Indonesian EFL Teachers’ Familiarity with and Opinion on the Internet-Based Teaching of Writing
Bibliographic record
Abstract
The use of the Information and Communication Technology (ICT) especially the Internet has been a common practice in education. However, research studies show that the Internet has not been frequently used in the teaching of English as a foreign language (EFL) writing, especially in the Indonesian context. This study aimed to find out whether or not Indonesian EFL teachers are familiar with the Internet-based techniques for the teaching of writing. In addition, it investigated their opinions on the Internet-based techniques of teaching of writing. This study involved 17 EFL teachers from various parts of the country who were asked about their experiences and opinions dealing with the Internet-based teaching of writing. The results of the study showed that almost half of the teachers admitted that they have used Internet facilities for the teaching of writing. The other EFL teachers either have indirect involvement with the teaching of writing using Internet application or have never used Internet applications at all. However, these teachers had intention to teach writing by applying Internet-based techniques for their future practices. The study also showed that Indonesian EFL teachers valued the Internet-based teaching of writing as this practice benefits the students in terms of their writing quality and quantity, autonomy, flexibility, as well as confidence. This implies that with the development of advanced ICT, there is a hope that students’ learning of writing could be improved well.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".