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The Presence of Gender Disparity on the Force Concept Inventory in a Sample of Canadian Undergraduate Students

2017· article· en· W2597119501 on OpenAlexaffvenueabout
Magdalen Normandeau, Seshu Iyengar, Benedict Newling

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGender gapDisciplinePsychologyConceptual changeMathematics educationMultiple choiceSocial psychologySignificant differenceSociologyMathematicsSocial scienceStatisticsDemographic economics

Abstract

fetched live from OpenAlex

Concept inventories (CI) are validated, research-based, multiple-choice tests, which are widely used to assess the effectiveness of pedagogical practices in bringing about conceptual change. In order to be a useful diagnostic tool, a CI must reflect only the student understanding of the conceptual material. The Force Concept Inventory (FCI) is arguably the standard for testing conceptual understanding of Newtonian mechanics. Studies in the United States and United Kingdom have shown the existence of a gender gap in FCI scores and gains between male and female students. This study aimed to examine whether such a gap exists for Canadian students at a mid-sized university. Four-hundred and thirty-four men and 379 women taking first-term introductory physics courses from the past nine years were assessed with the FCI prior to and after receiving instruction. A gender gap in the pre-instruction and post instruction scores was revealed in favour of male students (p < 0.01). There also existed a gender disparity in the learning gains between the two tests, where males had significantly higher gains (p < 0.01), although the effect size was small. Further analysis found that both male and female students who studied in classes that included interactive engagement methods had somewhat higher gains than students in traditional lecture courses, but that the interactive engagement methods did not eliminate the gender gap between male and female students (p < 0.01). Our results sound a cross-disciplinary note of caution for anyone using concept inventories as research or self-assessment tools. Les inventaires de concepts sont des questionnaires à choix multiples validés basés sur la recherche qui sont largement utilisés pour évaluer l’efficacité de pratiques pédagogiques en instaurant un changement conceptuel. Afin d’être des outils diagnostiques utiles, les inventaires de concepts doivent refléter uniquement la compréhension qu’a l’étudiant de la matière conceptuelle. Le « Force Concept Inventory (FCI) » est sans aucun doute la norme pour tester la compréhension conceptuelle de la mécanique newtonienne. Des études menées aux États-Unis et au Royaume-Uni ont montré l’existence d’un écart hommes-femmes dans les résultats du FCI et ainsi que dans les acquis. Cette étude vise à déterminer si un tel écart existe parmi les étudiants canadiens dans une université de taille moyenne. Un total de 434 hommes et 379 femmes inscrits à un premier cours d’introduction à la physique au fil des neuf dernières années ont été évalués avec le FCI au tout début et à la toute fin de la session. Les résultats ont révélé un écart hommes-femmes dans les résultats des tests, aussi bien ceux effectués avant le cours que ceux après le cours, en faveur des étudiants masculins (p < 0.01). Ils ont également révélé une disparité entre hommes et femmes dans les acquis d’apprentissage entre les deux tests : les hommes avaient atteint des acquis plus élevés (p < 0.01), bien que l’ampleur de l’effet ait été faible. Des analyses complémentaires ont montré que tant les hommes que les femmes qui avaient étudié dans des classes qui comprenaient des méthodes d’engagement interactif avaient obtenu davantage d’acquis que les étudiants qui avaient suivi des cours magistraux traditionnels, mais que les méthodes d’engagement interactif n’avaient pas éliminé l’écart hommes-femmes parmi les étudiants (p < 0.01). Nos résultats présentent une mise en garde à l’intention de ceux qui utilisent les inventaires de concepts en tant qu’outils de recherche ou d’auto-évaluation, quelle que soit leur discipline.

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)0.020
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0150.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.175
GPT teacher head0.421
Teacher spread0.247 · 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.

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

Citations4
Published2017
Admission routes3
Has abstractyes

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