Thrivers and Divers: Using Non-Academic Measures to Predict College Success and Failure
Bibliographic record
Abstract
We collect a comprehensive set of non-academic characteristics for a representative sample of incoming freshman to explore which measures best predict the wide variance in first-year college performance unaccounted for by past grades. We focus our attention on student outliers. Students whose first-year college average is far below expectations (divers) have a high propensity for procrastination -they self-report cramming for exams and wait longer before starting assignments. They are also considerably less conscientious than their peers. Divers are more likely to express superficial goals, hoping to 'get rich' quickly. In contrast, students who exceed expectations (thrivers) express more philanthropic goals, are purpose-driven, and are willing to study more hours per week to obtain the higher GPA they expect. A simple seven-variable average of these key non-academic variables does well in predicting college achievement relative to adding more variables or letting a machine-algorithm choose. Our results, descriptive in nature, warrant further research on the importance of non-linearities for the design and targeting of successful interventions in higher-education.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".