Collaborative Blended Learning Writing Environment: Effects on EFL Students’ Writing Apprehension and Writing Performance
Bibliographic record
Abstract
This study examined the effects of collaborative blended learning writing environment on students’ writing apprehension and writing performance as perceived by a selected group of EFL students enrolled in one of the international schools in Malaysia. Qualitative case study method was employed using semi-structured interview, learning diaries and observation. Twelve male students enrolled in Class Ten were selected to participate in a 13-week study. To learn how to write collaboratively, the students followed the procedures of the blended learning writing process. Students were divided into three groups and were given the freedom to choose the members of the group they would like to work with. They went through the writing process in face-to-face and online learning modes via the class blog and online Viber discussion. Data collected were analyzed qualitatively using thematic analysis. The findings indicated that the students had positive perceptions towards the collaborative blended learning writing environment they had experienced. They perceived that the collaborative blended learning activities had helped them reduced their writing apprehension and improve their writing performance as they experienced and learnt much knowledge concerning the micro and macro aspects of writing. Students also viewed that their online discussion and collaboration on writing in Viber groups and the class blog had assisted them greatly in their writing task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".