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Record W4292229937 · doi:10.3138/jvme-2021-0167

Effectiveness of a Student-Developed Instructional Video in Learning the Anatomy of the Equine Distal Limb

2022· article· en· W4292229937 on OpenAlexvenueno aff
M. Cathleen Kovarik, Tamara S. Hancock

Bibliographic record

VenueJournal of Veterinary Medical Education · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsComprehensionMedical educationVideo recordingAffect (linguistics)Interactive videoPsychologyMedicineMultimediaComputer science

Abstract

fetched live from OpenAlex

The anatomy of the equine distal limb (EDL) is both complex and important to veterinary clinical practice. First-year veterinary students (VM1s) often struggle to adequately understand it. Two third-year veterinary students collaborated with instructors to create an instructional video to facilitate first-year students’ comprehension of EDL anatomy. The video was offered to all VM1s. Learning outcomes were assessed via practical exams. Exam scores on EDL structures were compared between students who did ( video) and students who did not ( no video) watch the video. Students’ laboratory experiences and confidence were evaluated with a post-exam survey. The third-year students documented their experiences while producing the video. Eighty percent of VM1s viewed the video; 91% rated the video as very valuable. The video improved student confidence during the practical exam by 9%, and 89% of surveyed students indicated the video positively impacted their exam grade. One item score was significantly improved in the video group ( p < .001), as was the score of the five questions combined ( p < .001). As expected, overall practical exam scores were not statistically different. Student collaborators indicated that participation reinforced their knowledge while enhancing their professional development. Student collaboration was a beneficial strategy for instructional support development that positively impacted student affect and also generated opportunities for the involved students’ professional growth.

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.

How this classification was reachedexpand

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)0.001
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 score0.736
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.338
Teacher spread0.320 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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