Celebrating student engagement in an undergraduate histology course: A showcase review
Bibliographic record
Abstract
Drawing is a teaching tool that provides numerous benefits to student learning, including enhanced knowledge retention, improved observation skills, and increased engagement with course content. However, these exercises also place high cognitive demands on students and require a considerable time commitment. To acknowledge and celebrate the effort students invest in their drawings; while also giving these illustrations curricular significance, a gallery walk can be an effective teaching strategy. During a gallery walk, students move around a learning space to view, analyze, and discuss work displayed on the walls. This article describes an adaptation of a gallery walk, named a 'showcase review session', which was implemented in an undergraduate histology course. This optional session highlighted highly accurate assignment drawings while offering a content review before the final examination. The course instructor created review questions associated with the student drawings, which were projected onto screens around the room. Students could visit the session at any time, with the questions cycling continuously, and the course instructor and teaching assistants circulated to answer questions. Students responded positively to the session, noting that it helped them prepare for their upcoming practical laboratory examination and that showcasing student work added value to the course. Showcase review sessions like the one described can be applied across disciplines, giving students the opportunity to learn from their peers' work while effectively reviewing course content in an engaging and interactive environment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".