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Record W2738939902 · doi:10.5430/ijhe.v6n4p94

Class Participation of International Students in the U.S.A.

2017· article· en· W2738939902 on OpenAlexvenueno aff
Özgür Yıldırım

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Mathematics educationPsychologyChinaGraduate studentsSignificant differenceMedical educationPedagogyPolitical scienceMedicineComputer scienceLaw

Abstract

fetched live from OpenAlex

Following a qualitative research design, this study aims to explore the differences between international and American graduate students in terms of their class participation. The data for the study were collected from a graduate-level class at a university in upstate New York. There were seven participants in the study, three of the participants were American students, and the other four participants were international students, two from China, one from Iran, and one from Sudan. Main source of data was classroom observations. Three classroom sessions were observed and field notes were taken during observations. There were two phases of the data analysis process. During the first phase, field notes were reviewed after observations and five general categories of classroom participation were identified. During the second phase, the data were further analyzed in order to see the differences between American and international students in terms of their class participation according to these five categories. Results of data analysis revealed three main differences between American and international students’ class participation in the observed graduate classroom setting. The first main difference involved the short answer/example and explanation categories, the second main difference involved the questions to the instructor/classmates for clarification/repetition and the questions to raise discussion categories, and the last main difference involved the answer/explanation assigned by the instructor category.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.149
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.143
GPT teacher head0.572
Teacher spread0.429 · 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 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

Citations9
Published2017
Admission routes1
Has abstractyes

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