Navigating Teacher’s Display and Referential Questions to Enhance Learners’ Speaking Accuracy: A Case of Explicit and Implicit Corrective Feedback
Bibliographic record
Abstract
Corrective feedback with its potential role in oral interaction, and teacher’s questions with the capacity to engage the learners in conversational activities led to the investigation of their roles in speaking accuracy of EFL learners. Teacher’s display and referential questions were employed along with explicit (explicit correction, metalinguistic clue, elicitation) and implicit (conversational recast, repetition, clarification request) corrective feedback to create opportunities for the learners to participate in interaction, to modify their errors, and to produce accurate output. Therefore,112 learners who attended 10 intact classes of 15 session terms in one control and four experimental groups were homogenized through administering a PET. In all groups, accuracy was focused while learners were engaged in conversational activities. In the first and second experimental groups teacher’s display questions were implemented followed by the provision of explicit and implicit feedback types. However, the third and fourth experimental groups were asked to answer teacher’s referential questions who received explicit and implicit corrective feedback respectively. To measure the learners’ speaking accuracy, both pre and posttests of speaking were recorded and transcribed to estimate the percentage of error free clause. An analysis of covariance indicated that in learners’ speaking accuracy; both teacher’s display and referential questions with either explicit or implicit feedback types were significantly effective; there were no significant differences between the effectiveness of teacher’s display and referential questions with explicit corrective feedback; and teacher’s referential questions were significantly more effective than display questions with implicit feedback types. The substantial enhancement of EFL learners’ speaking accuracy bears testimony that in interactional view, communicative behavior resulting from the questioning and corrective feedback paves the way for a higher level of accurate output.
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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 | 0.004 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".