Intensive English for Academic Purposes: A Curriculum Designed and Developed for Local English Language Learners Entering University
Bibliographic record
Abstract
In recent years, the demographic profile of Canada has undergone rapid changes as increasing numbers of immigrants have chosen to call this country home. The Canadian K-12 and post-secondary educational systems have seen an enormous influx of language minority (LM) learners: 25% of the student body in Calgary’s K-12 systems have or have had EAL coding, and the proportion of local LM students in tertiary settings is even higher. Previous research has demonstrated that despite graduating in high numbers, many of these students struggle with academic language proficiency and experience difficulties in post-secondary settings, as exhibited in low GPAs and high rates of failed courses. In response to the academic needs of the ‘new mainstream’ of university entrants, the iEAP (intensive English for Academic Purposes) curriculum was designed, developed, and implemented as an intervention between high school and university. This thesis describes the iEAP curriculum and presents short-term outcomes based on pre- and post-testing and long-term outcomes based on subsequent academic performance. The short-term quantitative findings demonstrate that the iEAP participants displayed high rates of improvement on a variety of academic language measures. The long-term findings on academic performance were more difficult to measure due to confounding variables, and indicate that there was a wide range of academic outcomes in the years following participation in iEAP. Qualitative findings are also used to explain, corroborate, and better understand the quantitative findings and present the participants’ experiences in iEAP and university. The conclusion of this study is that growth in academic language proficiency can occur in a short period of time when well-designed and relevant curricula are put into practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".