Promoting Success in French Immersion: Early French Immersion Teachers' Identification of and Intervention in At-Risk Reading
Bibliographic record
Abstract
The purpose of this research paper was to explore the experiences of Ontario Early French Immersion teachers with regards to reading assessment and intervention for at-risk reading in the French Immersion context. In order to accomplish this, I completed a qualitative research study using semi-structured interviews with three grade one French Immersion teachers. Some of the findings which came from these interviews included that teachers perceive various reasons as to why a child may not be well suited for French Immersion, that parent involvement can be a double-edged sword, and that even if teachers use best-practices, as suggested by research, for reading assessment and intervention, they may continue to experience difficulty. From these findings, I have suggested several implications for the educational community including that teachers do not perceive that they have adequate reading assessment or intervention materials available for their French Immersion learners, and that these teachers do not perceive there is adequate special education support. Based on this, recommendations can be made including for school boards to allocate more funding and resources to special education in French Immersion, and that curriculum resource developers should create new reading materials which are specific to the Early French Immersion learning context.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".