Investigating the Role of an Inquiry-Based Biology Lab Course on Student Attitudes and Views toward Science
Bibliographic record
Abstract
Students' academic experiences can influence their conceptualization of science. In contrast experts hold particular beliefs, perceptions, opinions, and attitudes about science that are often absent in first-year undergraduate students. Shifts toward more expert-like attitudes and views have been linked to improved student engagement, critical-thinking ability, conceptual understanding, and academic performance. In this study, we investigate shifts in attitudes and views toward science by students in four biology classes with differences in student enrollment, academic support, and instruction. We observe significant, positive effects of enrollment in a guided-inquiry lab course and academic performance on the percentage of expert-like student attitudes and views at the end of term. We also identify variation in two aspects of student attitudes and views: 1) confidence and interest and 2) understanding and acceptance. In particular, enrollment in the lab course boosts student confidence and interest in scientific inquiry in the short term, even for students with low academic performance or little English-language experience. Our results suggest that low-performing students in particular may require additional opportunities for experiential learning or greater academic support to develop expert-like perceptions of biology as a science.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".