Language teaching and settlement for newcomers in the digital age: A blended learning research project
Bibliographic record
Abstract
Blended Learning (BL) in English language learning programs in Canada is defined as the combination of f2f learning with instructor-facilitated use by students of online activities and resources that complement the in-class teaching (Kennell & Moriarty, 2014). Blended Learning is increasingly in demand by students, teachers, and programs (Garrison & Vaughan, 2008), particularly in the Language Instruction for Newcomers to Canada (LINC) program, the English language and settlement program provided by Immigration, Refugees, and Citizenship Canada (IRCC) in Canada (Kennell & Moriarty, 2014). This article explains the findings of a demonstration research project regarding the effects of blended learning in LINC for students, instructors, and the program. The blended approach shows promise for enhancing English language learning and access to LINC classes for newcomers to Canada via technologies important in our digital age. The research findings here regarding the effects of BL in LINC are important in light of the need to enhance accessibility to English language learning for newcomers to Canada and the paucity of research related to BL for English language learning and settlement needs (Kennell & Moriarty, 2014; Lawrence, 2014).
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 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".