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Record W2758400885 · doi:10.25071/1916-4467.40286

Curriculum Alignment Among the Intended, Enacted, and Assessed Curricula for Grade 9 Mathematics.

2017· article· en· W2758400885 on OpenAlexvenueno aff
Paolina Seitz

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCognitionCurriculum mappingMathematics educationCurriculum theoryEmergent curriculumQuality (philosophy)Delphi methodCurriculum developmentComputer sciencePsychologyPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

This study examined curriculum alignment among the intended, the enacted, and the assessed curricula in Grade 9 mathematics in two domains: content/operations and cognitive processes. The Program of Studies was used to determine the content/ operations and the Delphi method was used to identify the cognitive levels for the intended curriculum. Classroom observations were used to capture the enacted curriculum. End of unit tests were used to determine the assessed curriculum. Results indicated that curriculum alignment among the intended, enacted and assessed curricula for the mathematics content/operations was high (97% alignment). In contrast, curriculum alignment among the intended, enacted, and assessed curricula for the cognitive processes was low (7.3% alignment). This study makes a contribution towards understanding the quality of the relationship among the intended, enacted, and assessed curricula in mathematics education. The methodological framework provides a model for subsequent research on curriculum alignment among the three components of the education system.

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)0.008
metaresearch head score (Gemma)0.046
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.395
Teacher spread0.337 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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