Understanding Ethnic Differences in Student Success at Universities in New Zealand Using Linked Administrative Data
Bibliographic record
Abstract
Using administrative data provided by a New Zealand university, Cao and Maloney (2018) examined the academic differences in first-year course completion and GPA achievement between the main ethnic minority groups (Māori, Pasifika and Asian) and European. The authors found that roughly a quarter of the Māori/Pasifika-European gaps in academic outcomes could be explained by observable factors that included personal characteristics, high school backgrounds and university enrolment patterns. The current thesis extends Cao and Maloney (2018) in two ways. First, it uses more data on personal, family and school backgrounds that stored in a large database, the IDI, which is maintained and operated by Statistics New Zealand. Second, this is national-level analysis on a broader range of university outcomes including participation, first- and second-year course completions, and, finally, qualification completion. The original study sample in this thesis is comprised of approximately 180,000 individuals who turned 15 years old in the years 2010 to 2012 and were enrolled in a school in New Zealand. These students are split into three age-15 cohorts. Second-year course analysis is restricted to the 2010 and 2011 cohorts, while degree completion analysis is further limited to the 2010 cohort. Based on ethnicity prioritisation, the population is ethnically broken down into 57.1% European, 21% Māori, 10.3% Asian, 9.1% Pasifika, 1.8% MELAA and 0.8% from other ethnicities. Probit with Maximum Likelihood, Fractional Probit and decomposition techniques are used to answer the research questions. Other personal characteristics, school characteristics, parent-related factors and university factors are included in our analysis. The regression results show clear patterns that are relatively in line with the literature. First, there are some sizeable overall differences between the ethnic minority and the European students. The most academically disadvantaged groups are Māori and Pasifika, who, when compared to European, are much less likely to undertake bachelor studies at university, complete their courses in the first and second years, or complete a degree qualification. Asian are initially more likely to participate in university relative to European, but to have lower levels of performance in the later stages of university studies. Our decomposition results indicate that giving Māori and Pasifika the same characterises as European could close most of these ethnic gaps in university participation, no more than half of the gaps in course completions and about one quarter of the gaps in degree completion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".