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Record W1996719374 · doi:10.3138/jvme.0714-067r1

Assessment Literacy: Definition, Implementation, and Implications

2014· article· en· W1996719374 on OpenAlexvenueno aff
Susan Rhind, Jessie Paterson

Bibliographic record

VenueJournal of Veterinary Medical Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
FundersHigher Education Academy
KeywordsPreparednessMedical educationCurriculumCohortPsychologySession (web analytics)LiteracyIntervention (counseling)Formative assessmentMathematics educationPedagogyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This paper explores the current notion of assessment literacy and describes delivery and evaluation of an intervention to support its development in two different cohorts in a veterinary curriculum. Before the intervention, two cohorts (Cohort A, first-year students; Cohort B, third-year students) were surveyed on their expectations and understanding of assessment. The new students expressed uncertainty about their understanding of the assessment process: 51% disagreed that they had a good understanding. As expected, more experienced students had a better understanding, although 30% still disagreed that they had a good understanding of the process. A workshop supporting the development of assessment literacy was implemented, giving students an opportunity to evaluate authentic student work of differing standards. Most of the students in both cohorts found the session helpful in terms of their understanding of different standards in assessments (92% and 97%), and most found it helped them understand how to prepare for the degree examination better (75% and 87%). Student grades were recorded in the workshop involving Cohort B, revealing a large variation in students' ability to grade other students' work accurately, with bias ranging from 22% to -25%. Finally, faculty views on student preparedness for assessment were also explored and compared to student views. Disagreement existed between faculty regarding perceived student preparedness for assessment, and significantly more faculty than students thought that students had a good understanding of how their assessments would be graded. The implications of these results for future work and faculty development are discussed.

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)0.000
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 score0.503
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.076
GPT teacher head0.495
Teacher spread0.420 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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