MétaCan
Menu
Back to cohort
Record W2132775121 · doi:10.1177/1362168809353872

Doing a group presentation: Negotiations and challenges experienced by five Chinese ESL students of Commerce at a Canadian university

2010· article· en· W2132775121 on OpenAlexaboutno aff
Luxin Yang

Bibliographic record

VenueLanguage Teaching Research · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)ConversationPsychologyClass (philosophy)NegotiationEnglish for academic purposesGroup workTask (project management)PedagogyCitizen journalismPeer feedbackMathematics educationSociologyComputer science

Abstract

fetched live from OpenAlex

This study investigated the negotiations and challenges experienced by five Chinese ESL (English as a second language) students of Commerce through their engagement in an academic presentation in a regular content course at a Canadian university. Multiple sources of data were collected, including interviews, class observations, group discussions, emails, field notes, assignment drafts, and course materials. Data analysis showed that, in their preparatory activities outside the classroom, students employed peer—peer dialogues (group discussions and email exchanges) to clarify the task requirements, generate ideas, seek peer comments, and coach rehearsals. However, the academic presentation — especially open discussion part — was a great challenge to them, related to their underdeveloped English conversation ability, their unfamiliarity with participatory communication modes in the Canadian classroom, and their limited experience with group work. To compensate for their limited sense of conversational abilities, they chose to present a thorough case analysis rather than engage the class in discussion through their presentation as expected by the instructor. The students eventually understood the norms of academic presentation in this content course through their observation of the instructor’s reaction to their presentation in class and subsequent presentations of their classmates, and they realized that they should approach assignments according to the requirements. Implications drawn from these findings are discussed.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.264
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0280.008
Scholarly communication0.0060.002
Open science0.0020.005
Research integrity0.0020.003
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 head0.039
GPT teacher head0.346
Teacher spread0.307 · 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 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

Citations64
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueLanguage Teaching ResearchSame topicEFL/ESL Teaching and LearningFrench-language works237,207