Learning about Academic Ability and the College Drop-out Decision
Bibliographic record
Abstract
We use unique data to examine how college students from low income families form expectations about academic ability and to examine the role that learning about ability and a variety of other factors play in the college drop-out decision. From the standpoint of satisfying a central implication from the theory of drop-out, we find that self-reported expectations data perform well relative to standard assumptions employed in empirical work when it is necessary to explicitly characterize beliefs. At the time of entrance, students tend to substantially discount the possibility of bad grade performance, with this finding having implications for understanding the importance of the option value of schooling. After entrance, students update their beliefs in a manner which takes into account both initial beliefs and new information, with heterogeneity in weighting being broadly consistent with the spirit of Bayesian updating. Learning about ability plays a very prominent role in the drop-out decision. Among other possible factors of importance, while students who find school to be unenjoyable are unconditionally much more likely to leave school, this effect arises to a large extent because these students also tend to receive poor grades. We end by examining whether students whose grades are lower than expected understand the underlying reasons for their poor grade performance.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".