Longitudinal Study of Student Attitudes in a Biology Program
Bibliographic record
Abstract
This is among the first longitudinal studies to report student attitudes across 4 yr of a university program. We found that the attitudes of students in biology become significantly more expert-like from the first year to the fourth year of the program, that is, there was a significant positive shift in students' overall percent favorable scores from 64.5 to 72%, as opposed to the expert response, which averaged 90%. There was a significant positive shift for the real world connection category (78-85%), the enjoyment (personal interest) category (74-82%), and the conceptual connections/memorization category (66-74%). Moreover, there was a significant correlation between students' overall percent favorable scores and performance (cumulative grade point average) at the end, but not at the beginning, of the fourth year, with high-performing students having significantly more expert-like attitudes than low-performing students. The correlation between percent favorable score and performance was the strongest for the problem solving: synthesis and application category, in which the highest-performing students finished their fourth year with 90% favorable compared with 35% favorable for the lowest-performing students. A comparison of these results with previously reported results and their implications for teaching are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".