Using Case Studies to Improve the Critical Thinking Skills of Undergraduate Conservation Biology Students
Bibliographic record
Abstract
Critical thinking (CT) underpins the analytical and systems-thinking capacities needed for effective conservation in the 21st century but is seldom adequately fostered in most postsecondary courses and programs. Many instructors fear that devoting time to process skills will detract from content gains and struggle to define CT skills in ways relevant for classroom practice. We tested an approach to develop and assess CT in undergraduate conservation biology courses using case studies to address both challenges. We developed case studies with exercises to support content learning goals and assessment rubrics to evaluate student learning of both content and CT skills. We also developed a midterm intervention to enhance student metacognitive abilities at a light and intensive level and asked whether the level of the intervention impacted student learning. Data from over 200 students from five institutions showed an increase in students’ CT performance over a single term, under both light and intensive interventions, as well as variation depending on the students’ initial performance and on rubric dimension. Our results demonstrate adaptable and scalable means for instructors to improve CT process skills among undergraduate students through the use of case studies and associated exercises, aligned rubrics, and supported reflection on their CT performance.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".