The Anatomy of E‐Learning Tools: Are modern e‐learning tools a suitable replacement for traditional learning methods?
Bibliographic record
Abstract
Increasing class sizes and a reduction in laboratory hours have increased the popularity of commercial anatomy e‐learning tools. Our previous research (n=70) compared a simple 2dimensional e‐learning tool (A.D.A.M. Interactive Anatomy) to a more complex tool that allows for a more 3‐dimensional perspective (Netter's 3D Interactive Anatomy). Despite the differences in how these e‐learning tools present information, student ability to learn anatomical material, and their mental effort while doing so, known as cognitive load, were identical between elearning tools. However, when students with low spatial ability studied anatomical content with the more complex tool (Netter's 3D Interactive Anatomy), their performance scores were significantly lower than those students with high spatial ability (p=0.007, R 2 =0.103). These results indicate that e‐learning tool software design can differentially influence students based on their spatial ability, but questions remain regarding how these e‐learning tools compare to more traditional learning processes, such as physically manipulating a skeleton. Studies are ongoing to determine how performance scores are impacted when students study a bony joint using a physical skeleton compared to a simple commercial software program (A.D.A.M. Interactive Anatomy). Student performance on anatomical post‐tests will be compared to their mental rotation test (MRT) score (a measure of spatial ability) in an effort to determine the relationship between spatial ability and the effectiveness of models versus software. Using a novel dual‐task methodology, undergraduate anatomy students from Western University, Canada (n=75) are being assessed using a baseline knowledge test, Stroop observation task response times (a measure of cognitive load), MRT scores and an anatomy post‐test (a measure of learning). We hypothesize that the acquisition of anatomical knowledge by students, regardless of their spatial ability, will be superior when learning is associated with a real model, rather than currently available e‐learning tools. Results of this study will determine if currently available e‐learning tools are effective in delivering anatomical education; alternatively, if this is not the case, we will have identified a major weakness in the strategy to move traditional education online. Support or Funding Information Ontario Graduate Scholarship, Government of Ontario, Canada.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".