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Record W2064299564 · doi:10.7202/1025006ar

Validation of competence models for developing education standards

2014· article· en· W2064299564 on OpenAlexvenueno aff
Erich Ramseier

Bibliographic record

VenueMesure et évaluation en éducation · 2014
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
FundersUniversität ZürichUniversité de Lausanne
KeywordsCompetence (human resources)Rasch modelHarmonizationComputer scienceMathematics educationPsychologySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Following PISA 2000, Switzerland launched a project called HarmoS (Harmonization of obligatory School) to develop binding education standards for compulsory education in the three language regions of the country. These standards for four subject areas are based on models of competence. Part of the development is an empirical study to validate these competence models. The present article describes the design, methods, and some results of this validation study. It also discusses the study’s usefulness for defining the standards and the consequences of some methodological choices, particularly the heuristic application of the Rasch model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.157
metaresearch head score (Gemma)0.279
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.157
Threshold uncertainty score0.830

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1570.279
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.004
Science and technology studies0.0020.004
Scholarly communication0.0050.006
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.423
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2014
Admission routes1
Has abstractyes

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