Search for the Woolly Mammoth: A Case Study in Inquiry-Based Learning
Bibliographic record
Abstract
Over the ages university teachers have searched for more and more effective methods of transferring knowledge. In this new millennium academic teachers have been exhorted to embrace new technology, be more flexible, and move from teacher-centered learning to student-centered learning. Educators are told constantly to allow students to be responsible for their own learning. The transmission-style lecture, potentially effective and practical in circumstances such as large classes, is still the major teaching approach used in universities today. Veterinary curricula in universities such as Mississippi University and Cornell are replacing traditional-style lectures with student-centered forms of learning that begin with problems. Traditional learning approaches can be likened to guidebooks the students read, while active, Inquiry-Based Learning (IBL) is a more experiential approach—the student becomes the traveler and takes the actual journey. This journey is guided by interaction with teacher and peers and helps students make connections among previously disparate and incoherent bits of information. Research by Rand found that veterinary students who experienced problemsolving approaches to learning had a better understanding of core principles and gained the ability to apply those principles to novel situations. Problem-based approaches allow students to formulate an integrated, holistic understanding of the learning material. Not only does the learning that they experience during their journey help to promote better understanding of concepts, it also encourages students to ask relevant questions, identify important issues, evaluate evidence, and use critical reasoning to formulate conclusions.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".