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Record W2619590783 · doi:10.12973/eurasia.2017.00686a

The Status of Science and Technology Relative to Other School Subjects. Results of a Study Conducted on Primary and Secondary School Students in Quebec

2017· article· en· W2619590783 on OpenAlexaffabout
Abdelkrim Hasni, Patrice Potvin, Vincent Belletête

Bibliographic record

VenueEurasia Journal of Mathematics Science and Technology Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversité du Québec à MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsCurriculumPreferencePsychologyMathematics educationPedagogyMathematics

Abstract

fetched live from OpenAlex

Background:In recent decades, many studies have examined students’ interest in science and technology (S&T) at school. However, few investigations have studied this interest in a manner that accounts for the status that students assign to this subject relative to other subjects in the curriculum. The main objective of this article is to conduct such an examination.Material and methods:This study that used a questionnaire included 2,571 students.Results:reveals that S&T occupies an intermediate position relative to other subjects, with only slight differences between boys and girls. However, there are important differences across school years: 1) S&T is perceived to be increasingly difficult as students’ progress in their schooling; 2) relative preference for S&T decreases during the primary-secondary school transition but subsequently rises; and 3) the relative importance of S&T increases compared with all other subjects as students advance in their education. Significant correlations are observed between the latter two dimensions and students’ intentions to pursue studies or careers in S&T.Conclusions:The hierarchization of the curriculum has a significant impact on students' interest for S & T and on the intention to pursue studies in the field

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)0.007
metaresearch head score (Gemma)0.032
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.010
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.401
Teacher spread0.366 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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