The skeletons in our closet: E‐learning tools and what happens when one side does not fit all
Bibliographic record
Abstract
In the anatomical sciences, e-learning tools have become a critical component of teaching anatomy when physical space and cadaveric resources are limited. However, studies that use empirical evidence to compare their efficacy to visual-kinesthetic learning modalities are scarce. The study examined how a visual-kinesthetic experience, involving a physical skeleton, impacts learning when compared with virtual manipulation of a simple two-dimensional (2D) e-learning tool, A.D.A.M. Interactive Anatomy. Students from The University of Western Ontario, Canada (n = 77) participated in a dual-task study to: (1) investigate if a dual-task paradigm is an effective tool for measuring cognitive load across these different learning modalities; and (2) to assess the impact of knowledge recall and spatial ability when using them. Students were assessed using knowledge scores, Stroop task reaction times, and mental rotation test scores. Results demonstrated that the dual-task paradigm was not an effective tool for measuring cognitive load across different learning modalities with respect to kinesthetic learning. However, our study highlighted that handing physical specimens yielded major, positive impacts on performance that a simple commercial e-learning tool failed to deliver (P < 0.001). Furthermore, students with low spatial ability were significantly disadvantaged when they studied the bony joint and were tested on contralateral images (P = 0.046, R = 0.326). This suggests that, despite limbs being mirror images, students should be taught the anatomy of, as well as procedures on, both sides of the human body, enhancing the ability of all students, regardless of spatial ability, to take anatomical knowledge into the clinic and perform successfully. Anat Sci Educ 10: 570-588. © 2017 American Association of Anatomists.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".