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Record W2800413136 · doi:10.29333/ejmste/90267

Comparison of Science and Engineering Concepts in Next Generation Science Standards with Jordan Science Standards

2018· article· en· W2800413136 on OpenAlexaff
Ahmad Qablan

Bibliographic record

VenueEurasia Journal of Mathematics Science and Technology Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Alberta
FundersMcKnight Foundation
KeywordsNext Generation Science StandardsPhysical scienceLearning standardsCurriculumMathematics educationSpace ScienceScience educationEngineering ethicsEngineeringMathematicsPedagogySociology

Abstract

fetched live from OpenAlex

The purpose of this Science Standards Content Crosswalk study is to compare the degree of alignment between Next Generation Science Standards (NGSS) and Jordan’s science standards in K-8. A team of 5 science educators worked together to review each NGSS standard and decide whether there is a conceptual match for it in current Jordan science standards. Rigorous content analysis and interpretation approach was used to make decisions about matches between both sets of standards. Results revealed significant misalignments between the science learning outcomes identified by NGSS and those of Jordan. Results also showed that, Physical Sciences concepts had the highest percentage (58%) of not addressed concepts in 3-5 grade band followed by Earth and Space Sciences (42%) and Life Sciences concepts (36%). However, the highest percentage of not addressed concepts in grade band 6-8 were Earth and Space Sciences (58%) followed by Physical Sciences (46%) and Life Sciences (36%) concepts. This finding can support projections of the needs for new instructional materials and for subject-specific teachers’ professional development. The results also provide a clear direction for the newly established national center for curriculum to revise the national science standards and curricula.

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)0.024
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.012
Science and technology studies0.0020.047
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.056
GPT teacher head0.439
Teacher spread0.383 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

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