The Human Anatomy Laboratory Companion: A Teaching Tool Created by Students, for Students
Bibliographic record
Abstract
Anatomy is a field that lends itself to a wide range of teaching methodologies depending on student numbers, course level, time constraints, and the level of anatomical detail addressed. The University of Guelph continuously provides high quality human anatomy education to undergraduate students. The program offers dissection‐ and prosection‐based courses at the first, third and fourth year levels of undergraduate study. The combined enrolment in all human anatomy courses is approximately 1000 students per academic year. An ongoing challenge is to continue to provide a very high level of human anatomy education to our students with the available resources. The aim of this project is to develop a ‘companion’ or dissection‐based laboratory manual that: 1) specifically targets curricular goals and course learning objectives, 2) promotes self‐sufficient learning in the laboratory, and 3) increases the efficiency in which material is taught in the laboratory. Fundamental to this laboratory companion is the incorporation of high quality digital images of cadaveric‐based dissections, and has the unique characteristic that it is ‘created by students, for students’. Upper year students who have previously taken the two‐semester dissection‐based course, contribute to the development of the companion, as part of course requirements for a subsequent Teaching, Learning and Knowledge Transfer (TLKT) experience. Thus, these students (TLKT) work closely with faculty and graduate students to prepare detailed, precise and step‐wise dissections that are captured digitally, and incorporated into this resource. Significant progress has been made in developing the framework for the companion, as dissections and digital images have been created. Preliminary data has been collected from students during the Winter 2017 semester of the third year course, with the use of questionnaires (n=305). The results show that this educational tool is desired by students to help them prepare for and review laboratory course content and excel independently. Subsequent to the development of the companion, it will be implemented as a learning resource in the dissection (HK*3401) and prosection (HK*3501) courses at the University of Guelph. The pedagogical impact will be investigated through comparison with historic controls and will be measured using student performance and student perception of the course experience as outcomes.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
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".