Interpreting differences between the United States and New Zealand university students’ engagement scores as measured by the NSSE and AUSSE
Bibliographic record
Abstract
Press releases concerning the Australasian Survey of Student Engagement (AUSSE) results warn that university students in Australia and New Zealand are less engaged than their peers at United States institutions. Such warnings about student engagement and interactions then become targets for improvement on Australasian universities’ strategic plans. In considering New Zealand university students’ survey responses, we examined AUSSE and the US National Survey of Student Engagement (NSSE) data for 2009 and 2010 with respect to all items that load on the five scales these instruments share. We argue that most of the observed differences in responses, response distributions and subsequent scale scores can be attributed to differences in educational pathways and cultures between the USA and New Zealand. Consequently, considerable caution in these trans-Pacific comparisons is warranted, particularly when formulating policy and practices to improve student engagement in New Zealand based on methods that have been employed in different educational contexts.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".