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Record W2767176779 · doi:10.1080/08975930.2017.1359767

International-Domestic Student Differences in Learning: Use of Classroom Response Systems in China Versus in Canada

2017· article· en· W2767176779 on OpenAlexaffabout
Jeffrey W. Power, Xiaofei Song

Bibliographic record

VenueJournal of Teaching in International Business · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsClickerPsychologyChinaCultural diversityMathematics educationPedagogyPolitical science

Abstract

fetched live from OpenAlex

This study compares the impact of audience response systems (clickers) on the learning experience and classroom behavior of Chinese and Canadian students. Based on differences in student learning styles, which are rooted in the differences in national cultures, we predict that clicker technology will result in a more positive learning experience, and have more impact on classroom behavior in Chinese students than in Canadian students. Our survey results show that, consistent with the findings of prior studies, both groups of students report a positive experience and improved classroom behavior with the use of clickers. Chinese students report a more positive learning experience, but no difference in classroom behavior changes than Canadian students. This study extends the research on clickers by considering the impact of cultural background and shows classroom technology such as clickers can potentially help mitigate the cultural barriers in international business education.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.097
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.083
GPT teacher head0.409
Teacher spread0.326 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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