A Study of Using E-Writing Instructional Design Program to Develop English Writing Ability of Thai EFL Learners
Bibliographic record
Abstract
Learning with technology has become essential in today’s education not only in Thailand but also all around the world. Technology has been an important tool for improving language learners’ reading writing, speaking, and listening for quite some time. Writing, however, has been one of the most supported skills thanks to technology. Learning how to write paragraphs or essays is an essential skill for learners. The purposes of this quasi-experimental study were:  1) to examine the effectiveness of the e-Writing instructional design program in developing the writing skills of EFL learners, 2) to explore learners’ satisfaction and motivation toward the e-Writing instructional design program, and 3) to study learners’ autonomy after completing the e-Writing instructional design program. This study employed the purposive sampling method to select 33 second-year learners. Lesson plans, e-writing programs, learner perception questionnaires, interviews, as well as a pre and post-test were the tools used to gather relevant data. A t-test with standard and average deviation was used to investigate the quantitative data. Interview data were analyzed using content analysis. The quantitative findings revealed that the writing achievement level of the learners before and after receiving the treatment was significantly different at 0.001. The learners’ post-test scores of 33 learners increased over the pre-test scores. From the questionnaire results, the satisfaction level of undergraduate learners toward the instruction of this course had average scores of 4.34 which was an excellent level.  Furthermore, interviews revealed that learners are satisfied with the e-Writing instructional design program because this could improve learners’ writing skills and promote more learner autonomy. Recommendations are made and presented in terms of future practical application and future research needs to be done to analyze results and the effects of future outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".