Developing Business Writing Skills and Reducing Writing Anxiety of EFL Learners through Wikis
Bibliographic record
Abstract
The present study aimed at investigating the effect of using wikis on developing business writing skills and reducing writing anxiety of Business Administration students at Prince Sattam bin Abdul Aziz University, KSA. Sixty students, who were randomly chosen and divided into two equivalent groups: control and experimental, participated in the study. Two main tools were devised to collect data: Test of Business Writing Skills (TBWS) and Writing Anxiety Inventory (WAI). The experiment was conducted during the second semester of the academic year 2015-2016. A t-test was utilized to calculate the differences between the mean scores of the two groups in pre- and post-intervention. The findings of the study showed statistically significant differences between the mean scores of the experimental group and the control group on the post-TBWS in favor of the experimental group. In addition, the writing anxiety level of the experimental group, after the intervention, was significantly less than the control group. These findings revealed the positive effects of wikis on developing business writing skills and reducing writing anxiety of EFL learners. It is recommended that sufficient training should be provided to instructors on how to integrate wikis in business writing instruction. Pedagogical implications and suggestions for further research are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".