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Record W2024099069 · doi:10.3138/jvme.33.3.455

A Comparison of Linear, Fixed-Form Computer-Based Testing versus Traditional Paper-and-Pencil-Format Testing in Veterinary Medical Education

2006· article· en· W2024099069 on OpenAlexvenueno aff
Lillian C. Folk, J. Zachary March, Robin D. Hurst

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPencil (optics)CurriculumTest (biology)PerceptionComputer scienceMedical educationMathematics educationPsychologyMedical physicsMedicinePedagogyEngineeringBiology

Abstract

fetched live from OpenAlex

Computerized testing has made significant inroads into veterinary education. Traditional paper-and-pencil examination formats are being replaced by computer-based testing (CBT). Computer-administered, fixed-form tests, because they mimic most closely the familiar fixed-response paper-and-pencil test formats, might intuitively seem to be inherently equivalent to their paper-and-pencil counterparts. However, research examining test-mode effects on student performance presents a very mixed picture. Additionally, students often report that they feel their performance is adversely affected by CBT and that their grades on the computer-based exams are lower than they would have been on the more familiar paper-and-pencil format. In order to address student perceptions of negative impact and the mixed nature of the published research results on the topic, a study was conducted to assess whether the transition from paper-and-pencil to equivalent linear CBT exams did, in fact, affect students' examination scores. This study found no evidence for significant test-mode effects on student scores as a result of the introduction of computer-based testing into the veterinary curriculum.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.492
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.535
GPT teacher head0.541
Teacher spread0.005 · 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

Citations17
Published2006
Admission routes1
Has abstractyes

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