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Record W1994087668 · doi:10.1139/p09-108

Student diversity and the persistence of gender effects on conceptual physics learning

2009· article· en· W1994087668 on OpenAlexaffvenueabout
Andie Noack, Tetyana Antimirova, Marina Milner‐Bolotin

Bibliographic record

VenueCanadian Journal of Physics · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsToronto Metropolitan UniversityUniversity of British Columbia
Fundersnot available
KeywordsDiversity (politics)Mathematics educationPhysicsConceptual changePersistence (discontinuity)Conceptual frameworkPsychologySociologySocial science

Abstract

fetched live from OpenAlex

At Ryerson University every year, hundreds of science and engineering students enroll into required introductory physics courses. The diverse educational histories and demographic characteristics of these students reflect the diversity of Toronto as an urban metropolis and Canada more generally. In this study, we investigate how students’ demographic and educational diversity affects their conceptual learning in introductory university physics. As expected, we found that the completion of a senior high school physics course is positively related to students’ initial conceptual understanding of physics. The unexpected result was that gender remained a predictor of the students’ initial conceptual understanding, even when the completion of high school physics was accounted for. Other demographic characteristics, such as students’ mother tongue and country of birth, seem not to matter. Students’ initial conceptual understanding was the strongest predictor of students’ course learning outcomes, which makes understanding students’ initial differences particularly important. Since learning outcomes in introductory science courses often impact students’ later success in undergraduate science degree programs, these results suggest that the impact of completing high school physics may extend far beyond the first year. The persistence of initial gender differences in students’ learning outcomes remains an ongoing concern.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.093
GPT teacher head0.333
Teacher spread0.240 · 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 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

Citations16
Published2009
Admission routes3
Has abstractyes

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