Oral corrective feedback: Pre-service English as a second language teachers’ beliefs and practices
Bibliographic record
Abstract
This study investigated the relationship between pre-service English-as-a-second-language (ESL) teachers’ pedagogical beliefs and their actual teaching practices. To determine the nature of this relationship, 99 teachers-in-training with little or no teaching experience were asked to complete a questionnaire seeking information about their teaching beliefs, particularly about oral corrective feedback (i.e. teachers’ responses to students’ language errors). The teachers’ responses were subjected to an exploratory factor analysis which revealed several dimensions underlying their beliefs. To examine how these beliefs affect classroom performance, 10 of the teachers were first asked to indicate how they would correct language errors illustrated in hypothetical (videotaped) classroom scenarios and were then observed teaching an authentic ESL class. The classes were video-recorded and 30-minute teacher-fronted communicative segments from the lessons were analysed for the number and type of errors learners made and the teachers addressed. Results indicate a multifarious relationship between stated beliefs and actual teaching practices in that while the teachers corrected fewer errors than they believed they would, they preferred the same corrective techniques in both hypothetical and actual teaching situations. Most notably, the study suggests that the complexities of the language classroom and the pre-service teachers’ lack of experience at integrating theoretical knowledge and practical skills, lead them to behave overall as native-speaking interlocutors, not as language teachers. Implications for teacher training are 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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".