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Record W2923725345 · doi:10.3389/feduc.2019.00020

Development and Examination of a Tool to Assess Score Report Quality

2019· article· en· W2923725345 on OpenAlexaffabout
Mary Roduta Roberts, Chad M. Gotch

Bibliographic record

VenueFrontiers in Education · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneralizability theoryReliability (semiconductor)Scale (ratio)Quality (philosophy)StakeholderRating scaleStandards for Educational and Psychological TestingPsychologySample (material)Applied psychologyAccountabilityMedical educationMedicinePolitical scienceHigher educationPublic relationsGeography

Abstract

fetched live from OpenAlex

The need for quality in score reporting practices is represented in the Standards for Educational and Psychological Testing (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education, 2014). The purpose of this study was to introduce a ratings-based instrument to assess the quality of score reports and examine the reliability of scores obtained. Quality criteria were derived from best-practices published within the literature (Hambleton & Zenisky, 2013). The rating scale was used to assess a sample of 40 English-language individual student score reports for elementary-level accountability tests representing 42 states and 5 provinces in the United States and Canada. A two-facet generalizability study (i.e., sr x d x r) was completed with an overall reliability coefficient of G=0.78. Application of the rating scale may provide a means to support empirical study of relationships between score report quality and stakeholder outcomes including interpretation, use, and impact.

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)0.007
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 score0.381
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
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 head0.220
GPT teacher head0.504
Teacher spread0.284 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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