Critical Thinking in Science Education: Can Bioethical Issues and Questioning Strategies Increase Scientific Understandings?
Bibliographic record
Abstract
Many North American school districts and post-secondary academic institutions are acknowledging the importance of becoming a critical thinker. Future citizens will need to be to informed consumers of technology, science, sociology, and ethics, to name a few. After all, the world has become vastly more complicated, necessitating such skills as reasonableness and logical thinking. By engaging students at a crucial time in their developmental process, we can lay the foundation for good critical thinkers. The purpose of this paper is to examine the importance of critical thinking in science education, both at the secondary and post-secondary levels. Evidence regarding its suitability will be drawn from critical thinking and science education literature, as well as previous studies using bioethical decision-making and generic question stem strategies with middle school and university students.
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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 | 0.065 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".