MétaCan
Menu
Back to cohort
Record W4378193990 · doi:10.3138/jvme-2023-0011

Student Use and Perceptions of Embedded Formative Assessments in a Basic Science Veterinary Program

2023· article· en· W4378193990 on OpenAlexvenueno aff
Lewis A. Baker, Dona Wilani Dynatra Subasinghe

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentThematic analysisCurriculumPreferenceProcess (computing)Medical educationPsychologyMathematics educationPerceptionComputer scienceQualitative researchPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

This work describes the implementation of online, timed, closed-book formative assessments across several modules of a first-year undergraduate veterinary program. This process does not require significant time investment since it can be implemented into existing programs of study. Students were surveyed on how they used these formative assessments for learning and, overall, were overwhelmingly positive about the opportunity to practice and receive feedback on their performance. Quantitative statistics on preferences as well as qualitative thematic analysis of open, free-text questions revealed clear preferences in how they choose to engage with the assessments for learning, as well as how they prefer assessments to be administered. Students were positive about the online nature of the exams and prefer formative assessments to be distributed across the teaching semesters without any time restrictions, allowing them to be completed as and when they choose. Immediate feedback in the form of model answers is the students' preference, although some value signposting to relevant resources for further research. Furthermore, students reported that they want more questions and tests to complement their learning, and overwhelmingly rely on guided and structured activities for learning and revision, which will need to be balanced with opportunities to develop critical thinking and independent learning skills when studying in a professional course, given students are not likely to default into such behavior. This work models a process many curriculum designers have undergone and continue to undergo in higher education as online, hybrid, and blended approaches to teaching have received renewed interest.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.122
GPT teacher head0.528
Teacher spread0.406 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical EducationSame topicStudent Assessment and FeedbackFrench-language works237,207