Doing a group presentation: Negotiations and challenges experienced by five Chinese ESL students of Commerce at a Canadian university
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".