Enhance the Attractiveness of Studies in Science and Technology WP 6: Formal Hinders Kevin Kelly Trinity College Dublin WP 6 Co-ordinator

WP 6: Formal Barriers Origins of WP6: Are there students who want to study engineering at third-level but who are prevented from doing so? What are the barriers in their way? Aim: To examine the formal barriers to engineering education at third-level For example: University admission requirements School systems which compel students to choose a particular path early on Financial circumstances and access issues

Development of the Work Package Expanding the focus of WP6 –Formal barriers only part of the issue –Needed to examine the subtle factors that can have a significant impact Examination of the pre-university education system –What are the structural factors that contribute to a student choosing engineering? –Assessment of formal barriers AND influencing factors (e.g. exposure to STEM subjects, career guidance, etc)

Actions performed so far Formulation of documentation template for circulation to partners Documentation of education systems in partner countries Preliminary analysis of results Comparison framework for national results

Documentation of education systems in partner countries Aim: To collect data on key aspects of the primary and secondary education systems, and university admissions practices, in all partner countries Example of topics covered: Structure of school system STEM subjects taught Teacher training Devised: April – June 2010 Revision and Agreement: June - October 2010 Sent to all ATTRACT partners: October 2010 Consolidation commenced: February 2011

Comparison Framework Aim: To provide a framework for readily comparing the education systems in partner countries under key headings – required in each work package Current status: Preliminary model devised to present comparison data Combination of charts, tables and textual info used Detailed information from each partner country will be added

Comparison Framework Categories for comparisons: General information about partner universities Pre-university education in each partner country Career Guidance provision for school students University admissions practices Financial situation for third-level students

Comparison Framework – Sample of preliminary data Overview of partner universities

Comparison Framework – Sample of preliminary data % of second-level students by type of school/curriculum

Purpose: To document the progressive hours of student exposure to engineering-relevant STEM subjects throughout the primary and secondary education cycles STEM Subjects covered: Maths (incl. Applied Maths) Physics Chemistry Other STEM (ICT, technical graphics, construction studies, etc) Comparison Framework: Exposure to STEM subjects over time

Student exposure to STEM subjects over time

Career Guidance

University Admissions

Statistical Analysis Aim: To examine factors affecting student success at summer exams, in the context of the formal barriers to third-level education assessed within WP 6 Point of Enquiry: What factors in the pre-third level education system impact on success at third level?

Statistical Analysis Background: HEA Study (October 2010) Examined factors affecting student progression, including: –Prior attainment in Maths –Prior attainment in English –Overall prior educational attainment –Field of study –Student characteristics (e.g. gender, age, socio-economic background) Findings: −Prior attainment in Maths was single strongest predictor of successful progression in higher education

Statistical Analysis: TCD Data Examined: entrants through CAO and leaving certificate 2078 students Of these, 168 were engineering students Data Analysis: Logistic regression was used to examine the following variables: –CAO points –Gender –CAO score in English –CAO score in Maths –Average of CAO scores in Maths and Physics –Average of CAO scores in Maths and Applied Maths The logistic model was of the form y=1/(1+exp(-u)) where u is a linear combination of the independent variables. The output of the regression therefore is the value of the weighting coefficients for u.

Results of Statistical Analysis: TCD Main findings: CAO results overall had a significant predictive power Results in Maths and English had no additional predictive capability Gender has a substantial impact on success at first year exams across Trinity College as a whole Applied Maths may have some predictive power, but more data is needed to confirm this Findings when considering engineering students only: Gender has no impact Further examination of CAO results in English may be worthwhile as there is a suggestion of some predictive power

Challenges and obstacles Definition of scope of comparison Formulation of headings for comparison Acquisition of data Distillation of data into coherent summary Difficulty in comparing very different education systems

Involvement of stakeholders Why & what typology –Missing data/more data –Other headings/metrics –Effectiveness/appropriateness of barriers In what way (activities and expectations) –Determined at project level –Circulation of draft documents –Comment/feedback process

Next Steps Gathering of outstanding data (late May 2011) Completion of comparison framework (early June 2011) Gathering evidence of effectiveness of current barriers (September 2011) Analysis of results & preliminary conclusions (end September 2011) Drafting of WP 6 final report (January 2012)

Final comments The number of formal barriers is not particularly high but the underlying systems are so different as to make comparison extremely difficult. This is a recurring theme in the project as a whole. The effectiveness and appropriateness of barriers depends crucially on the structure of the education system.

Thank You