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Record W2119408444 · doi:10.1080/17439884.2011.556123

Gender differences in student performance in large lecture classrooms using personal response systems (‘clickers’) with narrative case studies

2011· article· en· W2119408444 on OpenAlexaboutno aff
Hosun Kang, Mary Lundeberg, Bjørn H. K. Wolter, Robert C. delMas, Clyde Freeman Herreid

Bibliographic record

VenueLearning Media and Technology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsClickerNarrativeMathematics educationNarrative reviewPsychology

Abstract

fetched live from OpenAlex

This study investigated gender differences in science learning between two pedagogical approaches: traditional lecture and narrative case studies using personal response systems (‘clickers’). Thirteen instructors of introductory biology classes at 12 different institutions across the USA and Canada used two types of pedagogy (Clicker Cases and traditional lecture) to teach eight topic areas. Three different sets of multiple regression analysis were conducted for three separate dependent variables: posttest score, change in score from posttest to final, and transfer score. Interactions between gender and pedagogical approach were found across the three analyses. Women either performed better with Clicker Cases, or about the same with either instructional method, but men performed markedly better with lectures in most topic areas. Our results suggest that men and women experience two pedagogical approaches—Clicker Cases and lectures—differently, and that Clicker Cases are more favorable for women than for men.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.097
GPT teacher head0.370
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations37
Published2011
Admission routes1
Has abstractyes

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