An Action Research Into Task-based CLIL Applied to Education Majors: From Chinese Students’ Perspective
Bibliographic record
Abstract
An action research (AR) was implemented to see if the proposed Task-based CLIL model is effective in achieving the dual goals of content and language learning among Chinese education majors. A questionnaire and a focus group interview were conducted to collect data from the students to see how they perceived the model with their own experience of it. According to the collected data, students stated that they had improved themselves in both English proficiency and subject content knowledge in addition to communication skills. They also stated that tasks offered them more language use opportunities to interact with peers discussing the disparities around task processes and outcomes in class. However, some problems were also identified, such as task organization, cultural conflicts and the choice of the right task. Reflecting on these problems, the teacher revised the teaching method, in which tasks are organized around the teacher’s lectures and arranged according to their specific functions. This study can shed light on how CLIL can be successfully implemented in Chinese collegiate settings, which are different from those studies in non-Chinese contexts.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".