Implementation of task-based language teaching in a Spanish language program: Instructors’ and students’ perceptions
Bibliographic record
Abstract
Most research on task-based language teaching (TBLT) has focused on specific factors that play a role in task-based performance and learning, whereas considerably fewer studies have paid attention to how TBLT curricula have been developed and delivered in second language (L2) teaching contexts. However, it has been argued that the latter type of evaluative inquiry is crucial in order to advance the educational significance of the approach. While more evaluation studies have been published in recent years, few of them adopt a multi-methodological, longitudinal and cyclical perspective. The current study examines the planning and implementation of task-based instruction in a university-level Spanish as a foreign language program over a five-year period, with a particular emphasis on instructors’ and students’ perceptions about the approach. Data sources consisted of notes from meetings with instructors, classroom observations, students’ perceptions collected through journals, focus groups and questionnaires, and instructors’ perceptions collected through a questionnaire. The qualitative and quantitative analysis of these data revealed critical aspects of the planning phase, positive and challenging components of the approach, modifications made in response to participants’ perceptions, as well as a gradual increase regarding the level of acceptance of task-based instruction throughout the implementation. Implications for the implementation and evaluation of TBLT in other second language educational contexts are 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".