Focuses and Trends of the Research on Task-based Language Teaching (1998-2022)
Bibliographic record
Abstract
Task-based language teaching, as a communicative language teaching model, has gradually become a hot topic in the field of second language teaching and acquisition. In order to present the research focuses and trends of task-based language teaching, this paper, by resorting to the Web of Science and Excel, conducts a qualitative and quantitative statistical analysis of the relevant papers published in ten internationally renowned second language acquisition academic journals from 1998 to 2022. The results indicate that: (1) the number of papers presents a dynamic upward trend; (2) the research subjects are mainly college students who speak English as a second or foreign language; (3) the main research fields cover task performance, task characteristics, task implementation conditions, learner internal factors and integration of TBLT and computer network; (4) with research methodology being more diversified, empirical studies take a dominant position and the quantitative method plays a leading role. The results of this study have some implications for future task-based language teaching and research.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".