The Challenges of Conducting Research in Diverse Classrooms: Reflections on a Pragmatics Teaching Experiment
Bibliographic record
Abstract
For researchers, the typical way of determining whether a pedagogical innovation works is by conducting an experiment. In migrant settings, however, experiments are more challenging to carry out due to the diversity of the learner population. Unfortunately, how to deal with these challenges is not addressed in a practical way in research methods textbooks, which typically provide a normative view of the research process. This paper aims to draw attention to the realities of classroom research carried out in the Language Instruction for Newcomers to Canada (LINC) setting. These classes consist of adult immigrants and refugees from a wide range of cultural, linguistic and educational backgrounds. We illustrate how this diversity along with other characteristics of LINC programs impact the decision-making of the researcher with respect to a pedagogical experiment focused on pragmatics. The study compared a formula-enhanced approach to teaching speech acts to the more mainstream approach aimed at raising learners’ meta-pragmatic awareness about speech act behaviour. The pre-post-delayed-post-test gains appear to favour the Formula group, but the interpretability of these results is compromised by the fact that the composition of the two classes was very different. Discussion of the limitations of this case study feeds into a broader consideration of the implications for classroom research of linguistic and cultural diversity typical of L2 educational contexts like LINC.
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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 | 0.288 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| 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".