Transforming higher education and student engagement through collaborative review to inform educational design
Bibliographic record
Abstract
This paper reports on staff perceptions arising from a review process designed to assist staff in making informed decisions regarding educational design, approaches to engage students in learning, and the technology to support engagement in the classroom and across multiple locations and delivery modes. The aim of the review process was to transform the level of student engagement in the business faculty of an Australian university. The process took a collaborative approach through consultation with academic staff involved in the design and delivery of the units under review, and included targeted professional development as necessary. An institutional framework that characterises engagement indicator contexts and their attributes facilitated dialog during the review process. This paper reports on a mixed method study that included a survey of participants, and purposeful interviews to evaluate the effectiveness of the process. Although the study identified factors that hindered implementation and operationalization of review recommendations in some instances, study participants were generally of the view that recommendations would enhance student engagement. It is demonstrated that the bottom-up approach described in this paper is consistent with theoretical frameworks for transformational change in teaching and learning and the adoption of innovations.
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 | 0.491 | 0.597 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".