Blogging as a Learning Tool: A Study of Writing Gains in an EFL Setting
Bibliographic record
Abstract
Technology in the classroom today offers novel ways for enhancing the learning experience in ways that also engage the learners. This study aims to evaluate the efficacy of Blog writing in engaging and motivating Saudi EFL learners in the writing class to boost their writing output. The participants are 12 EFL learners at Qassim University, Saudi Arabia, who undertake a blog writing five-week support exercise. The instruments of data collection are pre and post-tests, writing evaluation, and interviews. Results of the paired sample-t test indicated that there is a significantly large difference between before (M = 2.04) and after (M = 3, 0.5), p =.032 phases of testing due to the five-week blogging intervention, with organization of ideas being the faculty most developed at the end of the experiment, followed by presentation, vocabulary, grammar, content, context, and audience appropriacy. Interviews reveal learners’ enhanced engagement and motivation after the intervention. The study concludes with recommendations that will be useful for teachers, curriculum planners, and learners.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".