Abstract

Equity in higher education is one of the major challenges higher education institutions and policy makers face today. The need to enhance equity in higher education raises difficult ethical dilemma such as: how equitable are affirmative admission policies if they are ethnicity or race based? The literature, however, is inconclusive and highlighting the need to re-assess the current paradigms. This study tests a new model entitled “Dual Admission Model” which aims to enhance equity and equality in higher education while addressing many of the ethical dilemmas associated with affirmative action admission policies. Data of three consecutive national cohorts of New Zealand secondary school graduates were used to establish and test the effectiveness of a range of admission models. These datasets include achievements from secondary school assessments and data from the first year at the university. The predictability of the first year university GPA was calculated for different alternative admission models based on the NCEA features. The effect of these admission models on different groups of students was measured across three student leaving cohorts. It was found that the best models give greater weight to the quality of the assessments (i.e. higher grades) and less weight to quantity (i.e. credit accumulation) and particular combinations of subject choices. It was also found that by combining the new model with the current admission model (Dual Admission Model) provides a merit-based admissions system, which would potentially increase the number of under-represented students (e.g. lower socio-economic communities) while maintaining their success in the university academic programmes. These finding were consistent across all cohorts.It is suggested that this Dual Admission Model (DAM) will increase participation and success in degree programmes for students from traditionally underrepresented groups without having to apply any affirmative action admission policy. Implications for policy makers are discussed.

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