The Effect of Using Blogs to Enhance the Writing Skill of English Language Learners at a Saudi University
Bibliographic record
Abstract
The purpose of this paper is to investigate the use of web blogs as a supplementary tool for teaching English. It focuses on studying the effectiveness of using blog exchanges for enhancing the Saudi female university students’ English writing, especially the vocabulary usage. The participants of the current study were thirty-seven Saudi female preparatory year students from the English Language Institute (ELI) at King Abdulaziz University (KAU). Their ages ranged between 18 to 20 years of age. All participants were studying level 103 of the Oxford Headway Plus curriculum, at the third quarter of the academic year 2015-2016. The study hypothesized that there is a positive impact of using web blogs as a supplementary tool in improving the students’ writing skill, especially the use of vocabulary. This research reports on an experimental design study using a quantitative approach. The study also used blog entries and pre/post-tests as primary data collection methods. The pre-test and post-test consisted of 50 vocabulary scale test items. They were taken to measure the differences in participants’ writing performance after 7 weeks of intervention. A paired-sample t-test was utilized for statistical analysis to determine if there were any improvements in the students’ writing performance. The findings indicated an improvement in the students’ writing performance after using the blog entries. In addition, the research experiment contributed to the extension of their vocabulary knowledge.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".