The Effectiveness of Teacher-Student Interaction in the English as a Foreign Language Classroom
Bibliographic record
Abstract
The aim of the present study is to evaluate the effectiveness of teacher-student interaction in promoting authentic L2 classroom discourse. Classroom discourse is a major part of instruction that promotes speaking and develops students and teachers’ conversational skills. It constitutes a significant factor in the English as a Foreign Language (EFL) classroom, as it promotes communicative activities (reception, production, interaction, mediation) fundamental to L2 learning. A common exchange pattern used extensively in classroom discourse is the Initiation, Response, Feedback (IRF) model in which the teacher initiates (I) an exchange through questioning the whole class or one single student, the student responds (R) to the question, and then the teacher gives feedback (F). A qualitative case study approach was adopted. Twenty-seven EFL high school classrooms in Israel were observed. Research instruments were classroom observation forms, filled by the researcher, where instances of IRF exchanges were marked; and semi-structured interviews with seven of the teachers observed to obtain information regarding their use of L1 in IRF exchanges. Findings show that teachers use the IRF exchange model to organize talk and follow basic turn taking rules. However, deviations from common exchanges result in (1) imbalance of dominance between teacher and student talk time; (2) excessive use of L1 in exchanges, minimizing students’ exposure to L2; and (3) limited flow of teacher-student spoken communication and lack of student willingness to participate in the lesson. Practical implications for teacher educators will be discussed.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".