What Sets College Thrivers and Divers Apart? A Contrast in Study Habits, Attitudes, and Mental Health
Bibliographic record
Abstract
Students from 4-year colleges often arrive having already done very well in high school, but by the end of first term, a wide dispersion of performance emerges, with an especially large lower tail. Students that do well in first year (we call the top 10 percent Thrivers) tend to continue to do well throughout the rest of their time in university. Students that do poorly (we call the bottom 10 percent Divers) greatly struggle and are at risk of not completing their degree. In this paper we use a mandatory survey with open ended questions asking students about their first-year experience. This allows us to explore more closely what sets Thrivers and Divers apart, in terms of study habits, attitudes, and personal experiences. We find that poor time management and lack of study hours are most associated with poor academic performance, and that those who struggle recognize these weaknesses. Divers also report feeling more depressed and unhappy with their lives. We posit an 'academic trap', whereby initial poor performance is related to poor time management which in turn lowers expectations, which in turn leads to lower study time, and so on. Thrivers, in contrast, study significantly more and meet with course instructors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".