Examining teachers’ evaluations of task characteristics
Bibliographic record
Abstract
Abstract Though teachers have been shown to be widely influential in effective task-based initiatives (e.g., Van den Branden, 2016 ), research on teachers and task-based language teaching (TBLT) has primarily explored how teachers develop or adapt tasks for use in their own contexts during professional development workshops or pre-service teacher programs (e.g., Erlam, 2016 ; Gurzynski-Weiss et al., 2024 ). In comparison, how teachers perceive the strengths and limitations of task characteristics has been less commonly examined. To address this gap, this multi-site study utilizes a mixed-methods design to examine teacher insights about the defining qualities of language learning activities. Two cohorts of pre- and in-service language educators ( N = 40) teaching five different target languages were recruited from task-based teacher education programs in the United States and Japan. After learning about key characteristics of tasks ( Ellis & Shintani, 2013 ; Long, 1985 ; Skehan, 1996 ; Willis & Willis, 1996 ), teachers evaluated the quality of a series of sample language learning activities. Teachers then participated in a post-evaluation interview about their ratings. Results reveal how teachers differentiate the characteristics and quality of tasks and highlight the factors influencing their ratings. The study contributes to ongoing efforts to bridge the divide between research and practice within TBLT initiatives, providing insights into teachers’ decision making. Implications for task-based teacher education, such as how constructs like the definition of ‘task’ could be better articulated when working with teachers, are also discussed.
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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 | 0.008 | 0.048 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".