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Record W4414946872 · doi:10.3138/jvme-2025-0034

Teaching Tip: The Role of Cognitive Task Analysis in Teaching Complex Skills Using Canine Fundoscopy as an Example

2025· article· en· W4414946872 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Veterinary Medical Education · 2025
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTask (project management)CognitionRecallTask analysisCognitive loadCognitive skillProtocol analysis

Abstract

fetched live from OpenAlex

Canine ophthalmoscopy is a challenging procedural skill to teach due to multiple unsighted components and patient compliance for examinations. Free recall instruction can unintentionally omit steps and lead to incomplete instruction of skills. Teaching via cognitive task analysis (CTA)-developed teaching protocols can better ensure comprehensive construction of complex skills and deconstruction into simpler steps, which can lead to improved task performance by learners. Our preliminary findings suggest that when teaching complex skills in a single instructional session, use of cognitive task analysis alone may not provide substantial learning benefit. Combining cognitive task analysis with other instructional strategies, such as deliberate practice, feedback, and deconstruction to avoid cognitive overload, may lead to improved learning and retention of complex skills.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.073
GPT teacher head0.468
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it