Facilitating Scientific Inquiry Skills through Fiction-Based Learning
Bibliographic record
Abstract
Scientific inquiry skills (e.g., observation, critical analysis, hypothesis generation) are the cornerstone of scientific training, yet these skills are seldom taught. Consequently, an inquiry-based course was created at McMaster University, Science of Fictional Characters, that facilitates students to develop scientific inquiry skills. By analysing the feasibility of fictional characters in the real world, students apply and understand concepts in various scientific disciplines (e.g., biology, psychology, physics). This course embeds a variety of active learning strategies to foster the development of skills relevant to the scientific process, including group projects, written reflections, and Socratic discussions and debates. To gather student opinions about the effectiveness of the course, we administered an end-of-term survey, a modified version of the Course Interest Survey (CIS). This self-reported CIS included five subscales: student attention, relevance, confidence, satisfaction, and scientific inquiry skill development. Of the 17 surveys completed, on average the subscales scored above four on a five-point scale. Additionally, we performed a thematic analysis on 15 reflective assignments. Qualitatively, 10 codes were extracted from the student testimonies, which were grouped into four themes: student satisfaction, perceived applicability, course flexibility, and barriers to learning. Both data types revealed that students were engaged with the course and felt they improved their scientific inquiry skills. Our data further suggest the course would benefit from additional foundational scientific content. Nevertheless, the study provides an example of how fiction and an active learning model can create an engaging, skills-based learning environment in a science course.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".