Optimising cognitive load and usability to improve the impact of e-learning in medical education
Bibliographic record
Abstract
E-learning has the potential to support the development of expertise in clinical reasoning by being able to provide students with interactive learning experiences, exposure to multiple cases, and opportunities for deliberate practice with tailored feedback. This review focuses on two important but underappreciated factors necessary for successful e-learning, i.e. the management of the learner’s cognitive load and the usability of the technology interface. Cognitive load theory views learning as involving active processing of information by working memory via separate visual and auditory channels. This system is of very limited capacity and any cognitive load that does not directly contribute to learning is considered extraneous and likely to impede learning. Researchers in cognitive load theory have provided evidence-based instructional design principles to reduce extraneous cognitive load and better manage the cognitive processing necessary for learning. Usability is a concept from the field of human-computer interaction which describes how easy technology interfaces are to use, and is routinely evaluated and optimised in the software development industry. This is seldom the case when e-learning resources are developed, especially in the area of medical education. Poor usability limits the potential benefit of educational resources, as learners experience difficulties with the technology interface while simultaneously dealing with the challenges of the content presented. Practitioners in the field of human-computer interaction have provided guidelines and methods for evaluating and optimising the usability of e-learning materials. The fields of cognitive load theory and human-computer interaction share a common goal in striving to reduce extraneous cognitive load. The load induced by poor usability of e-learning materials can be viewed as a specific component of extraneous cognitive load, adding to any load resulting from poor instructional design. The guidelines from these two fields are complementary and, if correctly implemented, may substantially improve the impact of our e-learning resources on the development of the clinical reasoning skills of students.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".