Combining Foreign and Second Language Teacher Education: Rewards and Challenges
Bibliographic record
Abstract
Second and foreign language (FL) teacher education have more commonalities than differences. Nevertheless, in the United States, English as a second language (ESL) teachers and FL teachers often complete their initial or continuing education in different departments or even different colleges. The reasons for this may be philosophical or historical or both. In the Second Languages and Cultures (SLC) Education program in the Department of Curriculum and Instruction at the University of Minnesota, we have long argued that second language (L2) contexts are fragmented and isolated from one another-in schools, in programs that prepare teachers for L2 settings, and in the profession at large (Tedick & Walker, 1994; Tedick, Walker, Lange, Paige, & Jorstad, 1993). In both preservice and inservice teacher education, FL teachers are primarily prepared in language departments, ESL teachers often receive their professional development in linguistics or English departments, and bilingual teachers are enrolled in isolated programs that are often linked to education departments administratively. Immersion teachers in the United States have little opportunity to have professional development that is designed to address their unique needs and issues (Met & Lorenz, 1997), although Canada and Australia are two countries where immersion-specific preparation programs exist (see, e.g., Day & Shapson, 1996; Erben, chap. 16, this volume). We maintain that language teachers, regardless of context, should engage in professional development together, and we have become aware of the rewards and challenges of combining FL and L2 teacher education.
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 | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".