Fostering Pragmatic Proficiency: The Influence of Explicit Instruction on Plurilingual EFL Learners’ Mastery of Hedging Devices in Canadian Academic Writing Context
Bibliographic record
Abstract
Hedging academic claims is a crucial aspect of scholarly writing that presents challenges for many non-native English-speaking academic authors. Scholars, such as Hyland (2021), have emphasized the vital role of explicit instructional interventions in raising awareness about hedging devices among Plurilingual non-native English writers. This is particularly relevant considering the nuanced nature of certain hedging devices, characterized by polysemy and polypragmatics. This research aims to investigate the effectiveness of explicit instruction in enhancing the pragmatic competence of non-native English-speaking learners, with a specific focus on the acquisition and application of English modal auxiliaries as hedging mechanisms within an academic context. In this study, a group of 37 non-native English-speaking College students, representing various academic disciplines, were purposefully selected from Sommet College located in Greater Montreal area, and divided into two groups: a control group and an experimental group. The control group received conventional academic writing instruction, while the experimental group underwent explicit instruction on the use of modal auxiliaries for hedging in their academic writing. Both groups completed pretests and post-tests as part of the evaluation process. Analysis of the test scores and t-test results revealed a significant improvement in linguistic and pragmatic proficiency concerning the use of modal auxiliaries for hedging within the experimental group. Moreover, the findings demonstrated the superior performance of the experimental group in employing modal verbs for hedging purposes. The findings of this study have broader implications that reach beyond pedagogical practice, resonating with educational program administrators and curriculum developers. These results underscore the importance of including explicit instruction on hedging devices, particularly modal auxiliaries, to bolster the academic writing skills of non-native plurilingual English-speaking learners.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".