Investigating Characteristics of a Dialogic Discourse Pattern in Japanese Academic English Classrooms
Bibliographic record
Abstract
This article investigates the dialogic aspects of discourse in English for Academic Purposes (EAP) classrooms. A more dialogic pattern of classroom discourse indicates that many participants, for example, students as well as the teacher, are involved in generating the whole classroom discourse. For the purpose of determining the level of dialogicality in academic English classes, twenty four lessons of four different teachers were audio- and video-recorded for an entire academic year. The classroom discourse was transcribed and the level of dialogicality was coded based on principles suggested by Nystrand (2003). The principles cover the authenticity of the questions asked by the teacher and the occurrence of uptake. Accordingly, different modes of classroom discourse are observed in each of these classes which are monologic, recitation, and occasionally dialogic. The cases analyzed in this article reveal that it is not just the type of the questions that can lead to establishment of a dialogic mode, but there are some other teacher moves which can be either facilitative or interruptive. These moves are identified and labeled as encouraging student’s participation (ESP) and discouraging student’s participation (DSP). It is argued that these moves can influence the formation of ground rules and consequently the establishment of a dialogic mode.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".