Incentives and Services for College Achievement: Evidence from a Randomized Trial
Bibliographic record
Abstract
Many North American college students have trouble satisfying degree requirements in a timely manner. This paper reports on a randomized field experiment involving two strategies designed to improve academic performance among entering full-time undergraduates at a large Canadian university. One treatment group (services) was offered peer advising and organized study groups. Another (incentives) was offered substantial merit-scholarships for solid, but not necessarily top, first year grades. A third treatment group combined both interventions. Service take-up rates were much higher for women than for men and for students offered both services and incentives than for those offered services alone. No program had an effect on mens grades or other measures of academic performance. However, the Fall and first-year grades of women in the combined group were significantly higher than those of women in the control group, and women in this group earned more course credits and were less likely than controls to be on academic probation. These differentials persisted through the end of the second year, in spite of the fact that incentives were given in the first year only. The results suggest that the study skills acquired in response to a combination of services and incentives can have a lasting effect, and that the combination of services and incentives is more promising than either alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".