Celebrating student engagement in an undergraduate histology course: A showcase review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it