14. By Students, For Students: Adapting Inquiry-Based Learning for Undergraduate Human Anatomy Education in a Large Class Setting
Bibliographic record
Abstract
Inquiry-based learning (IBL) is a well-documented educational paradigm that has been adopted for the teaching of various subjects. In the interest of finding novel methods of teaching a traditionally lecture-based subject, IBL was adopted into an undergraduate human anatomy course in 2009 as a project called Inquiry 216. This project allows students to engage in a group-oriented and open-ended research project culminating in a free-format presentation. Since its inception, Inquiry 216 has undergone multiple revisions in methodology, with particular attention paid to formalizing the role of student facilitation, encouraging graduates of Inquiry 216 to become facilitators, and the evaluation and subsequent improvement of IBL in the context of Inquiry 216. A chronological account of conception, issue identification, objective and subjective evaluation and improvement of Inquiry 216 to its present model is illustrated, along with a specific emphasis on the benefits of student-based facilitation. We suggest that successful development, evaluation and improvement of IBL as a parallel to didactic education can further enhance students’ potential and enthusiasm for learning across various subjects.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".