Developing a Dissection‐based Human Anatomy Laboratory Manual to Target Course and Laboratory Learning Objectives at the University of Guelph
Bibliographic record
Abstract
Dissection‐based human anatomy is an inherently time‐consuming process. Accordingly, students must use their time in the laboratory as efficiently as possible. There are many commercial resources that are currently available to students for pre‐laboratory preparation at home; however, preliminary data gathered from student questionnaires (n = 217) indicate that a significant proportion of third‐year undergraduate students enrolled in human anatomy at the University of Guelph choose not to use these resources to prepare for their laboratory sessions (p<0.01). Anecdotal student feedback revealed that their avoidance of the resources was because the students typically perceived the commercial resources to be overly text‐based and lacking alignment with the course curriculum. Therefore, we created a preparatory dissection‐based laboratory manual as a part of an ongoing project in which we are developing, evaluating, and refining educational tools to enhance our human anatomy program. To maximize the potential impact of all such laboratory resources, we aim to develop the dissection‐based laboratory manual to: 1) specifically target the University of Guelph's human anatomy course and laboratory learning objectives, 2) bridge lecture and laboratory content, and 3) enable students to address basic practical and clinical applications of anatomy. To achieve these objectives, the manual incorporates various educational components, including dissection‐based cadaveric images, digital illustrations and schematics, dissection instructions, and active‐learning exercises. These components will be integrated to help students visualize and understand concepts such as: 1) the origins and pathways of nerves and arteries that are initially discovered and examined in distal sites, 2) compartmentalization of the limbs, and 3) relative levels of depth between structures and their organization within the human body. In addition, the manual targets course concepts that are not easily visualized or understood with a regional anatomy approach, given the time constraints and the schedules of our dissection‐based courses. By making this resource available to students outside of the laboratory, our goal is to promote student preparedness for each session and enhance the quality of learning within the human anatomy laboratory itself. Student feedback sessions will be used to inform further development of the laboratory companion and improve future editions of the resource. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".