Language assessment tools for Arabic-speaking heritage and refugee children in Germany
Bibliographic record
Abstract
Abstract Though Germany has long provided education for children speaking a heritage language and received two recent waves of refugees, reliable assessment tools for diagnosis of language impairment or the progress in the acquisition of German as a second language (L2) by refugee children are still lacking. The few tools expressly targeting bilingual populations are normed for younger, early successive bilingual children. This study investigates 27 typically developing children with Arabic as first language (L1), comparing 15 school-age Syrian refugees (6;6–12;8), with 12 heritage speakers (6;0–12;9). We assess the L1 and L2 skills of these two groups with standardized tests, but crucially with an Arabic and a German sentence repetition (SRT) as well as a nonword (NWRT) repetition task (Grimm & Hübner, in press; Marinis & Armon-Lotem, 2015). Comparable scores emerged only for German LITMUS-NWRT and Arabic LITMUS-SRT. Refugee children had an advantage in L1 measures, for example, vocabulary and morphosyntactic production, whereas they performed poorly in the German LITMUS-SRT and other L2 tests involving morphosyntax and vocabulary even with 24 months of systematic exposure. This indicates that the acquisition of adequate vocabulary and complex syntax takes time. The paper explores factors influencing performance on the repetition tasks and relates the results to established diagnostic procedures and educational policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".