Bibliographic record
Abstract
Anatomy is generally considered by students to be a subject that is simply learned through assimilation of facts. Unfortunately, this type of strategy both ill‐equips students to form connections between material and ill‐prepares them for more complex learning that is necessary in medical curriculums. It has been shown previously (Regpalla & Easteal, 2013) that retrieval practice improves results in large gross anatomy classes and we are currently assessing the benefit of sleep consolidation in the hard wiring of anatomical information that would provide the basis for more complex learning when required. These results, and others, will be highlighted in a discussion describing teaching and learning techniques used in 2 nd and 3 rd year pre‐med courses in gross anatomy that have optimized up‐to‐date teaching and memory strategies. The presentation will involve the discussion of the following teaching techniques: cognitive load application, spiral syllabus, association (or congruency), constructive alignment, and the use of document cameras. In addition, descriptions of memory acquisition techniques will include: 1) study of lecture material within 3 hours of sleep, 2) sleep acquisition of ‘recent memory’, 3) once‐a‐week labs in both an anatomical dissection room and museum (+1200 specimens), and 4) the use of retrieval practice to enhance long term memory (which has been shown to be more than twice as effective as studying for long term memory: Karpicke & Roediger, 2008). Moving forward, the hope is that the discussion of these teaching and learning techniques, as currently used in gross anatomy courses, will provide educators with examples of how they can be implemented into course curriculum with the aim of improving student learning and long‐term retention. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".