From Bakhtin to See the Co-construction of EFL Adult Learners’ Utterances
Bibliographic record
Abstract
The purposes of this study were to explore the effect of dialogic activities on EFL students’ utterances development by engaging with others, as well as the students’ perceptions in the dialogic learning environment. The theoretical framework guiding this inquiry consists of the on-site lecture from the instructor and voice board feedback from the peers and the instructor based on the dialogical theory of language concepts from Bakhtin’s dialogism which emphasizes a social and interactive situation of foreign language learning by engaging with others. In this study, we cover multiple data sources that give us an overview of students’ interaction in the dialogic activities: the questionnaire of voice board interactions, students’ interviews, and speaking tests. The results showed, on the whole, English language learners actually developed some kind of utterances by engaging their own and others. They transformed others’ utterances in the oral interaction for their own use in the Asynchronous Computer Mediated Communication (ACMC) environment. Additionally, the learners perceived the voice board activities helpful for the development of their speaking abilities, while the learners’ perceptions are mediated through the dialogical activities in which the learners are engaged in.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".