Hinderance or Assistance: A Case Study on the Application of First Language (L1) in an English as a Foreign Language (EFL) Classroom at an English Language Institution
Bibliographic record
Abstract
L1 use inside EFL classrooms has been a topic of continuous discussion for a considerable period of time, with no prevailing opinion about its impact and extent within L2 (second language) learning environments, particularly within Chinese language institutions. The primary objective is to address the current gap in research and acquire a thorough comprehension of the true effects of L1 use in an EFL classroom. This was achieved through the observation of a specific teaching scenario and conducting an interview with the instructor. Data were classified using Tasçi and Aksu’s nine functions of L1 use. The results suggest that the most often observed functions of L1 in the context of education include imparting instruction, translating new terms, and attracting attention. Additionally, it was found that L1 is most frequently used during the vocabulary section and in the middle of each instructional session. Furthermore, the interviewed teacher expressed the belief that L1 usage in the classroom has positive effects, since it serves many functions and should be tailored differently depending on the students' levels of skill. This study implies that the inclusion and promotion of L2 in the EFL setting is advantageous, as learners can considerably benefit from a language immersion environment. However, L1 can still be used when deemed essential. Furthermore, during instructional sessions, educators have the opportunity to engage in reflective practices to assess the alignment between their pedagogical strategies and their intended instructional objectives.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".