Cognitive Load Theory: A Critical Lens for Examining Procedural Skills Training in the Health Professions : The Case of Gastrointestinal Endoscopy
Bibliographic record
Abstract
This thesis seeks to address challenges inherent to procedural teaching and learning in the health professions by considering the lens of cognitive load theory (CLT). CLT is a cognitive learning theory focused on limitations of human working memory. The combined activities of the working memory are referred to as “cognitive load”. Intrinsic load occurs when learners accomplish essential task elements. When learners use working memory resources to create learning schema, germane load occurs. Extraneous load occurs whenever learners use working memory resources in unproductive ways; extraneous load never contributes to learning. When working memory is overloaded, learning and performance suffer. We studied gastrointestinal (GI) endoscopy as an exemplar procedural setting. Chapter 1 provides an overview of the challenges of procedural skills training, theoretical tenets of CLT, and rationale for the planned research. We started the thesis research by developing a psychometric instrument – the Cognitive Load Inventory for Colonoscopy (CLIC) – to measure intrinsic, extraneous and germane load during colonoscopy training (Chapter 2). We followed a rigorous process to develop the CLIC and tested it among 477 colonoscopy learners; extensive evidence for validity is presented. In Chapter 3, we analyzed a large body of additional data obtained from the 477 participants in Chapter 2, including features of learners, the tasks/patients, procedural settings/environments, and supervisors. We developed multivariate models of features associated with intrinsic, extraneous and germane load. We next directed efforts towards how specific teaching approaches might impact cognitive load by performing a scoping review (performed as a BEME review) (Chapter 4). The 116 diverse studies yielded multiple theoretical implications and practical recommendations. We next wanted to know how experts approached procedural teaching, and so we designed a qualitative study interviewing 22 experienced endoscopy teachers in the United States, Canada and the Netherlands (Chapter 5). Participants described learner challenges related to learners, tasks, settings and teachers. Participants reported using teaching strategies that aligned with CLT. Finally, we performed a mixed methods study, observing actual colonoscopies performed by teachers and learners (Chapter 6). We observed 515 instances of 11 teaching activities during 10 colonoscopies. Most teaching activities benefitted goals of CLT. However, even beneficial teaching activities were perceived to induce extraneous load when the attending taught excessively. The thesis concludes with the Discussion in Chapter 7, which synthesizes the research completed with other literature related to CLT and procedural skills training. Chapter 7 specifically addresses several major contributions of this work to the literature including: measurement of cognitive load subtypes, conceptualization of germane load, what contributes to intrinsic, germane and extraneous load in procedural skills training settings, and how to leverage teaching and curricular design to balance and optimize procedural learners’ cognitive load. Overall this thesis makes substantial theoretical and practical contributions to our understanding of teaching procedural skills in the health professions through the lens of CLT. We hope that is serves as a stimulus for further research and teaching and curricular innovations as we strive to optimize learning in this challenging yet rewarding domain in the health professions.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".