Experienced ESL/EFL writing instructors' conceptualizations of their teaching: Curriculum options and implications
Bibliographic record
Abstract
Education for future language teachers, like the training to become any kind of teacher, involves a process in which novices must acquire both relevant content knowledge and training in pedagogical strategies to be able to create successful classroom experiences for their future students. This is undoubtedly true for English as a second or foreign language (ESL/EFL) writing instructors, who must develop the relevant professional expertise required for this field. Conceptualizing, planning, and delivering courses is the primary focus of the work that such instructors engage in. To help clarify some of the complexities of this practical, professional knowledge, the present chapter1 identifies and analyzes the usual practices that a variety of experienced ESL/EFL writing instructors use to organize their courses. The descriptions of individual and general practices are based on data collected from personal interviews conducted in several different countries; a primary goal of these in-depth interviews was to gather specific information regarding the curriculum practices of highly experienced instructors offering classes in a range of settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".