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Record W2353786278 · doi:10.17105/spr45-1.19-38

Mapping the Relationships Among Basic Facts, Concepts and Application, and Common Core Curriculum-Based Mathematics Measures

2016· article· en· W2353786278 on OpenAlexaff
Robin S. Codding, Sterett H. Mercer, James E. Connell, Catherine A. Fiorello, Whitney L. Kleinert

Bibliographic record

VenueSchool Psychology Review · 2016
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculum-based measurementPsychologyCommon coreCurriculumCore (optical fiber)Mathematics educationDevelopmental psychologyCurriculum developmentComputer sciencePedagogyCurriculum mapping

Abstract

fetched live from OpenAlex

.There is a paucity of evidence supporting the use of curriculum-based mathematics measures (M-CBMs) at the middle school level, which makes data-based decisions challenging for school professionals. The purpose of this study was to examine the relationships among three existing M-CBM indices: (a) basic facts, (b) concepts/application, and (c) measures aligned with Common Core. In a sample of 408 sixth, seventh, and eighth graders, cross-lagged panel analyses were used to examine the temporal relationships of the M-CBM indices over three screening occasions. Latent growth models were also used to investigate (a) patterns of annual growth on the indices and (b) predictive validity of M-CBM level and slope on a high-stakes state assessment. Results indicated that (a) concepts/application scores predicted change in the Common Core measure with mixed evidence that basic facts predicted change on the concepts/application and Common Core tools; (b) growth was positive in all grades but nonlinear in some grades; and (c) fall scores on all measures, but only slopes on the Common Core tool, were related to performance on the high-stakes assessment.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.121
GPT teacher head0.378
Teacher spread0.257 · 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 designTheoretical or conceptual
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

Citations11
Published2016
Admission routes1
Has abstractyes

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