Undergraduate Student Course Engagement of an Ethnically Diverse Population in Auckland, New Zealand
Bibliographic record
Abstract
Increasing student engagement leads to improved educational outcomes, promotes positive student experiences, and reduces attrition rates. In Aotearoa (New Zealand), Māori students now account for 20% of university enrolments, but first-year attrition rates are approximately 17%. Both Māori and Pasifika students are more likely to drop-out during their first year of study than Pākehā students. To address the question Are Māori and Pasifika students less engaged than Pākehā students when studying in their first year of university? we measured student engagement during a compulsory first-year course delivered by a university in Tāmaki Makaurau (Auckland). Questions were from the National Survey of Student Engagement, and students identified their ethnicity as either Māori, Pākehā, Pasifika, or Other. Information on the campus of study was also collected. Three scales within the questionnaire were (1) Cooperative Learning, (2) Cognitive Development, and (3) Personal Skills, and both total score, and score for each scale, were compared between ethnicities and across campuses. Total engagement for Māori students was higher than all other ethnicities on all campuses, and at the South Auckland Campus, Māori students scored higher in Cooperative Learning than all other ethnicities. These encouraging scores for Māori students reflect a commitment of inclusion and support for Māori enrolled at this large tertiary education provider in Tāmaki Makaurau, Aotearoa. The questionnaire was convenient to use and scales showed good internal consistency. We suggest that regular measures of student course engagement are made so trends can be shown for all student groups enrolled at universities throughout Aotearoa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".