Impact of English Teachers’ Behavior on English Proficiency Skills of ESL Learners at Secondary Level
Bibliographic record
Abstract
The main aim of this study is to investigate the impact of teachers’ behavior on English proficiency of ESL learners at secondary level. Teacher’s behavior directly or indirectly influences students’ performance positively or the otherwise. This study was descriptive in nature and survey based design was adopted to pursue operational phase. Respondents were selected from accessible population by using random and convenient sampling techniques. The data was collected quantitatively from 200 students and 40 teachers. Questionnaire was used to collect data from teachers and students to know their perceptive about the impact of instructor’s behavioral patterns on learners’ achievements. The raw data was analyzed followed by tabular, graphical and narrative interpretations. The findings of the study show that teacher’s behavior affects the whole language learning process according to the needs of the students. It further determines that there is a positive correlation between teacher’s positive behavior and student’s linguistic and academic growth. The results also show that positive behavior of female teacher was more learning friendly as compared to the behavior of male teachers. The study concludes with strong recommendation that positive behavior training skills should be implemented in continuous professional development programs for English language teachers. At times, positive behavior of teachers proved more beneficial than their competency for ESL learners.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".