Dynamic Assessment of Early French Immersion Literacy Learning Competencies
Bibliographic record
Abstract
French Immersion programming in Canada is not always an inclusive environment for all learners. Students with language disabilities or delays are often placed into English-only programming when difficulties arise in French immersion programming. This study aimed to establish a method of identification of reading difficulties, in either language, early in the reading process. Such an assessment would allow educators to intervene and assist these students, and all students, with reading and vocabulary development in their second language of French before these language issues can negatively affect learning.\nEssential to language learning in immersion programs is the development of speech perception and lexical specificity, defined as the knowledge of how words should sound in a language. Dynamic assessments in both French and English were used as they focus on how well a student can learn a concept. This project examined second language (L2) French learning in a dynamic way to predict literacy learning in children who are not yet proficient readers in English, their first language (L1). The particular skills of phonological awareness and vocabulary development in both L1 and L2 were examined.\nA one-year longitudinal study was conducted to investigate the language abilities of children in French immersion in grade 2. In L1, dynamic assessments were better predictors of vocabulary than static assessments. In L2, static assessments were better predictors of vocabulary than dynamic assessments. In L1, lexical specificity, word reading, phonological awareness (elision), and rapid naming predicted word reading. In L2, phonological awareness (elision) in both French and English, and French word reading predicted word reading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".