Selective Planning of the First Year Experience in Higher Education: A Sweden-Australia Comparative Study of Support
Bibliographic record
Abstract
Literature on the support of the First Year Experience (FYE) in institutions of Higher Education provides a range of modelled approaches. However, we argue that institutions still need to selectively plan which approach/es and attendant strategies are best suited to their particular contexts and institutional policy and practice frameworks and how their FYE is to be presented for their particular student cohort. This paper compares different ways of supporting students in their first year in two contrasting universities. The first case study focuses on a first year course at Stockholm University (SU), Sweden, a large, metropolitan, single campus institution, while the second investigates a strategy for supporting first year students using a community of practice at a satellite campus of the University of the Sunshine Coast (USC), a small regional university in South-East Queensland, Australia. The research contrasts a formal, first generation support approach versus a fourth generation support approach which seeks to involve a wider range of stakeholders in supporting first year students. The research findings draw conclusions about how effective the interventions were for the students and provide clear illustrations that selective planning in considering the institution’s strategic priorities and human, physical, and resource contexts was instrumental in providing a distinctive experience which complemented the institute and the student cohort. (212 words)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".