Finding success with pedagogical innovation: A case from CSL teachers’ experiences with TBLT
Bibliographic record
Abstract
This study uncovers the under-explored influences that encourage teachers to incorporate task-based language teaching (TBLT) for teaching Chinese as a second language, and the process of teachers’ pedagogical attempts at a Chinese university. Activity Theory (Engeström, 1987) was adopted as the conceptual framework. As a qualitative study, the analysis drew on data from interviews and classroom observations with eight teachers, and complemented by interviews with two directors and 17 students. The study reveals that teachers’ pedagogical practice results from a process of negotiating the possible pedagogical tools to reach their teaching objectives in their context of teaching. In particular, teachers depart from the traditional teaching approach to incorporate tasks as a personal initiative in response to the perceived challenges in the effort to achieve their objectives. The study argues that compared to the constraints from the local education context, teacher beliefs and knowledge play a more critical role in shaping the extent to which teachers choose to adopt TBLT, as teacher beliefs and knowledge directly creates tension between TBLT as a tool and the desired objectives. The study proposes that the Problem-Solving Model (Havelock, 1969) for introducing pedagogical change gives teachers agency and ownership over TBLT, which may serve as a possible direction for realizing the pedagogical innovation.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".