Learning Challenges, Teaching Strategies, and Cognitive Load: Insights From the Experience of Seasoned Endoscopy Teachers
Bibliographic record
Abstract
PURPOSE: Learners of medical procedures must develop, refine, and apply schemas for both cognitive and psychomotor constructs, which may strain working memory capacity. Procedures with limitations in visual and tactile information may add risk of cognitive overload. The authors sought to elucidate how experienced procedural teachers perceived learners' challenges and their own teaching strategies in the exemplar setting of gastrointestinal endoscopy. METHOD: The authors interviewed 22 experienced endoscopy teachers in the United States, Canada, and the Netherlands between May 2016 and March 2019 and performed thematic analysis using template analysis method. Interviews addressed learner challenges and teaching strategies from the teacher participants' perspectives. Cognitive load theory informed data interpretation and analysis. RESULTS: Participants described taking steps to "diagnose" trainee ability and identify struggling trainees. They described learning challenges related to trainees (performance over mastery goal orientation, low self-efficacy, lack of awareness), tasks (psychomotor challenges, mental model development, tactile understanding), teachers (teacher-trainee relationship, inadequate teaching, teaching variability), and settings (internal/external distractions, systems issues). Participants described employing strategies that could match intrinsic load to learners' levels (teaching along developmental continuum, motor instruction, technical assistance/takeover), minimize extraneous load (optimize environment, systems solutions, emotional support, define expectations), and optimize germane load (promote mastery, teach schemas, stop and focus). CONCLUSIONS: Participants provided insight into possible challenges while learning complex medical procedures with limitations in sensory channels, as well as teaching strategies that may address these challenges at individual and systems levels. Using cognitive load theory, the authors provide recommendations for procedural teachers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".