Efficacy of Task-Based Learning in a Chinese EFL Classroom: A Case Study
Bibliographic record
Abstract
This study investigated how task-based learning (TBL) developed the verbal competence of Chinese learners of English as a foreign language (EFL) by employing qualitative and quantitative analyses. We compared the impromptu oral presentations on reading texts of 76 intermediate EFL learners given respectively in the beginning and the end of the 15-week study period at a Taiwanese university. The findings revealed that TBL was effective in fluency, lexical and syntactic complexity, and ineffective in accuracy. Besides, a standardized pretest and a post-test of English proficiency were administered, apart from a students’ self-report questionnaire at the end of the experiment. The results of the proficiency exams showed that students made significant improvements in reading and little improvement in listening after TBL. In the self-assessment of the effects of TBL, students felt improvements in vocabulary and pronunciation. Overall, TBL was motivational and useful for language acquisition. Learners’ attention was directed to a reading text as a whole for communicative purposes, and therefore their receptive and productive competence was enhanced.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".