Bibliographic record
Abstract
The purpose of this study is to examine online in-service language teacher training programs domestic and abroad in attempt to find out implications for developing glocalized version of online in-service English teacher training program. For this purpose, the study analyzed six Korean online in-service English teacher training programs(KOTTP) and one Canadian in-service language teacher training program(Online Teacher Training, OTT) in terms of framework, contents, teaching and learning strategies, interaction, technical aspects and evaluation. The study also analyzed post-training free descriptions of 97 trainees who participated in the programs to find out their needs and satisfaction about online in-service teacher training programs. The results of the study showed that : 1) OTT emphasized on trainees' own reflection after attending OTT whereas KOTTP focused on program contents themselves. 2) KOTTP provided trainee-friendly technical functions but, in terms of interaction, OTT focused on interaction between trainees and trainers or among trainees whereas KOTTP has tools for contents-trainees interactions. 3) According to post training free descriptions, 77% of trainees showed their opinions about the contents of program and trainees also described their demands and needs for various aspects of program such as introducing practical materials for actual classroom instruction, making training courses relevant to real teaching and learning situation. 4) Trainees mentioned their changes in terms of their English proficiency, teaching skills, confidence and reflection of their teaching styles and so on. Finally, based on the findings, the paper discussed a few suggestions for developing glocalization versions of online in-service English teacher training program.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".