Building on the VU Block foundations: Results from theinaugural first year cohort
Bibliographic record
Abstract
In 2018, Victoria University (VU) implemented the most radical change to first-year university education in Australian history. Transforming the traditional model of concurrently studying four lecture and tutorial-based units into sequential 4-week blocks conducted in small, active learning groups, led to improvements in retention and results. Comparing the inaugural first-year student cohort to matched counterparts from the preceding year, this paper presents the initial results of an innovative, cross-disciplinary first-year change initiative which recalibrated most aspects of higher education: mode of delivery, teaching practice and curriculum to ensure student retention and increase opportunities for success. The data demonstrates how the approach has impacted on diverse student cohorts including non-English-speaking background students (up 14%), first-infamily (up 13%), Aboriginal or Torres Strait Islander (up 21%), low socio-economic (up 15%) as well as all domestic (up 9.9%) and international (up 5.8%) students. The paper challenges traditional assumptions of how Australian higher education is delivered and provides evidence that purposefully applying focused strategies
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".