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Record W2765144371 · doi:10.3138/jvme.0217-024r

Use of Extra-Credit Questions in a Comparative Theriogenology Course

2017· article· en· W2765144371 on OpenAlexvenueno aff
Margaret V. Root Kustritz, Scott Madill

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkSyllabusCurriculumMedical educationFeelingMathematics educationPsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

There is little information in the literature about extra credit in undergraduate coursework and even less about professional schools, including colleges of veterinary medicine. For the present study, syllabi at one veterinary school were reviewed to determine availability of extra credit. Extra credit was offered in 13.0% of courses in the core curriculum (first five semesters), with number of points ranging from 1% to 4% of total course points. Students in a comparative theriogenology course in 1 year of the curriculum (year 1) were offered 12 extra-credit questions over the semester. There was no correlation between number of questions completed and examination scores or final grade for the course. Sixty students (85.7%) agreed or strongly agreed that the extra-credit questions helped them review material from this course and other courses, and 80.0% agreed or strongly agreed that the questions helped them integrate material. The next cohort of students taking the course (year 2) were required to answer one of the questions as an assignment, and were given the option of choosing the question answered. Sixty-six students (79.5%) agreed or strongly agreed that the assignment questions helped them review material from this course and other courses, and 69.9% agreed or strongly agreed that the questions helped them integrate material. Students generally had a better feeling about completing extra-credit questions than they did about completing a required assignment, and this feeling was not due to points received relative to their perceived effort.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.669
GPT teacher head0.618
Teacher spread0.051 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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