Investigating the Use of Word Choice and Students' Achievement in English Language Learning
Bibliographic record
Abstract
This study investigates the correlation between teachers' word choices and student learning outcomes in an English as a Foreign Language (EFL) classroom. This study highlights the importance of teacher word choice in language acquisition, noting the gap between theoretical understanding and practical application. The study employs qualitative research methods, including classroom observations, interviews with teachers and students, and data analysis techniques, to explore the impact of teacher word choice on student comprehension. The findings revealed that teachers' word choices can significantly affect classroom interaction and student understanding. Inappropriate word choices, such as the use of overly complex vocabulary or unclear explanations, can lead to misunderstandings and hinder student learning. Conversely, the use of simple, clear, and contextually relevant languages can facilitate students’ comprehension and improve learning outcomes. The study concludes by emphasizing the importance of teacher training in effective language use and selection of appropriate vocabulary to enhance student learning in EFL classrooms. These findings underscore the critical role of teachers’ language awareness in creating an effective learning environment. Teachers should be encouraged to reflect on their word choices and adapt their language to match their proficiency levels and learning needs. Incorporating explicit vocabulary instruction and providing opportunities for students to engage with new words in meaningful contexts can enhance their overall language acquisition and academic performance.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".