A Novel Low-Cost Indicator of Student Perseverance and Its Association with College Student Academic Performance
Bibliographic record
Abstract
Within the research literature investigating how student characteristics related to perseverance impact academic outcomes, leading scholars have encouraged the development of new measurements, both survey and non-survey based. We introduce here an innovative non-survey-based measurement, derived from common higher education variables, that reflects the perseverance construct. The created perseverance proxy is easily created and explainable to audiences with minimal statistical background. The variable was used to analyze academic outcomes at a mid-sized public university in the United States. The perseverance proxy strongly positively associates with academic outcomes, as measured both by GPA and odds of academic probation, in multivariate analysis across both genders. The perseverance proxy explains more of the variation in academic outcomes than any of the cognitive and financial aid variables used in the analysis. The technique for constructing the perseverance proxy is easily replicated at any college or university having data on students’ high school ranking and college admission exam scores.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".