Developing a Signature Pedagogy and Integrated Support Model for First-Year Teacher Education Students Studying at a Regional University
Bibliographic record
Abstract
The Australian regional university where this pilot study was completed is confronted with a number of demographic factors that challenge the delivery of effective student support and engagement. In 2020, the teacher education student cohort comprised of approximately 5,100 students, with 82.6% 25 years of age or older, 20.3% identified as having a low SES background, 43.7% being first-in-family, and 96.1% studying off-campus. Student demographic characteristics such as these are commonly cited as factors that contribute to increased challenge in completing tertiary study (Grebennikov & Shah, 2012; Li & Carroll, 2020). The attrition rate for commencing students for the period from 2010 to 2018 ranged between 24.6% and 36.2%. While these demographic characteristics are largely objective in character and may not be able to be addressed by university-based intervention, the nature and quality of the learning environment students’ experience is able to provide the best opportunity for them to successfully complete their tertiary study endeavours, despite their personal context and backgrounds. One factor that has been identified as critical to the success of commencing students, particularly those from non-traditional backgrounds, is the nature of their relationships with, and the academic environment established by the academics teaching first-year units (Farr-Wharton, Charles, Keast, Woolcott, & Chamberlain, 2017).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".