1. Widening Participation in Higher Education: A Quantitative Analysis Institute of Education Institute for Fiscal Studies Centre for Economic Performance

2. Aims To provide a theoretically based analysis of HE participation for different types of student –the factors influencing the likelihood of entering higher education –the quality and nature of the higher education experienced by different types of student –the determinants of and barriers to progression in HE.

3. Key Objective To identify at what stage in the lifecourse, and for which groups of students, interventions to widen participation in higher education might be best focussed.

4. Participation in HE at age 18 by A- level score and parents’ SEG

5. Methods – Integrated data set School Pupil Level Annual School Census: ALL PUPILS in State schools in England in Year 11 in 2001/02 School Attainment records: Key Stages 2, 3, 4 and 5 Further Education Individual Learner Records National Information System for Vocational Qualifications Higher Education UCAS records: all applicants to HE HESA records: all attendants in HE [Student Loan Book records]

6. Summary of progress Major task is to collect and merge the necessary data We are well advanced in this and are working closely with the DfES and HEFCE Analysis of the main data set is underway Some results for the project are available –Work on teacher expectations

7. Methods – integrated dataset Work ongoing on school and FE data –Data work Construction of post-16 participation variables Attainment post-16 Background characteristics –Modelling: Decision to remain in post-compulsory education Attainment post-16

8. Methods – integrated dataset Construction of post-16 participation variables –School: attendance in Y12 or Y13 PLASC –FE: presence of ILR record Gives crude measure of participation BUT: –ILR and PLASC have very different structures so measure is not consistent across different forms of post-16 participation –Transition from state to private => counted as non- participation! –Cannot YET distinguish full-time vs. part-time FE

9. Methods – integrated dataset Attainment post-16 –Work in progress… –Need consistent measure across vocational and non-vocational qualifications –DfES constructed data contains derived measures of government targets (e.g. L2 attainment by 19) but not very detailed scores

10. Methods – integrated dataset Individual background characteristics –Gender, ethnicity, FSM and EAL status Previous attainment –Results at Key Stages 2 and 3 School characteristics –Ethnic, FSM and EAL composition; and ranking at GCSE level EMA availability

11. Preliminary findings – integrated dataset At 16At 17 % participating81.272.2 - School27.521.1 - FE38.334.9 - WBL7.19.2 - Some combination thereof8.37.0 Sample size548,749 These match official DfES post-16 statistics quite closely

12. Post-16 participation (1)

13. Post-16 participation (2)

14. Ongoing work HE records to be added Model sequence of decisions Sub-group analysis

15. The role of teacher and pupil expectations in students' HE decisions –Can teachers' perceptions about the student's ability explain inequalities in HE participation? –Can students’ perceptions about their own abilities explain inequalities in HE participation?

16. Steve Gibbons and Arnaud Chevalier Teacher expectations and pupil attainment

17. Motivation and questions How important are teacher expectations in influencing pupils’ education decisions and outcomes? How well do teacher expectations reflect actual student achievement? Do expectations differ across demographic groups?

18. Methods and data: teacher expectations Quantitative (regression) approach based on differences between Teacher Assessment of pupil attainment level at Ks3, and actual attainment Investigates Teacher Assessment at age-14 relative to what we would expect of pupils given past, current and future attainment PLASC/NPD data: around 1.1.million pupils without special needs in non-special schools. Cohorts in year 11 in 2001/2, 2002/3 and 2003/4 Staying-on based on attendance in year 12. No other post- compulsory schooling data yet

19. Teacher Expectations Teachers tend to under estimate the educational potential of certain groups of students, across a range of subjects

20. Teacher Assessment of pupils at age 14 (English). Demographic groups relative to white British girls, not on free meals. Estimates control for Ks3 level actually attained; outline-only non-significant

21. Teacher Assessment of pupils at age 14 (Science). Demographic groups relative to white British girls, not on free meals. Estimates control for Ks3 level actually attained; outline-only non-significant

22. Teacher Assessment of pupils at age 14 (Maths). Demographic groups relative to white British girls, not on free meals. Estimates control for Ks3 level actually attained; outline-only non-significant

23. Teacher Assessment of pupils at age-14 (English). Estimates conditional on all past and current attainment Controls are Ks2 & Ks3 levels and test scores, Teacher Assessment at Key Stage 2; outline-only non-significant

24. Teacher Assessment of pupils at age-14 (Science). Estimates conditional on all past, current attainment Controls are Ks2 & Ks3 levels and test scores, Teacher Assessment at Key Stage 2, GCSE points (all subjects); outline-only non-significant

25. Teacher Assessment of pupils at age-14 (Maths). Estimates conditional on past and current attainment Controls are Ks2 & Ks3 levels and test scores, Teacher Assessment at Key Stage 2; outline-only non-significant

26. Summary For some demographic groups, teachers’ assessment of age-14 attainment differs systematically from what we would expect based on based on past assessment, past test scores and current test scores Teacher assessments are systematically below test score- based assessments: –For those on free meals –For boys, in English and Maths

27. Summary Picture more mixed for ethnic groups –Teacher assessments are systematically above test score- based assessments for Chinese pupils Discrepancies are fairly small: –e.g. average under-assessment in English for FSM-boys is only about 0.9 KS3 points (< 1 term’s progress)

28. Do teacher expectations matter? Do teacher expectations influence pupil decisions and outcomes?

29. GCSE points percentile Probability of staying on to year 12 Stay on to year 12, conditional on GCSE points Teacher Assessment English (points) +0.47+0.37%+0.10% Teacher Assessment Science (points) +0.32+0.28%+0.09% Teacher Assessment Maths (points) +0.38+0.32%+0.11% Association between Teacher Assessment at age-14 and age-16 outcomes, conditional on test scores and prior assessment Estimates control for pupil characteristics, Ks2 and Ks3 levels and test scores, teacher assessment at Key Stage 2. Last column controls for GCSE points. All significant at <0.1%

30. Summary Low teacher expectations translate into lower pupil GCSE point scores and lower staying on rates (at school) –A pupil assessed 1 level down (6 points) in English Maths and Science can expect to be 7 percentiles down in GCSE points –And around 1.8 percentage points less likely to stay on at school (on a base of 20% for boys on FSM)

31. Summary Two competing hypotheses: 1. Teacher under-assessment at age-14 is rational and based on information not available to us –e,g. past teacher assessment, and actual past and current attainment are worse predictors of the ability of boys on FSM than of girls not on FSM –difficult to see why different demographic groups should differ in this way 2. Expectations of attainment of these groups are lower than they should be, and become self-fulfilling