MétaCan
Menu
Back to cohort
Record W4320400475 · doi:10.53841/bpsadm.2015.7.4.13

Testing dyslexic candidates using CAT administration: Taking time out of the test equation

2015· article· en· W4320400475 on OpenAlexaff
Sean Keeley, Parkes Jo

Bibliographic record

VenueAssessment and Development Matters · 2015
Typearticle
Languageen
FieldComputer Science
TopicIntelligent Tutoring Systems and Adaptive Learning
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsTest (biology)Computerized adaptive testingAdministration (probate law)DyslexiaDivision (mathematics)PsychologyComputer scienceAudiologyCognitive psychologyDevelopmental psychologyReading (process)MedicineArithmeticPsychometricsMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Here we address the question as to whether or not CAT (Computer-adaptive testing) administration can help to lessen group differences between dyslexic and non-dyslexic candidates by providing all candidates with all of the time they require. The article is a summary of our findings from a study presented at the 2015 BPS Division of Occupational Psychology Conference.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.113
GPT teacher head0.319
Teacher spread0.205 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAssessment and Development MattersSame topicIntelligent Tutoring Systems and Adaptive LearningFrench-language works237,207