Teaching a Procedural Tutorial for the Dissection of the Musculoskeletal System Through the Use of a Digital Multimedia Platform
Bibliographic record
Abstract
Introduction The use of technology is becoming synonymous with our everyday lives. This is true for both the teacher and the learner. With the advances in technology, learners are now able to choose between a variety of modalities to facilitate their learning. This is in contrast to the teachers developing new ways to appeal to all learning styles. It has been previously seen that students using both computer resources and cadavers scored better exams than students who used cadavers solely (Biasutto et al., 2006). One area that still needs to be developed further is that of human gross anatomy. In 2012, Azer S.A.'s publication showed that an online resource such as YouTube was an inadequate source of information for students and recommended that medical schools develop anatomy videos and publish them as an open source (Azer, 2012). Objectives This project was designed to create an educational video tutorial that provides a step by step visual tutorial on how to conduct a dissection. This is important because students coming to the anatomy lab are expected to read instructions that tell them to make cuts along certain parts of the body. However, these instructions fail to highlight the imagery that the students see while dissecting the body. Although there are prosections and textbooks which show the anatomy on a cadaver, they fail to show the students the steps that were taken to get to that point. The tutorial is a dissection of the anterior leg and dorsum of the foot. Methods A checklist was created to determine which structures need to be dissected and which order needs to be followed to achieve this task. Then the cadaver and filming equipment was set up. The dissection and narration were conducted and filmed. After filming, the videos were edited and animations were added to highlight the structures that were identified previously in the checklist. At the end of the video, a small quiz was added to help students test their knowledge. Results To gather the efficacy of the videos and receive feedback we gave the students a survey to conduct prior to and after their lab. The results of this survey showed that 95% of students felt that after having watched the video they were more prepared for the dissection. When asked after completing the lab, 98.7% of students felt that the videos were helpful in enhancing their learning. When asked about whether they will be using the video as a study tool for their exam 90.9% of students said yes. Some of the comments that were received include, “the videos are great in orienting students and serve as a great study tool” and “really helpful to see the dissection beforehand and the quiz questions are very helpful as study tools.” Conclusion From the positive responses that were received from the survey it can be concluded that the use of video dissections is a viable teaching tool to complement the learning that is conducted in the classroom and dissection labs. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".