Students and the Teacher’s Perceptions on Incorporating the Blog Task and Peer Feedback into EFL Writing Classes Through Blogs
Bibliographic record
Abstract
With the availability of Web 2.0 technologies, blogs have become useful and attractive tools for teachers of English as a Foreign Language (EFL) in their writing classes. Learners do not need to understand HTML in order to construct blogs, and the appearance and content can be facilitated via the use of photos, music, and video files (Vurdien, 2013). To provide an authentic and motivating writing environment, a blog task was designed and integrated into three writing courses for 57 applied English or English major students at two southern Taiwan universities. Using the triangulated approach, this study collected data from three different angles (students’ questionnaires, students’ focus group interviews, and the teacher’s observation log) to investigate whether participant perceptions empirically supported the theoretical hypothesis that blogging contributes to writing performance. The findings showed that both the teacher and students had a positive attitude towards the blog task and may indicate that blogging is a useful alternative approach but may also be regular incorporated in writing classes to enhance EFL writing motivation. Nevertheless, blogs may not be the most suitable tool for all types of writing tasks and the most appropriate medium for all components of feedback. The conclusions of this study are consistent with previous findings on the practicality and potential of using blog software to promote peer feedback as well as to facilitate effective writing instruction.
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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 | 0.004 | 0.016 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".