The Relationship of Fluid Intelligence Level with Higher-order Thinking Skills in Work and Energy among Sixth-grade Students in Jordan
Bibliographic record
Abstract
This study aims to investigate the relationship between levels of fluid intelligence and higher-order thinking skills according to Bloom's three levels classification (analysis, evaluation, creation) among sixth grade students in Jordan in the subject of work and energy as one of the science book topics for the first semester of the academic year 2021-2022. For this purpose, a test for higher-order thinking skills was designed on the subject of work and energy, consisting of 17 paragraphs of the type of essay questions and multiple-choice in the 3 areas of higher-order thinking skills (analysis, evaluation, creation). The standard Raven test of fluid intelligence with its 5 levels was also applied to the students. After conducting the statistical analysis, the results of the study revealed: (1) a decrease in the level of higher-order thinking skills to less than the average (analysis, evaluation, and creation, respectively); and (2) a positive correlation between the students' fluid intelligence and their scores in the higher-order thinking skills test. Therefore, the study recommended including activities that take into account fluid intelligence in the curricula to improve students' performance in higher-order thinking 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".