The Effect of Conducting De Bono’s Six Thinking Hats Activity on Developing Paragraph Writing Skills of University Students in The Kingdom of Saudi Arabia
Bibliographic record
Abstract
The purpose of the present study is to examine the effect of conducting De Bono’s six thinking hats activity on developing the paragraph writing skills of university students. Two groups of students studying the course Technical Writing in Business (NAJM 167) of Prince Sattam Bin Abdulaziz University were chosen to achieve this objective. Pre- and post-tests were conducted for both the groups to determine the difference in their mean scores. Both the groups were given the task of writing a paragraph as a pre-test before conducting the activity. Then, in the experiment section, six thinking hats activity was conducted six times, each time changing the hat color of the groups. As the students had class for 100 minutes three times a week, the experimental group did the activity six times for two weeks. The control group was taught the textbook verbatim. The two groups were given the task of writing a paragraph again as a post-test. The paragraphs were evaluated. At 5% significance levels, two-tailed test was applied. The scores of the control group were much lower than the experimental group in the paragraph writing test. The statistics also showed significant differences between mean scores of the two groups. The results prove the effectiveness of six thinking hats activity in developing writing skills of university students. Therefore, the present study recommends that it is appropriate for EFL teachers in Saudi Arabia to make students do the six thinking hats activity along with other activities given in the textbook in order to improve their writing skills.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".