Target versus Native Language Use in Foreign Language Classes: Perspectives of Students and Instructors
Bibliographic record
Abstract
Whether or not to use students’ native language (L1) in second or foreign language classes has always been debated by many scholars and researchers. This controversial issue has taken place in the literature with various case studies and findings. The current study investigates into both students and teachers’ perspectives on using L1 in foreign classes. The study was conducted in a state university offering compulsory language education for at least one academic year in three foreign languages, English, German, and French in 2016-2017 academic year. As data collection instruments, two Likert-type questionnaires were administered to instructors and students. Ten English, seven German and three French Language instructors participated in the study together with 217 students from three compulsory foreign language preparatory classes. Descriptive data were analysed in order to determine the students and instructors’ perspectives about L1 use. ANOVA test was also used to find out whether there exist significant differences among students and instructors with regard to L1 use. The results reveal that there is a statistically significant difference between English and German language students and between French and German language students with regard to L1 use in the foreign language classrooms. However, there is not a statistically significant difference between English and French language students with regard to L1 use in the foreign language classrooms. The findings also indicate that there is no statistically significant difference among English, French and German language instructors concerning the use of L1 in the classes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".