Language Teachers’ Development of Decision‐Making and Pedagogical Reasoning: A Sociocultural Perspective on Peer Coaching
Bibliographic record
Abstract
ABSTRACT Decision‐making and pedagogical reasoning are two of the underlying skills in language teaching. Integrating sociocultural theory (SCT) into teachers’ classroom decisions can be one novel way to inform their decisions with pedagogical reasoning. This study adopted peer coaching as an SCT‐oriented inquiry‐based approach to professional development and drew on the concepts of the zone of proximal development and mediation. Two novice teachers of English participated in the study. The data were collected through documentation, classroom observation, teachers’ self‐reflection, and video‐stimulated recall within the context of online teaching during the Covid‐19 pandemic. Conversation analysis and qualitative content analysis of the data showed that the peers scaffolded each other's decisions, actively applying the key tenets of mediation using each other, the video recordings, and the metalanguage they had mastered as mediational resources to achieve their goals in language teacher learning. The findings offer practical implications for teacher educators to implement this mediational approach to professional development in their teacher education programs.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".