Cognitive load imposed by ultrasound‐facilitated teaching does not adversely affect gross anatomy learning outcomes
Bibliographic record
Abstract
Ultrasonography is increasingly used in medical education, but its impact on learning outcomes is unclear. Adding ultrasound may facilitate learning, but may also potentially overwhelm novice learners. Based upon the framework of cognitive load theory, this study seeks to evaluate the relationship between cognitive load associated with using ultrasound and learning outcomes. The use of ultrasound was hypothesized to facilitate learning in anatomy for 161 novice first-year medical students. Using linear regression analyses, the relationship between reported cognitive load on using ultrasound and learning outcomes as measured by anatomy laboratory examination scores four weeks after ultrasound-guided anatomy training was evaluated in consenting students. Second anatomy examination scores of students who were taught anatomy with ultrasound were compared with historical controls (those not taught with ultrasound). Ultrasound's perceived utility for learning was measured on a five-point scale. Cognitive load on using ultrasound was measured on a nine-point scale. Primary outcome was the laboratory examination score (60 questions). Learners found ultrasound useful for learning. Weighted factor score on "image interpretation" was negatively, but insignificantly, associated with examination scores [F (1,135) = 0.28, beta = -0.22; P = 0.61]. Weighted factor score on "basic knobology" was positively and insignificantly associated with scores; [F (1,138) = 0.27, beta = 0.42; P = 0.60]. Cohorts exposed to ultrasound had significantly higher scores than historical controls (82.4% ± SD 8.6% vs. 78.8% ± 8.5%, Cohen's d = 0.41, P < 0.001). Using ultrasound to teach anatomy does not negatively impact learning and may improve learning outcomes. Anat Sci Educ 10: 144-151. © 2016 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".