Practising what we preach: using cognitive load theory for workshop design and evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Theory-based instructional design is a top priority in medical education. The goal of this Show and Tell article is to present our theory-driven approach to the design of instruction for clinical educators. We adopted cognitive load theory as a framework to design and evaluate a series of professional development workshops that were delivered at local, national and international academic conferences in 2014. We used two rating scales to measure participants' cognitive load. Participants also provided narrative comments as to how the workshops could be improved. Cognitive load ratings from 59 participants suggested that the workshop design optimized learning by managing complexity for different levels of learners (intrinsic load), stimulating cognitive processing for long-term memory storage (germane load), and minimizing irrelevant distracters (extraneous load). Narrative comments could also be classified as representing intrinsic, extraneous, or germane load, which provided specific directions for ongoing quality improvement. These results demonstrate that a cognitive load theory approach to workshop design and evaluation is feasible and useful in the context of medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it