Strengthening Language Instruction for Newcomers to Canada: Learning from Experiences in Saskatoon
Bibliographic record
Abstract
The immigrant population in Saskatoon has grown in the past few decades. With the increase in the number of immigrants in Saskatoon, the federally sponsored program Language Instruction to Newcomers to Canada (LINC) has also been in demand. The purpose of this study was to establish ways in which LINC service providers in Saskatoon can support new ESL LINC instructors to excel in their careers. This study examined the experiences of these instructors in terms of what was expected from them at their workplace, the challenges they faced, and the supports they needed. This research was conducted as an intrinsic case study, considering Saskatoon as a complete case. The guiding theory for this research was the theory on organizational entry by Louis (1980). Due to pandemic, the data were collected through online one-on-one interviews and focus group discussions. The four overarching themes that emerged from the coded data were: motivation to join LINC, expectations from LINC instructors, LINC instructors’ experiences with instruction, and supports for new ESL LINC instructors. Findings of this study revealed that the new ESL LINC instructors face challenges, need guidance in understating the LINC system, and require technical and moral supports. To assist the new LINC instructors in their career, the recommendations are to assign one-on-one mentoring, to offer an annual refresher on CLB and PBLA, to boost the technical support in LINC classrooms, to equip the new instructors on the software and applications being used in LINC classrooms, to consider new LINC instructors’ feedback for implementing blended learning (BL) in CLB 1-4 classes, to support the new instructors on handling trauma in the classrooms, and to give clear instructions to the new LINC instructors on online and BL teaching. For future research, it is suggested to conduct similar research on LINC learners and LINC support staff, which includes managers and administrators for their views on the working conditions and their experiences with LINC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".