An Investigation of Secondary School STEM Subjects as Predictors of Academic Performance in Tertiary Level Health Sciences Programs
Bibliographic record
Abstract
Tertiary institutions are experiencing an increased number of enrolments, with students varying in their demographics, previous education, and academic achievement. This relative increase in undergraduate enrolments in Australia has not translated to an increase in student retention or graduate numbers. This prompts the need to explore predictors of academic performance for tertiary students to identify those most at risk of underperforming. This study aimed to investigate the relationship between secondary school subject completion and undergraduate grade point average (GPA). A cohort of 709 secondary school students entering undergraduate health science courses between 2012 and 2015 at an Australian university were investigated. Completion of Mathematics 3C3D, Physics, Chemistry and Physical Education Australian Tertiary Admission Rank (ATAR) subjects were significantly associated with GPA. In a subset of 458 students, longitudinal analysis revealed completion of secondary school Mathematics 3C3D was a significant predictor of academic performance over the duration of the tertiary health science courses. The results suggest that completion of advanced secondary school mathematics, but not physical sciences, is predictive of student achievement. This outcome further supports the need for improved uptake and completion of advanced mathematics in secondary school.
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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 | 0.000 |
| Open science | 0.000 | 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".