What impact can a TBLT teacher education course have?
Bibliographic record
Abstract
Abstract Despite teachers’ overall positive perceptions about Task-Based Language Teaching (TBLT) ( Ellis et al., 2020 ; Van den Branden, 2022 ), support in the form of teacher education appears to be crucial in helping current and future teachers in their efforts to implement TBLT ( Brandl, 2017 ; East, 2022 ). This article examines the impact of a 13-week-long course for pre-service teachers of Spanish L2 on participants’ beliefs about and understanding of TBLT, and on their ability to design tasks. Drawing from Erlam (2016) and Ogilvie and Dunn (2010) , data were collected using a pedagogical beliefs questionnaire, written reflections gathered throughout the course, the analysis of three pedagogical sequences, and the design of a task as the final project in the course. The quantitative and qualitative analysis of the data show that participants’ disposition towards using TBLT in their future teaching practice increased throughout the course and that 80% of the final tasks fulfilled all or most of Ellis’ (2009) task criteria. The study contributes to the growing body of research on the role of teacher education in the diffusion of TBLT as a pedagogical innovation.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".