Rethinking language teacher training: steps for making talk-in-interaction research accessible to practitioners
Bibliographic record
Abstract
The goal of this paper is to enhance the quality of language teaching and improve language teacher training by making spoken interaction research accessible to practitioners. Research on teacher cognition has shown that basic beliefs and assumptions about language affect language teacher training programs and language teachers’ priorities in the classroom. Such beliefs tend to reflect teachers’ own socialization and orient to current administrative guidelines in L2 teaching, often resulting in a focus on language production of individual speakers. In contrast, a social-interactionist perspective emphasizes the co-constructed nature of language and interaction. Unpacking teachers’ beliefs and their consequences for what is taught is necessary for implementing interactional competence-based instruction. This paper suggests concrete steps to facilitate teacher training, preparing language teachers for Conversation Analysis-based Interactional Competence instruction. Such training includes, (1) sustained critical reflection of teachers’ conceptions of what language is, (2) basic training of pre- and in-service teachers in micro-analytic procedures that enable the analysis of actual talk-in-interaction, and (3) models for translating and transferring research on spoken communication and interaction into pedagogical practice. These teacher training elements: advance an empirically informed, state-of-the art view on interactional competence (IC); provide teachers with the necessary tools for meaningful, reflexive work with IC materials; and can supplement current methodology textbooks.
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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 | 0.177 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.030 | 0.049 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".