Articles Task-Based Language Teaching and English for Academic Purposes: An Investigation into Instructor Perceptions and Practice in the Canadian Context
Bibliographic record
Abstract
English for Academic Purposes (EAP) programs designed to meet postsecond-ary English language proficiency requirements are a common pathway to higher education for students from non-English-speaking backgrounds. Grounded in a Canadian context, this study seeks to examine the prevalence of Task-Based Lan-guage Teaching (TBLT) in EAP, common examples of EAP tasks, and the benefits and drawbacks of this approach for EAP students. EAP professionals (n = 42) were recruited from the membership of TESL Canada, and participants completed a questionnaire on their perceptions of TBLT for EAP. Of those who participated, 69 % reported using TBLT in at least half of their lessons, with 86 % of the par-ticipants indicating that TBLT was suitable for EAP instruction. Further qualita-tive analysis of the data revealed that presentations, essays, and interviews were the top three tasks employed by EAP teachers; the practicality, effectiveness, and learner-centredness of TBLT were its major benefits; and mismatched student expectations, lack of classroom time, and excessive instructor preparation were TBLT’s major drawbacks. Ambiguity regarding what constitutes TBLT was also
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".