Self-Concepts in Reading and Spelling among Mono- and Multilingual Children: Extending the Bilingual Advantage
Bibliographic record
Abstract
Cognitive representations and beliefs are what comprise an individual's self-concept. A positive self-concept is related to and influences academic achievement, and the relationship between a domain-specific self-concept and achievement in the same domain is positive and strong. However, insufficient attention has been paid to these issues among multilingual children. More importantly, since instruction strongly contributes to the development of metacognition and executive functions (EFs), and since the bilingual advantage hypothesis holds that the constant management of multiple languages entails benefits for EF, we bring together these important issues in the present study. We examine the relationship between domain-specific self-concepts and standardized assessment of reading and spelling competences against the background of potential differences in self-concept between monolingual and multilingual German children. While between-group comparisons revealed no significant differences for self-concept nor reading competency, monolinguals outperformed multilinguals in spelling. Correlations between domain-specific self-concepts and academic achievement in reading comprehension, reading fluency, and spelling were positive and significant for both groups. Regardless of language background, children's evaluations of their academic achievement (reading and spelling) were realistic. We argue, on a theoretical basis, that metacognition and EFs could facilitate a bilingual advantage and improve educational outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".