Enhancing Undergraduate Success in Biology through the Biomentors Program
Bibliographic record
Abstract
Many undergraduates who wish to pursue degrees in science, particularly students from underrepresented groups, drop out of science majors before realizing their goal. This study examines the effectiveness of a mentoring program – called Biomentors – aimed at promoting success in biology courses for undergraduates beginning their coursework toward a bachelor's degree in the biological sciences. Students enrolled in the Biomentors program met twice a week in a small group with an advanced biology major under the supervision of a faculty member to explore effective learning strategies for success in an introductory-level biology course they were taking. Students who participated in the Biomentors program scored significantly higher (based on total points earned) than other students enrolled in the course across two cohorts (d = 0.36 in the fall quarter of 2014; d = 0.34 in the winter quarter of 2015). The biomentors group significantly outscored the control group even when the effects of gender, parent income level, parent education level, total SAT score, and cumulative GPA were statistically controlled using a stepwise regression. Overall, the results encourage further investigation of the effectiveness of peer-mentoring programs that emphasize domain-specific learning strategies for college students beginning as science majors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".