İLK VE İKİNCİ DİLDE İLK OKUMAYI GELİŞTİRMEYİ ETKİLEYEN FAKTÖRLER
Bibliographic record
Abstract
As globalization becomes more widespread and countries become more multicultural, there is an increased need to understand how to effectively promote the learning of foreign languages. An important part of that process relates to understanding how reading develops when a child is learning a new language. Researchers' focus on assessing students' foreign language oral language proficiency has not provided as much insight as expected into that development. This paper reports on a study of grade one students whose first language was English and who were being schooled in French as a second language. The context provided an ideal case in which to investigate how well variables in addition to oral language proficiency such as phonological awareness, word reading, memory, rapid automatized naming (RAN), and vocabulary predict reading development in a first and second language. Methods involved testing 47 Canadian students at the beginning and end of the school year. Findings revealed that word reading was the sole variable that predicted reading development within and across time and for the first and second language. Phonological awareness and RAN were the next most important variables, followed by memory and vocabulary. Oral language proficiency did not play a significant role. The results of this study suggest that those involved in assessing young children's first and second language reading should focus on word reading, phonological awareness, RAN and memory. As well, findings suggest that oral language proficiency in a foreign or second language may not be an accurate predictor of reading development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".