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Record W2939645427

The Integration of Science Process Skills: A Content Analysis of the 2016 Ontario Kindergarten Program

2018· article· en· W2939645427 on OpenAlexaffabout
Roxana Yanez-Gonzalez

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScience educationContext (archaeology)Content analysisProcess (computing)Mathematics educationPsychologyContent (measure theory)Science learningPedagogyComputer scienceSociologySocial scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Prior works has demonstrated that engaging young children in meaningful science experiences leads to positive attitudes towards science and to a better understanding of formal science concepts. In particular, implementing activities that support the development of science process skills such as observing, inferring and predicting in the early years has been proven as essential for students to cultivate the abilities required to advance scientific inquiry in the later grades. This study examined how science process skills and science in general are represented throughout the 2016 Ontario Kindergarten Program with the purpose obtaining an understanding of the science content present in the document. The results of the study obtained by means of content analysis revealed an absence of explicit mentions of science education in the document contrasting with a large number of mentions of science process skills. However, the identified references to science process skills were mostly found without a context and scattered all over the document, and not explicitly found as elements of cohesive units of science education. The results of the study put in evidence the need to strength the science content in the 2016 Ontario Kindergarten Program.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.107
GPT teacher head0.400
Teacher spread0.294 · 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 designQualitative
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 routes2
Has abstractyes

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