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. One might expect that the scope of the variables that ESL/EFL writing instructors typically face when they plan and conduct their courses contributes to a range of curriculum practices. One can also anticipate that experienced instructors would be able to draw from a common pool of practices that reveal some commonality in their courses as well. Thus, identifying areas of commonality and difference in their stated curriculum practices should be of particular value in helping novice instructors to focus their thinking on key aspects of their courses, to reflect on their ongoing teaching experiences from a global perspective, and to anticipate curriculum alternatives that they may wish or be obliged to pursue.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".