Predicting Academic Success of Health Science Students for First Year Anatomy and Physiology
Bibliographic record
Abstract
Students commencing tertiary education enter through a number of traditional and alternative academic pathways. As a result, tertiary institutions encounter a broad range of students, varying in demographic, previous education, characteristics and academic achievement. In recent years, the relatively constant increase in tertiary applications in Australia has not translated to an increase in student retention or graduate numbers. The Health Sciences discipline typically falls within this paradigm, prompting various approaches to promote academic success and overall student retention. In this study, the demographic and previous education of health science students at an Australian University, were analysed along with first year science grades from a core first year anatomy and physiology unit. A generalized linear model (GLM) demonstrated statistically significant relationships between performance in the unit (measured by grade point average) and year 12 Australian Tertiary Admissions Rank (ATAR) subjects (human biology and chemistry; p <0.001) and gender ( p <0.001). No significant performance correlation was observed with household socioeconomic status, as measured by socio-economic indexes for areas. Taken together, the results from this study facilitate estimation of academic success by some parameters prior to their commencement at University.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".