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Record W4389324451 · doi:10.55016/ojs/ajer.v64i1.56472

Deciding Whether to Respond: A Latent Class Analysis of Nonresponse on Ontario’s Grade 9 Assessment of Mathematics

2018· article· en· W4389324451 on OpenAlexafffundvenueabout
Ruth A. Childs, Orlena Broomes, Monique Herbert

Bibliographic record

VenueAlberta Journal of Educational Research · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTest (biology)PsychologyClass (philosophy)Multiple choiceCovariateLatent class modelMathematics educationSocial psychologyMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

This study investigates nonresponse on Ontario’s Grade 9 Assessment of Mathematics – in particular, whether or not students responded to all multiple-choice or all open-response items in two test booklets. Whether students responded to all items of one type (multiple-choice or open-response) by booklet (for the first or second day of testing) was modeled, with and without proportion correct scores by item type as covariates, using latent class analysis. Both a 3-class model without the covariates and a 4-class model with the covariates but without direct effects distinguished among students who responded to all items, students who left both multiple-choice and open-response items blank, and students who left only open-response items blank. The results suggest that deciding to respond to all open-response items is distinct from deciding to respond to all multiple-choice items. Attitudes toward mathematics were also more related to the decision to respond to all open-response items than to the decision to respond to all multiple-choice items. Cette étude porte sur l’absence de réponse au test de mathématiques pour la 9e année en Ontario –nous cherchions notamment à savoir si les élèves avaient répondu à toutes les questions à choix multiples ou bien à toutes les questions ouvertes dans deux livrets d’examen. Une analyse de structure latente a permis la modélisation du comportement des élèves, à savoir s’ils avaient répondu à tous les items d’un type (questions à choix multiples ou questions ouvertes) dans un livret (lors du premier ou deuxième jour des tests) avec et sans des scores reflétant la proportion de bonnes réponses par type d’items comme covariables. Un modèle de classe 3 sans les covariables ainsi qu’un modèle de classe 4 avec les covariables mais sans effets directs ont tous les deux fait la distinction entre les élèves qui avaient répondu à tous le items, les élèves qui n’avaient ni répondu à certaines questions à choix multiples ni à certaines questions ouvertes et les élèves qui n’avaient pas répondu à certaines questions seulement dans le cas des questions ouvertes. Les attitudes face aux mathématiques ont également joué un plus grand rôle dans la décision de répondre à toutes les questions ouvertes que dans celle de répondre à toutes les questions à choix multiples.

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)0.029
metaresearch head score (Gemma)0.381
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.381
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.008
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.691
GPT teacher head0.623
Teacher spread0.068 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes4
Has abstractyes

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