1. Using Alis Predictive Data Dr Robert Clark Alis Project Manager

2. Predictions vs Targets

3. What is an Alis ‘Prediction’ ?  NOT a forecast of the grade the student will get An indication of the grade (points score) achieved on average by students of similar ability in the previous year Targets ? Minimum Targets – Round Alis prediction down ? Realistic Targets – Use nearest Alis grade ? Challenging Targets –75 th percentile ? Prior Value Added ? Arbitrary grade fraction ?

4. 75 th Percentile Predictions Excel spreadsheet - ‘Predictions – Spreadsheet (75 th Percentile)’ If all students attain 75 th percentile predictions, School VA will be at top 25% Approx 1/5 grade per student per subject Can also be generated in PARIS software Prior Value Added Only where prior VA is positive ? 1 year or 3 ? Reasonable to use raw residual figures as this is an approximate measure and raw residuals give grade fractions Can be calculated using PARIS software Data used to inform, not replace, professional judgement

5. Understanding Your Students: Baseline & Predictive Data

6. Intake Profiles

7. Intake Profiles (Historical)

8. IPR... Full Alis 2009 Demo School (999) Banana, Brian ? Studying : Maths Physics Chemistry Biology

9. Prediction Reports Probability of achieving each grade Expected Grade

10. Which predicted grades are the most appropriate for ths student ?

11. Predictions Based on GCSE (7.0) B C B Predictions Based on Test (106) C B D B C What is this Student’s ability ? What Grades should we expect her to get ? If she gets C’s instead of B’s, is this a problem ?

12. Why is the pedicted grade not always equal to the highest bar ? Most likely grade Predicted (‘expected’) grade

13. Subject Report Prediction Reports

14. A2 vs AS predictions and the impact of the A* Grade

17. Worked Examples: Baseline Data & Predictions

18. Refer to the Intake Data on the next 2 slides For each school what deductions might you make ? What implications are there (if any) for teaching & learning ?

19. School A

20. School B

21. Refer to the Y12 data on the next 2 slides. What impact might there be on the pupil’s learning ? What subjects would you be worried about them studying ? Note : Non Verbal section includes Perceptual Speed and Accuracy, Pattern Matching, logical reasoning and dice folding

22. Y12 - Pupil D

23. Y12 – Pupil E

24. Refer to the data on the next 3 slides. Does the data show any ‘warnings’ about future potential achievement? Based only on the information provided, what would be realistic subject targets for the students, and why?

25. Student 1

26. Student 2

27. Student 3

28. Worked Examples: Target Setting

29. Basing Targets on Prior VA – One Methodology from an Alis School Discuss previous value added data with each HoD Start with an agreed REALISTIC representative figure based, if available on previous (3 years ideally) of value added data add to each pupil prediction, and convert to grade (i.e. in-built value added) Discuss with students, using professional judgement and the chances graphs, adjust target grade calculate the department’s target grades from the addition of individual pupil’s targets

32. Discussion Assess the merits and concerns you may have with this value- added model of setting targets

33. Dr Robert Clark Alis Project Manager robert.clark@cem.dur.ac.uk 0191 33 44 193