The document outlines the formal barriers to engineering education at the tertiary level, highlighting challenges such as high school systems, university entry requirements, and socio-economic factors that limit access to engineering programs. It details a three-phase analysis involving surveys, a comparison framework, and a report on the identified barriers, emphasizing the need for structural changes and tailored recommendations to enhance access and encourage diversity in engineering education. The recommendations focus on admission standards, educational structure adjustments, and addressing socio-cultural issues to foster a more inclusive STEM environment.

1. Enhance the Attractiveness of Studies in Science and
Technology
WP 6: Formal Barriers to Engineering
Education
Kevin Kelly
Trinity College Dublin
WP 6 Co-ordinator

2. WP 6: Formal Barriers
to Engineering Education
Aims:
• To examine the formal barriers to engineering
education at third-level
• To facilitate maximally open access to
engineering education without compromising
standards or unfairly exposing ill-equipped
students
2

3. WP6 Key Deliverables
Three phases of work:
1) Survey of education systems in partner countries
2) Comparison Framework
3) Report on formal barriers to engineering higher
education
3

4. WP6 Deliverables
1: Survey of Education Systems
• Extensive questionnaire developed and
distributed to all partners
• Documented national education systems from
primary level to university entry, regarding
provision of science, engineering & technology
(SET) subjects
• Survey data gathered was used to inform other
WP 6 deliverables
4

5. WP6 Deliverables
2: Comparison Framework
• ‘At a glance’ information to compare partner
countries under key headings, relevant to all work
packages
• Provides necessary context to enable conclusions
to be drawn between national structures with
significant variations
• Combination of graphs, tables and textual
information used
5

6. WP6 Deliverables
3: Report on Formal Barriers
The report documents:
• main factors restricting access to engineering
higher education
• research illustrating the impact of these barriers
on access to engineering
• data analysis of the relationship between entry
barriers and subsequent student progression
6

7. Defining the Barriers
What do we mean by barriers?
• Any factors which stand in the way of entry to engineering
programmes
• Can be set by universities, professional bodies, or may
result from structural and/or socio-cultural conditions
Or to put it another way:
How are potential students prevented from studying
engineering?
7

8. Report on Formal Barriers
Main categories identified:
• Entry requirements for engineering courses
• Structures within the school system
– E.g. streams which force students to choose at an early age
between academic or vocational pathways
• Socio-economic factors
– Engineering programmes in several countries appear to be
less diverse in socio-economic terms than other university
programmes
8

9. Barrier Category 1
High School System
9

10. Structure of Education Systems
Full-time compulsory education Part-time compulsory education
Belgiu
m
Pre-school
Finland
Primary education
Ireland
Secondary
education
Italy
Upper secondary
education
Portuga
l
Sweden
0 5 10 15 20
Student Age (years)
10

11. Structural Factors
• Separation between academic and vocational
branches of high school in most countries
• Choice typically made at ~ age 15
• This impacts on options for higher education 
vocational students may not be eligible for
academic study at university
• Subject specialisation creates further restrictions
– students who don’t specialise in science/technology
may be ineligible for engineering programmes
11

12. Comparison Framework
High-school system: proportion of students by type
100%
80%
60%
Other
Vocational
40% Academic
20%
0%
Belgium Finland Ireland Italy Portugal Sweden
12

13. General vs. Specialised
High-School Education
• Many countries operate ‘track-based’
programmes for upper-secondary school
– ‘Tracks’ provide specialised education within a focus
area, e.g. science/technology, humanities, languages
– Students specialise based on interest and/or ability
• Other countries use a more general system where
a broad range of subjects is offered and students
may choose to study a variety of subject areas
13

14. General vs. Specialised High-School
Education
General Track-based
Belgium
Ireland
Italy
Portugal
Finland
Sweden
14

15. Gender and subject specialisation
• Persistent gender differences in uptake of
engineering-relevant subjects, especially Physics
• In Ireland some technology-related subjects are
not taught in all-girls schools
• Research suggests causal relationship between
lower % of girls specialising in Physics, Chemistry
and Mathematics in high school and low numbers
of female students entering engineering
programmes at university
15

16. Pre-university education
How much exposure do students
have to STEM subjects during their
school careers?
16

17. Time spent on Maths
17

18. Time spent on Physics
18

19. Time spent on Chemistry
19

20. Barrier Category 2
University Entry
Requirements
20

21. Is entry to university restricted?
(numerus clausus)
Yes No
Ireland
Belgium
Finland
Portugal
Italy
Sweden
21

22. University Admissions
Does the university control
student selection?
22

23. Yes No
Belgium
Italy
Finland Ireland
Portugal
Sweden
23

24. Overview
Key points:
• No subject requirements set for entry in Italy and
Belgium
– (but all students study a core component of
mathematics and science in high school)
• Mathematics required in all other countries, plus
Physics and Chemistry in most
• Limited students eligible for entry as a result
– max 12% eligible in Sweden and Ireland
24

25. General University Admission Criteria
• Exact criteria vary in each country
• All ATTRACT countries use one, or a
combination, of the following:
– High school certificate exams
– Grades from on-going performance at high
school
– University entrance exams
25

26. Engineering-specific requirements
• Grades in relevant subjects often required
Belgium Finland Ireland Italy Portugal Sweden
Maths No Yes Yes No Yes Yes
Physics No Yes No Yes Yes
10% of
Chemistry No Yes programmes No Yes Yes
require one
of these Required in
Biology No No subjects No No certain
programmes
26

27. High-school students meeting
subject-entry requirements
100%
80%
60%
40%
20%
0%
Belgium Finland Ireland Italy Portugal Sweden
27

28. Do entry requirements serve the intended
purpose?
• Correlation between prior achievement in
subjects required for entry and subsequent
performance in university engineering
programmes (Evidence from IST and TCD)
– in IST students who entered with grades lower than
those now required had significant difficulty in
successfully completing the programme
28

29. Alternatives to Standard
Entry Requirements
• Alternative entry routes exist in most partner
countries
• Function: To facilitate access to university for
non-traditional students & those not meeting
standard entry requirements
• Proportion of students entering via these means
varies from 0% (Italy) to 29% (Sweden)
29

30. Barrier Category 3
Socio-economic Factors
30

31. Socio-economic Diversity in
Engineering
• Engineering courses in many countries are less accessible
to students from lower socio-economic backgrounds
than other fields (Eurostudent IV)
• This is reflected in several ATTRACT countries
– in Ireland, Sweden and Italy engineering programmes tend to be
less socio-economically diverse than other programmes
• In Ireland, PISA mathematics scores were significantly
higher among students designated as ‘high socio-
economic status’ than among others
31

32. PISA – maths, science & GDP
Finland
Ireland
USA
Sweden Belgium
Italy
Portugal
32

33. PISA – top students in maths & science
“the number of students reaching level 5 or 6
in mathematics and science will be
particularly important for countries wishing
to create a pool of workers able to advance
the frontiers of scientific and technological
knowledge in the future and compete in the
global economy”
– OECD PISA 2009 Results Vol. 1
33

34. % of Students at Level 5(6)
in Maths and Science
Country Maths Science
Shanghai 24(26) 20(4)
Finland 16(6) 15(4)
Belgium 14(5) 8(2)
OECD average 9(3) 7(2)
Sweden 8(3) 7(1)
Portugal 7(3) 3(1)
Italy 7(2) 5(1)
Ireland 5(1) 8(1)
34

35. Data Analysis
Student entry data &
progression
35

36. Motivation for Analysis of Student Data
Is there a relationship between the existing
entry requirements (barriers) and the factors
that increase a student’s chance of successful
progression, or are other variables more
significant?
36

37. Details of Analysis
• Data presented on 3 partner universities: PoliTo
(Italy), Trinity College (Ireland) and IST (Portugal)
• Study of TCD and IST examines student
background and prior educational attainment in
relation to university progression
• Data for PoliTo looks specifically at aptitude test
scores at entry to university and credits gained
during first year
37

38. Data Analysis: PoliTo
• Incoming students sit aptitude tests in
mathematics, logic and comprehension
• Results are for information purposes only, not
admission selection
• Strong correlation between results in these tests
and credits gained during first year of study
38

41. Data Analysis: IST
• Can the information that HEIs have on new
students be used to predict academic
performance?
• Study examined 1235 students admitted in
2004/5
41

42. Dimensional Axis of Analysis
Academic
background
Socioeconomic
Motivations status and
expectations family capital
Contextual
Factors
42

43. Axis 1:
Socio-economic status & family capital
• Parental education level not significant
• Household income: below national average
corresponds to 8% increase in academic
performance (typically scholarship students)
• Gender: Female students 10% more likely to
successfully complete studies than male
43

44. Axis 2:
Academic Background
• Significant relationship between grades from high
school and academic achievement at university
(40% performance increase for every 10 points)
• Having studied Physics at high school correlated
with greater academic achievement at university
(72% increase in probability of success)
44

45. Axis 3:
Student Motivation and Expectations
• Students who don’t gain places in their first
choice of programme experience negative impact
on academic achievement (- 16%)
• Students not expecting to achieve a good average
in all subjects attain lower results (- 9%)
• Having chosen the selected programme early
corresponds to a 22% increase in pass rates
45

46. Axis 4:
Contextual Factors
• Residential status: 17% lower academic
performance for students living away from family
home/usual residence
• Travel time to campus found to be significant
– 10% decrease in performance for those travelling over
1 hour in each direction
46

47. Data Analysis: Trinity College
• Similar question as in IST study:
– How does available student data at time of
entry correspond to performance during first
year?
– Can we identify those students less likely to
progress to second year?
• Study examined 1835 students over ten-year
period (2000 – 2009)
47

48. Factors Analysed
• Inputs
– Whether a student took a particular subject (binary)
– Mark achieved in each subject (0-100)
– Degree (one of two available) programme chosen (binary)
– Gender (binary)
– Year (have things changed over 10 year period) (1-10)
– CAO mark (cumulative grade in best 6 subjects) (0-600)
– Living at home (binary)
• Output
– Progressed to 2nd year (Binary)
48

49. Sample/Notional Student
Exam Results: CAO Points
Mathematics – B1 85
Physics – C2 65
Irish (L) – A2 50
English – A2 90
French – C1 70
Chemistry – B3 75
Economics – B1 85
470 CAO Points total – used to
allocate limited places
49

50. Summary of Results
• Past achievement in Mathematics confers single
biggest advantage to students
• Physics and Chemistry are also significant if
student has scored above a certain threshold
• Gender not significant among engineering cohort
(contrasts with university-wide cohort among
which females are significantly more likely than
males to progress)
50

51. Logistic Analysis of Performance
Input Weighted Logistic
Probability
Variables Sum Function
[0 / 1] [- ∞, + ∞] [0 - 1]
[0 - 100]
51

52. Influence of subject grade on probability
of progression
Exam Mark versus Effect Size
0.2
0.1
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
-0.1
Accounting
-0.2
-0.3
-0.4
52

55. Subject Uptake
100.00%
Gender Uptake of Various Subjects
90.00%
80.00%
70.00%
60.00%
Female
50.00%
Male
40.00%
30.00%
20.00%
10.00%
0.00%
55

56. Relative Subject Attainment
10.00%
Relative Subject Difficulty
5.00%
0.00%
-5.00%
-10.00%
Male
Female
-15.00%
56

57. Sample/Notional Student
Exam Results:
Mathematics – B1
Physics – C2
Irish (L) – A2
English – A2
French – C1
Chemistry – B3
Economics – B1
57

64. How Effective Is This?
1
Progress
0.9
0.8 Not Progress
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
64

65. How Effective Is This?
1
Progress
0.9
0.8 Not Progress
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
65

66. 1
How Effective Is This?
Progress
0.9
0.8 Not Progress
0.7
0.6 False Positive Rate
0.5
0.4
= 2/(2+9)
0.3 = 0.18
0.2
0.1
0
True Positive False Positive
True Positive Rate = 7/(7+2) 7 2
=0.78
False Negative True Negative
2 9
66

67. Receiver Operating Characteristic - Progression
1
0.9
0.8
0.7
True Positive Rate
0.6
0.5 All Factors
Random
0.4 CAO
CAO + Maths
0.3
0.2
0.1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
False Positive Rate
67

68. Can We Target These Students For Help?
Receiver Operating Characteristic - Progression
1
0.9
0.8
True Positive False Positive
0.7 7 2
True Positive Rate
0.6
0.5 All
Factors
0.4
Random
0.3 False Negative True Negative
0.2
0.1 2 9
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
False Positive Rate
Progress (as before)
Fail to Progress (as before)
Progress as before Exposed to whatever
Maybe progress? help/initiatives we put in
place
68

69. Economic Aspects
69

70. Developing Recommendations
• No ‘one size fits all’ in terms of recommendations as
existing systems and challenges faced are different in each
country
• Therefore each partner developed tailored
recommendations for their own country
• Nonetheless, certain recommendations were common to
most or all partners
• Aim of proposed changes is expand the numbers of
motivated, qualified and suitable students applying for and
taking up places in engineering programmes in our
countries.
70

71. Recommendations
Three categories:
a. Changes to admission requirements
b. Structural Changes to Education System
c. Socio-economic and cultural issues
Relative difficulty of implementation
• Easy, moderate or difficult
– Varies from country to country, see report for details
71

72. Recommendations (a)
Changes to admission requirements
Set required level of attainment in:
• Mathematics
• Physics and/or Chemistry
72

73. Recommendations (b)
Structural changes to education system
• Higher level of preparation in STEM
• Later ‘tracking’ of students
• Higher core STEM content for all students
If entry requirements in STEM subjects are
increased, it will be essential to equip students to
meet these standards
73

74. Recommendations (c)
Socio-economic and cultural issues
• Need to show relevance of STEM to real life
• Encourage more girls to pursue engineering
• Increase participation from marginal socio-
economic groups
74