A Study on the Usage of Verb’s Complements with Cases by French Bilingual Somalian Students Learning Turkish as a Foreign Language
Bibliographic record
Abstract
An important part of the grammatical proficiency of students learning Turkish as a foreign language is the use of verb complements in terms of case suffixes used with verbs. The suffixes that determine the relations between nouns and verbs that make up the two main word categories of Turkish are case suffixes. Noun case suffixes, whose main function is to connect nouns to verbs, are one of the most difficult subjects for students learning Turkish as a foreign language. However, there are few studies on teaching noun case suffixes to foreign students. The aim of this study, which was prepared based on the deficiency in the relevant literature, is to determine the usage levels of noun case suffixes in the oral expressions of French bilingual Somalian students learning Turkish. The study group of the research consists of 25 Somalian Somali students studying at the B1, B2 and C1 levels at Bursa Uludag University Turkish Teaching Center in the 2019-2020 academic year and voluntarily participated in this research. The data obtained within the scope of this research, which was designed as a qualitative case study, were analyzed according to the suffix category that provides the relationship between the noun case suffixes and the verbs in the sentence. Other functions of the noun case suffix that provide the connection between nouns and nouns, nouns and prepositions are excluded from the scope of the study. The research data obtained by the semi-structured interview technique were analyzed using frequency analysis, which is one of the sub-techniques of content analysis. As a result of the study, it was determined that 25 French bilingual Somalian students learning Turkish made mistakes at a rate of about half when using the verbs with case suffixes, and they used the nominative case with the least mistakes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".