Teaching the Teachers: Second Language Teachers weigh in on how to better prepare future teachers for the realities of the classroom
Bibliographic record
Abstract
Second language teachers are more likely than teachers of other subjects to quit the profession early, with a reported 47% of ESL teachers in Canada considering leaving the profession within the year (French & Collins, 2011). This research project using a “World Cafe” research design Koen, du Plessis & Koen, 2014) to ask preservice and in-service teachers at two national conferences to reflect on the practices and approaches they experienced in teacher education and to what degree they felt Canadian Teacher Education was adequately preparing pre-service second language teachers for the realities of the classroom. Initial findings suggest second language teachers strongly believe that Teacher Education can better prepare second langauge teachers in two ways: first through restructuring the field placement experience to be longer and more closely integrated to the course content taught in the teacher education programs and second, by addressing the mentoring relationship between student teacher and teacher mentor. This project aims to contribute to the discussion of ways Teacher Education graduate new teachers with a healthier, stronger sense of efficacy and more practical skills to help them survive and thrive in language classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".