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Record W4389399118 · doi:10.56916/jesi.v1i2.575

Utilizing Standards in Competency Assessment for General Education – A Case Study of British Columbia, Canada and Lessons for Vietnam

2023· article· en· W4389399118 on OpenAlexaboutno aff
Thi Thanh Huong Duong, Truong An Vu

Bibliographic record

VenueJournal of Education For Sustainable Innovation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)Competency assessmentStandards-based assessmentMedical educationAssessment centerStandardized testAdaptation (eye)Educational assessmentPolitical sciencePsychologyMedicineApplied psychologyMathematics education

Abstract

fetched live from OpenAlex

The utilization of standards in competency assessment is a critical consideration in contemporary education. This study examines the experience of British Columbia in employing standardized criteria for assessing competencies in general education, drawing valuable insights for Vietnam. This paper analyzes the competency assessment frameworks and methods adopted by the province of British Columbia, Canada. It delves into the adoption and adaptation of standards, the alignment of assessment processes with predefined criteria. Findings highlight that incorporating standards in competency assessment enhances assessment validity and transparency. Standards facilitate a more consistent and reliable assessment process, while also aiding the establishment of clear performance expectations for learners. These findings offer valuable lessons for Vietnam's education system, emphasizing the potential benefits and challenges of integrating standardized criteria into competency assessment practices.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.430
Teacher spread0.397 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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