Do They Make a Difference? The Impact of English Language Programs on Second Language Students in Canadian Universities
Bibliographic record
Abstract
Few studies have investigated the impact of English language programs on second language (L2) students studying in Canadian universities (Cheng & Fox, 2008; Fox, 2005, 2009). This article reports on questionnaire responses of 641 L2 students studying in 36 English language programs in 26 Canadian universities. The researchers identified programs by their activity emphasis as either English as a second language ( ESL ) or English for academic purposes ( EAP ). Activity emphasizing speaking, social interaction, and general language development was viewed as ESL , whereas activity that emphasized academic reading, writing, and language development was considered EAP . The researchers used structural equation modeling procedures to examine the network of relationships between language program emphasis and participants' background characteristics in influencing academic and social engagement. A model of moderated mediation (Wu & Zumbo, 2008) was confirmed; that is, language program activities were found to account for variation in strategies which mediated academic and social engagement. However, the impact was moderated (lessened or strengthened) by three personal background factors: anxiety, stress, and motivation. This study refines our understanding of the positive impact of ESL and EAP programs on L2 university students' academic and social engagement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".