Leveraging Baseline Test Data for Effective Teaching and Learning

1. Peter Hendry: CEM Consultant Interpreting Feedback from Baseline Tests - Whole School & Individual Student Data Course: CEM Information Systems for Beginners and New Users Day 1 Session 2 Wednesday 17th October 2012 Peter.Hendry@cem.dur.ac.uk

2. To inform professional judgement • To start a conversation

3. USING BASELINE DATA FOR TEACHING AND LEARNING IF FEEDBACK DATA IS TO BE TRUSTED THEN PROBABLY: • The students understood the purpose of the assessments • And each student did their best at the time……. • As a year group, how able are they? • What strengths and weaknesses does the group have? • How did each student perform?

4. Bands, percentiles, standardised scores… 30 10 5 1 40 90 20 80 70 99 60 50 95 C B A D Standardised scores Percentiles: 25 75

5. National Quartile Ability Bands

6. Alis: year 12 students, two baseline profiles for the same school Nationally, 25% in each band Comments and potential implications?

8. Band Profile Graph: all MidYIS cohort A school with a ‘completely average’ intake would have 25% of pupils within each band. Checking this graph each year will give you an immediate overview of your intake. Band D Band A

9. Band D Band A Band D Band A Comments?

11. On another day, with 95% certainty, the score of 100.6(+/- 1.1 x 2) • would not be higher than 100.6 + 2.2=102.8 • would not be lower than 100.6 – 2.2 =98.4 How might this information be useful?

13. Using national baseline test scores to Identify Gifted Pupils • Scores over 130 – top 2% nationally • Scores over 126 – top 5% nationally • Scores over 120 – top 10% nationally • Scores over 110 – top 25% nationally

14. Year 7: the ‘top’ students Perceptual speed and accuracy Proof reading

17. A relatively lower vocabulary score might indicate a difficulty which: • could contribute to under-performance in most, if not all subjects • might lead to ‘stressful situations’ • may lead to further investigation and subsequent pupil support

19. A relatively low maths score might indicate potential weaknesses in subject areas which require: • numerical skills • logical thinking • skills such as sequencing CONVERSELY: a high maths score but a low vocab/reading score…….

21. A relatively high non-verbal score might indicate potential strengths in subject areas which require: • 3d, and 3d into 2d, visualisation • Spatial awareness • Understanding images in 2d representing 3d • Extracting information from visual images • Science, D and T, Art, Geography…… and vice versa

23. A relatively low skills score might indicate potential weaknesses such as: • Speed of processing/working • Potential underperformance in test/examination conditions • Poor written work (SPG etc.) and vice versa

24. Skills: Proof Reading

25. Skills: Perceptual Speed and Accuracy

27. SEN use of MIDYIS test results Analysis of baseline test individual skill profiles to indicate potential areas of learning difficulties e.g. If the two lowest scores are the vocabulary and skills section and are, or close to being, statistically significant (see IPR) • Then the student might be dyslexic....... • Follow-up with appropriate diagnostic tests…..

29. Sharing data with colleagues: e.g. baseline test data

30. INSIGHT Pupil IPR

31. Maths

32. IPRs in KS3 Levels NATIONAL AVERAGE

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