Lessons Learned from a Discipline-Specific Language Support Initiative for Multilingual Students (MLSs) in a Foundational Course in Health Sciences: A Mixed-Methods Case Study from a Western Canadian University
Bibliographic record
Abstract
This mixed methods explanatory case study intends to explain low utilization of a joint initiative to improve language skills targeted to English as an additional language (EAL)/multilingual students (MLSs) in a disciplinary context. Considering the importance of discipline-specific language and literacy skills, a university health sciences faculty and an English language learning centre at a western Canadian University collaborated on a joint initiative to support EAL/MLSs. Language support services, such as an extra semester-long adjunct language tutorial, drop-in language services, and language support files uploaded on the Canvas Learning Management System (LMS) were provided for students enrolled in a first-year introductory-level health sciences course. A comparison of a pre-test and post-test Post-Entry Language Assessment (PELA) revealed improvements in both writing skills and perceived language skills. However, the discovery of the underutilization of language support services prompted a sequential explanatory mixed methods case study to identify learners’ reasons for low participation. Findings from the quantitative survey and qualitative interviews are shared along with recommendations for improving language support service utilization.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".