Reflections as near‐peer facilitators of an inquiry project for undergraduate anatomy: Successes and challenges from a term of trial‐and‐error
Bibliographic record
Abstract
Near-peer facilitators (senior students serving as facilitators to their more junior peers) bring a unique student-based perspective to teaching. With fewer years of teaching experience however, students who become involved in a facilitator role typically develop related skills quickly through a process of trial-and-error within the classroom. The aim of this paper is to report on the authors' own experiences and reflections as student near-peer facilitators for an inquiry-based project in an undergraduate anatomy course. Three areas of the facilitator experience are explored: (1) offering adequate guidance as facilitators of inquiry, (2) motivating students to engage in the inquiry process, and (3) fostering creativity in learning. A practical framework for providing guidance to students is discussed which offers facilitators a scaffold for asking questions and assisting students through the inquiry process. Considerations for stimulating intrinsic motivations toward inquiry learning are made, paying attention to ways in which facilitators might influence feelings of motivation towards learning. Also, the role of creativity in inquiry learning is explored by highlighting the actions facilitators can take to foster a creative learning environment. Finally, recommendations are made for the development of formalized training programs that aid near-peer facilitators in the acquisition of facilitation skills before entering into a process of trial-and-error within the classroom.
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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 | 0.032 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".