145.8140A* 2 6.198.8B117.6 AB 3 697.1B115.8 AA 4 6.9113A/B131.7 A*/AA* 5 6.9113A/B131.7 A*/AB 6 5.588.2C107 A/BA 7 7.5123.6A142.3140A* 8 6.6107.7A/B126.4 A*/AA* SubjectScorePredictionClosest GradeAdj PredictionAdj Closest Grade (A2) Business Studies: Single6.191.6B/C95B (A2) Art and Design6.198.8B117.6A (A2) History Of Art6.195.4B B (A2) Geography6.189.2B/C90.5B/C Alis predictive data

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

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

16. Prediction: 5.8 grade B INDEPENDENT SECTOR Student 4