Improving Student Preparedness and Retention—Perceptions of Staff at Two Universities
Bibliographic record
Abstract
In 2010 the Australian government provided funding under the Higher Education Participation and Partnerships Program (HEPPP) to assist universities to achieve a 20 percent participation rate for students from lower socioeconomic backgrounds. This funding has allowed universities the opportunity to implement projects towards this end. This study explores the reactions of staff employed in devolving HEPPP projects within Deakin University (DU) and Southern Cross University (SCU). Both universities have a diverse student body, with participation by regional and low socioeconomic status (SES) students at higher proportions than the national higher education average. DU has used its HEPPP funds to establish the Deakin University Participation and Partnerships Program (DUPPP), which comprises community, school and technical/vocational education and training partnerships, embedded academic skills programs, and inclusive support programs. In contrast, SCU, through its i-OnTrack project, is developing a tracking system that will follow cohorts of students coming from diverse backgrounds in order to identify those factors in their life that either impede or boost academic excellence. Key informant interviews of academic staff at both these universities (N=18) were thematically analyzed and compared. Our recommendations for institutional practice across Australia arising from this analysis include: the need to maintain appropriate resourcing for academic staff (especially for casual tutors) to support the kinds of programs that make a difference, to commence intervention programs early at secondary school and prior to the students entering university, and for intervention programs to target all students in order to capture any students who may not be obviously at risk.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".