The Effect of Using Brainstorming on Developing Innovative Thinking and Achievement in Teaching English Language Students
Bibliographic record
Abstract
This study aimed to determine whether or not brainstorming can help foster creative thinking and academic success in seventh-grade English classes at the Wadi Al Seer Directorate of Education. The study's sample was comprised of (240) students at this grade level. There were 124 students in the study's experimental group, who received instruction in the English language through brainstorming, and 116 students in the control group, who received instruction in the English language through the conventional method. Following the end of instruction, valid and reliable innovative thinking and accomplishment tests were administered using appropriate statistical methodologies to ensure their validity and reliability on a scientific basis. The study found the following results: Results from a poststudy test measuring creative problem-solving abilities (fluency, adaptability, and originality) showed that the experimental group that had studied English using brainstorming had a statistically significant advantage over the control group at the 0.05 level. Statistically significant differences were seen at a significance level of 0.05 between the average performance of the two groups in the study's achievement posttest. These differences were in favor of the experimental group, which used brainstorming as a method to study the English language subject.
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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".