The development of interactive online learning tools for the study of Anatomy
Bibliographic record
Abstract
BACKGROUND: The study of human anatomy is a core component of health science programs. However large student enrolments and the content-packed curricula associated with these programs have made it difficult for students to have regular access to cadaver laboratories. METHODS: Adobe Flash MXwas used with cadaver digital photographs and textbook-derived illustrations to develop interactive anatomy images that were made available to undergraduate health science students enrolled in first-year combined anatomy and physiology (ANP) courses at the University of Ottawa. Colour coding was used to direct student attention, facilitate name-structure association, improve visualization of structure contours, assist students in the construction of anatomical pathways, and to reinforce functional or anatomical groupings. The ability of two-dimensional media to support the visualization of three-dimensional structure was extended by developing the fade-through image (students use a sliding bar to move through tissues) as well as the rotating image in which entire organs such as the skull were photographed at eight angles of rotation. Finally, students were provided with interactive exercises that they could repeatedly try to obtain immediate feedback regarding their learning progress. RESULTS: Survey data revealed that the learning and self-testing tools were used widely and that students found them relevant and supportive of their self-learning. Interestingly, student summative examination outcomes did not differ between those students who had access to the online tools and a corresponding student group from the previous academic year who did not. CONCLUSION: Interactive learning tools can be tailored to meet program-specific learning objectives as a cost-effective means of facilitating the study of human anatomy. Virtual interactive anatomy exercises provide learning opportunities for students outside the lecture room that are of especial value to visual and kinesthetic learners.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".