Exploring the Impact of Blogging in English Classrooms: Focus on the Ideal Writing Self of EFL Learners
Bibliographic record
Abstract
In order to contribute to the line of inquiry on the use of Web 2.0 technology in Second Language (L2) learning, the current research employed a quantitative research method to probe the contribution of blog-mediated writing instruction to developing the writing skill and the ideal writing self of Iranian English-as-a-Foreign Language (EFL) learners.In so doing, a sample of 47 Iranian EFL learners in a private language institute were recruited as the subjects of this research.These students were then divided into two classes and were randomly assigned to an experimental class (N = 24) and a control class (N = 23).Concerning the research intervention, the students of the blog group used blogs in their writing instruction while those in the control group received the conventional, inside-the-class writing instruction.The Ideal L2 Writing Self Scale was given before and after the intervention as the pre-test and the post-test, respectively.The results from ANCOVA analysis indicated that although both groups progressed in both writing performance and ideal writing self scores, the learners of the blog group outperformed those of control group, suggesting that the blog-mediated instruction significantly contributed to improving the writing performance (F= 9.78, p = 0.003, partial eta squared = 0.18) and ideal L2 writing self of the EFL students (F= 11.678, p = 0.001, partial eta squared = 0.210).These results may offer important implications for EFL writing research and pedagogy.Overall, the integration of blogs in L2 pedagogy might be effective type of blended learning approach which can result in better linguistic and affective 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".