Training English Language Pre-service Teachers Using a Team Based Learning Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Team Based Learning which focuses on enhancing positive group dynamics is a relatively popular instructional approach in several disciplines such as Health Sciences and Business but has yet to gain popularity in Education. This paper examines the use of Team Based Learning in teacher training as well as the receptiveness towards the approach as indicated by a group of Teaching English as a Second Language teacher trainee. The trainees were asked to write diaries regarding their experiences working in a team during a course for a semester which were then collected in three cycles throughout the semester. Entries were analysed in terms of whether there were positive, negative or neutral reference to working in teams. All members of a team were also required to participate in individual micro teaching sessions for which they were evaluated. Findings indicate that Team Based Learning has a potential role in teacher training as positive entries outnumbered negative entries. Additionally, teams with high average micro teaching scores also had more positive diary entries.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it