Exploring discipline differences in student engagement in one institution
Bibliographic record
Abstract
Student engagement has become increasingly important in higher education in recent years. Influenced internationally by government drivers to improve student outcomes, many countries and institutions have participated in surveys such as the National Survey of Student Engagement (NSSE) and its progeny, the Australasian Survey of Student Engagement (AUSSE). Findings from these surveys are used to make comparisons, for example, between disciplines within an institution, and between different institutions. The intention is positive – to generate institutional improvement. However, some researchers are raising issues with the design and use of instruments like the NSSE, particularly as it becomes dominant in countries such as the USA, Canada, Australia, New Zealand, South Africa, China and Ireland. Questions have also been raised about discipline differences in student engagement. This article reports on a study conducted in New Zealand. It draws on data from an AUSSE to answer the question: what can we learn about discipline differences in student engagement from AUSSE data in one institution? It uses analysis of variance and post hoc procedures to identify significant differences between disciplines. Findings show that: there were significant differences between disciplines on all six engagement scales; some discipline differences are influenced by assumptions in the AUSSE; findings on differences between hard and soft disciplines are both similar to and different from previous studies; AUSSE data not be compared across disciplines within an institution; and the AUSSE scales need to go beyond the current focus on measuring students’ behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".