Promoting CLT within a Computer Assisted Learning Environment: A Survey of the Communicative English Course of FLTC
Bibliographic record
Abstract
This study is based on a survey of the Communicative English Language Certificate (CELC) course run by the Foreign Language Training Center (FLTC), a Project under the Ministry of Education, Bangladesh. FLTC is working to promote the teaching and learning of English through its eleven computer-based and state of the art language laboratories. As Computer Assisted Language Learning (CALL) is becoming increasingly popular in many EFL/ESL contexts, the fully computer based language teaching facilities offered by FLTC can be seen as a significant step towards implementing CALL in the soil of Bangladesh. However, any language course using technology alone may not always ensure the best practices of language teaching and learning; the advantages of technological advancements are to be adapted according to the pedagogic needs of the concerned language course. This paper, therefore, seeks to explore how aspects of CALL are being integrated within a framework of Communicative Language Teaching (CLT). The survey was conducted on 425 learners who had completed the CELC course at the selected four centers of FLTC. This study provides a brief overview of how computers are being used for teaching communicative English. It is found that the trainees gave a positive response about the contents, facilities, and organization of the course but not completely satisfied with the teaching techniques and participants’ role as independent learners. It is recommended that there is a need for making the computer based learning materials more accessible to the learners so that they can use them more independently in and outside the language lab.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".