Impact of Teaching a Proposed Unit on Successful Intelligence and Augmented Reality in Biology on Lateral Thinking and Science Fiction among High School Students in Al-Saih City, Saudi Arabia
Bibliographic record
Abstract
The study examined the impact of teaching a unit based on the Theory of Successful Intelligence and Augmented Reality in Biology on developing lateral thinking and science fiction among high school students in Al-Saih City, Saudi Arabia. To verify the research experience's effect, a quasi-experimental design, the "Lateral Thinking Test," and the "Science Fiction Scale" were used. The research sample included 34 experimental and 37 control students (all high school students). The research tool used to examine both groups' lateral thinking contains 24 questions on concepts, alternatives, linkages, and ideas. Science fiction skills include alertness, flexibility, imagery, daydreaming, retreating from reality, and sustaining direction. The results demonstrated a statistically significant difference (0.05) between the average scores of the experimental and control groups for each lateral thinking skill and the lateral thinking test as a whole, in favor of the experimental group. Also, teaching a unit based on the Theory of Successful Intelligence and Applications of Augmented Reality in biology helps develop lateral thinking and science fiction. The research advocated applying the notion of Successful Intelligence and Augmented reality in high school, based on the study's results, to improve educational outcomes such as "lateral thinking" and "science fiction".
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".