Peer Collaborative Dialogues: Perceptions and Attitudes of EFL Students and Teachers
Bibliographic record
Abstract
Research has consistently demonstrated that student interactions and collaborative learning are essential components of the educational process. It is crucial to incorporate peer-to-peer interactions in second language learning environments. This study investigated the student and teachers’ perceptions and attitudes towards the emphasis on formal language aspects during peer interactions in their regular English classes. The data was collected through a student survey and an interview with the teacher. The study involved one English language teacher and 22 seventh-grade students learning English as a Foreign Language (EFL) at a public school in Oman. The results indicated that the students generally held positive attitudes towards collaborative dialogues (CDs). The participants were found to be capable of assisting one another and contributing to overall language improvement, particularly in the correct pronunciation of English words. The teacher also expressed a favourable stance on implementing CDs in her instruction, asserting that the dialogues promote cooperation and mutual learning among the students. Consequently, it is recommended that textbook writers incorporate activities that promote student interaction, which may facilitate productive CDs. Educators should judiciously integrate collaborative learning activities into their daily classroom practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".