Exploring Learners' Perception on Communicative Coursebook by Metaphor Analysis: The Case of CECL
Bibliographic record
Abstract
Despite the popularity of communicative approach in EFL contexts, the use of communicative coursebook in CLT class has not been well discussed. This exploratory study thus investigates through questionnaires how Chinese students view and understand a set of communicative coursebooks—CECL, short for Communicative English for Chinese Learners in classrooms. Questionnaires were distributed to 103 English majors who have studied the material for at least one year at university. Metaphors are used in the questionnaire to pin point both the strengths and weaknesses of the coursebook. By analyzing and categorizing metaphors by the students, the study found that students were generally positive about the material since they regarded the coursebook as all-inclusive, authentic, communicative, culture-bound and stimulating. Whereas, negative metaphors reflected students’ dissatisfaction with the coursebook due to its disorderly organization and outdated content. Blend metaphors revealed their contradictory feelings to both merits and demerits of the coursebook. The results indicated that task-based communicative materials might confuse students who have long been accustomed to text-based coursebooks and grammar teaching. Evaluation on materials from learners and necessary training on teachers are needed to bridge the gap between theory and practice in classrooms.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".