Dissection‐based Audiovisual Modules to Supplement Laboratory‐based Human Anatomy Education
Bibliographic record
Abstract
The notion of “doing more with less” is ubiquitous. At the University of Guelph, over 500 undergraduate students attend the human anatomy program yearly. Courses are accompanied by weekly dissection‐ and prosection‐based laboratories. Dissection‐based audiovisual modules were developed to ameliorate the high student to teaching assistant (TA) ratio, improve laboratory efficiency, promote deep learning (DL), and ultimately improve the overall educational experience. Through high‐definition videography, photography, and anatomical illustration, the modules capture a regional‐based dissection for the first 12 weeks of the course (back, upper limb, thorax, and abdomen). Each module is comprised of three chapters; 1) an introduction, presenting learning outcomes and objectives of the laboratory; 2) an overview, using hyper‐lapse videography to demonstrate the full dissection in a minute; 3) the main chapter, presenting the dissection procedure and techniques to achieve laboratory goals, along with important anatomical relationship and concepts. The modules are packaged in a user‐friendly, multiplatform package, offering interactive quizzes, supplementary resources, and user tracking. The revised two‐factor study process questionnaire (RSPQ‐2F) was used to measure students' approach to learning. An extended version of the course experience questionnaire (CEQ) was used to quantify the students' subjective course experiences. Weekly laboratory exit surveys were used to quantify the students' module usage characteristics. Additional data such as age, sex, and overall GPA average were collected during the semester. Lastly, focus group sessions were conducted with students and TAs. Dissection students utilized the modules 240% more than prosection students over the length of the semester (Di: 110.2 ± 8.17 min, Pro: 45.8 ± 10.8 min, t(73.22) = 4.76, p = < .0005, d = .54) (Fig. 1). Morning dissection laboratories which typically initiate new dissections utilized the modules 186% more than the afternoon dissection laboratories (AM: 143.6 ± 14.4 min, PM 77.2 ± 6.7 min, 45.8 ± 10.8 min, t(173.94) = 3.96, p = < .0005, d = 0.50). Module usage peaked during the deep back and axilla dissections, and dipped during the practical exam review laboratories, suggesting students focused on reviewing from the donors (Fig. 2). Lastly, as students became more self‐directed throughout the semester, module usage was reduced (Fig. 2). Overall CEQ score increased by 7.93 points when compared to the previous year (t(228) = 3.71, p < .0005, d = 0.25). The highest categorical increase was attributed to the “Clear Goals and Standards” scale, with a 20.35 point increase (t(228) = 5.84, p < .0005, d = 0.39). There was no significant correlation between module usage and deep approach scores. However, positive correlation was observed between module usage and the deep strategy subscale (r s (242) = 0.155, p = .023). TA focus groups suggests students relied less on the TAs for basic identification and guidance questions, providing TAs with more time to explain important anatomical relationship and concepts. Responses for student focus groups and surveys suggests expedience of this tool and need for online availability as a pre‐laboratory study resource. Overall, data suggests that dissection‐based modules are an effective supplementary tool for laboratory‐based human anatomy education. 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".