non progression among higher education new entrants: a multivariate analysis
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Non Progression Among Higher Education New Entrants: A Multivariate Analysis. Dr Selina McCoy, ESRI Dr Delma Byrne, NUIM 28 October 2010. Moving to a Value-Added Approach. Crude/overall patterns of non progression - PowerPoint PPT PresentationTRANSCRIPT
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Non Progression Among Higher Education New Entrants:
A Multivariate Analysis
Dr Selina McCoy, ESRI
Dr Delma Byrne, NUIM
28 October 2010
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Moving to a Value-Added Approach
● Crude/overall patterns of non progression● Doesn’t take account of differences in student
intake across institutions and sectors● Need for like-for-like comparison● Reduces risk of creating incentives for greater
student selection● Does not negatively label institutions with more
diverse student intakes● Focus is on institutional effectiveness, taking
account of student intake
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Two main questions:
Which students are most likely to progress?
Taking account of these characteristics, does the average chance of progression vary across institutions?
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Student Characteristics● Gender ● Social Class Background (Father’s Position)● Educational Attainment at Second Level
● Leaving Certificate Points ● Attainment in Mathematics, English and Irish
● Nationality ● Grant Recipient ● Field of Study ● Course Level (Level 6,7 & 8)● Institution
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Information we don’t have
Motivation for enrolling in HE Financial well-being Participation in part-time employment Academic engagement Views on teaching staff, educational experience Attendance, participation in extra-curricular activities Institutional supports for students
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Approach STATA Takes account of clustering – students in the same
institution share common influences, may be more like each other
Overall (unadjusted) differences in progression chances across institutions
Net differences – do the chances of progression vary across institutions
Results presented in odds ratios: chances of group not progressing relative to reference group
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Findings 1: Characteristics of Students Who Do Not Progress/Not Present
– Model 1: Gender, Age, Nationality, Social Class
– Model 2: Leaving Certificate Points and Receipt of Grant
– Model 3: HE Sector
– Model 4: Field of Study, NFQ Level
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Non Progression and Gender
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Model 1: Age, Nationality,Class
Model 2: LC Performance &Grant
Model 3: Sector Model 4: FOS, Level
Male
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Non Progression and Social Class
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Professional Manager Non Manual Skilled Manual UnskilledManual
Class Unknown
Model 1: Gender, Age, Nationality
Ref: semi-skilled manual
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Non Progression and Social Class
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Professional Manager Non Manual Skilled Manual UnskilledManual
Class Unknown
Model 1: Gender, Age, Nationality Model 2: LC Performance & Grant
Ref: semi-skilled manual
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Non Progression and Social Class
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Professional Manager Non Manual Skilled Manual UnskilledManual
Class Unknown
Model 1: Gender, Age, Nationality Model 2: LC Performance & Grant
Model 3: Sector Model 4: FOS, Level
Ref: semi-skilled manual
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Non Progression and LC Performance
0
0.5
1
1.5
2
2.5
3
0-150 155-200 205-250 255-300 305-350 355-400 405-450 455-500 505-550 555-600 Unknown
Model 2: Gender, Age, Nationality, Class, Grant
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Non Progression and LC Performance
0
0.5
1
1.5
2
2.5
3
0-150 155-200 205-250 255-300 305-350 355-400 405-450 455-500 505-550 555-600 Unknown
Model 2: Gender, Age, Nationality, Class, Grant Model 3: Sector Model 4: FOS, Level
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Non Progression and Field of Study
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Education Science, Agri,Vet
ComputerScience
Engineering Construction Healthcare Services Combined &Other
Model 4: Gender, Age, Nationality, Class, LC Performance, Grant, Course Level
Ref: Social Science, Arts, Law
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Non Progression and Sector
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Institute of Technology Other HE College
Ref: University Sector
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2. Non Progression Across Institutions
Model 1 – All Individual (named) HE Institutions (ref: UCC)
Model 2 – Gender, Age, Nationality, Class Model 3 – LC Performance, Grant Model 4 – Field of Study, Course Level
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Non Progression Across Institutions
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Model 1
Ref: UCC
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Non Progression Across Institutions
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Model 1 Model 4: Gender, Age, Nationality, Class, LC Performance, Grant, FOS, Course Level
Ref: UCC
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3. Non Progression Within Institute of Technology Sector
Model 1: All Individual (named) IoTs (ref: Blanchardstown)
Model 2 – Gender, Age, Nationality, Class Model 3 – LC Performance, Grant Model 4 – Field of Study, Course Level
Level 6 & 7
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Non Progression Within the IoT Sector
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Model 1
Ref: Blanchardstown IT
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Non Progression Within the IoT Sector
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Model 1 Model 2: Gender, Age, Nationality, Class
Model 3: LC Performance, Grant Model 4: FOS & Course Level
Ref: Blanchardstown IT
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Non Progression Within the IoT Sector
No real gender differences Some age differences Irish students higher non progression Grant impact Some class difference (skilled manual) LC Performance – impact of low performance, high
performers do better Clear Field of Study patterns
– Computer Science higher, Healthcare lower than Science, Agriculture & Veterinary students
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4. Non Progression Within the University Sector
Model 1: All Individual (named) Universities (ref: UCC)
Model 2 – Gender, Age, Nationality, Class Model 3 – LC Performance, Grant Model 4 – Field of Study
All Level 8
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Non Progression Within the University Sector
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
DCU UCD NUIG UL NUIM TCD
Model 1 Model 2: Gender, Age, Nationality, Class Model 3: LC Performance, Grant Model 4: FOS
Ref: UCC
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Non Progression Within University Sector
No gender differences No age differences No nationality difference No grant difference Some class difference (managerial v non manual ) Clear LC Performance patterns Clear Field of Study patterns
– Computer Science higher, Education & Healthcare lower than social science, law & arts students
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SummaryWhich students fare well?
– LC Performance strong predictor of progression– Maths performance particularly strong influence– Importance of grant support, particularly for IOT
students– Strong disparities across subject areas and fields
– No gender differences– Delayed entry not significant– Class differences are small, and largely operate
through LC performance
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Summary
Do the average chances of progression vary across institutions?– Wide raw differences shrink dramatically when
taking account of student intake– Dangers of crude league tables– Main differences between sectors – Colleges of
Education and NCAD doing very well
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Summary
Need to unpack the processes underlying institutional effectiveness: what can institutions do?– Pre-entry guidance, informed choices– Academic supports in 1st year– Attention to particular FOS– Financial wellbeing– At risk students– Broader student engagement