Bibliographic record
Abstract
Abstract Task-based language teaching (TBLT) is a prominent approach in second and foreign language education ( Ellis et al., 2020 ; Long, 2015 ). However, for many instructors of less commonly taught languages (LCTLs) at institutions of higher education (IHE), TBLT is still an innovative approach that deviates from more familiar structure-focused, teacher-dominated teaching methods. In a multi-year project, LCTL instructors partnered with curriculum design experts to create task-based, proficiency-oriented lesson materials using a reverse design framework. This study focuses on three Portuguese instructors’ understanding and application of TBLT principles and four students’ perspectives on engaging tasks. Data sources included meeting notes, instructor interviews and student responses to a pre- and post-course unit survey, analyzed through qualitative content analysis. Findings revealed that although the instructors’ understanding of TBLT changed over three years of collaborative work, they still struggled with the concept of a task and integrating grammar and vocabulary into task-based lessons. Student feedback challenged conventional task criteria, offering insights into engaging task design features for advanced learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".