Strategies to Help Students Tackle Complicated Anatomical Terminology
Bibliographic record
Abstract
The study of anatomy involves the acquisition of a new language as students familiarize themselves with the names of the components of organs and organ systems. Many of these names derive from Latin and Greek, languages with which students have limited experience and they struggle with the pronunciation and spelling of each new term. Furthermore, students gain little experience in actively recalling, speaking and writing this terminology (they are provided with PowerPoint (PPT) lecture slides) and high enrolments (250–300 students per course) make weekly laboratory sessions impossible. Interactive learning strategies are needed to enrich their educational experience. Videos narrated by peers allow students to hear the names of bones and muscles while simultaneously using visual pathways to situate their study of musculoskeletal (MSK) anatomy within applied contexts such as a fractured wrist or the assumption of a yoga pose. Interactive PPT‐based Hangman games (PPTAlchemy R ) require students to both recall functional anatomy content and to focus on the letters composing a muscle's name when assigning letters to spaces. Indeed, gamification is a new educational trend that promotes interactive learning by giving students low‐stakes opportunities to practice and to learn from their errors. A final approach, the development of content‐specific crossword puzzles using EclipseCrossword R , also uses clues to stimulate recall and forces students to focus on word structure. But these puzzles have the additional advantages of accommodating words of unlimited length (word limit for PPT‐based Hangman is 15 letters), alerting students to an answer with the wrong number of letters, allowing students to identify spelling errors following puzzle submission, and motivating students to produce a finished product. Despite the fact that marks were not assigned to these interactive activities, tracking data shows that they were well used by students when preparing for summative evaluation. Students in two undergraduate courses in MSK anatomy (n = 233 and 235) presented with a series of four yoga videos over the three weeks leading up to summative exams watched each video an average of 1.92–2.70 times with most students being sufficiently engaged to view the entire series at least once. Preliminary data tracking student use of crossword puzzles in a first‐year anatomy and physiology course (n = 296) revealed a high level of interest when each puzzle was released (immediately accessed by over 25% of students) and that many students (at least 25–30%) returned to try the puzzles more than once. These studies involving students of anatomy show that educational strategies that combine audio with visual and/or use interactive word‐based games can enrich learning and are welcomed by students tackling a new language. Support or Funding Information University of Ottawa Undergraduate Research Opportunity Program
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".