The Integration of 21st-Century Skills in Grade Eight Mathematics Curriculum
Bibliographic record
Abstract
The study measured the extent of the implementation of twenty-first-century skills in grade eight math curriculum in public schools in the Kingdom of Bahrain, and it included all Cycle Three Mathematics Books (or curriculum) for the academic year (2022-2023). The researcher chose grade eight mathematics books in the first and second semesters as a sample for her study; furthermore, the research was conducted using the descriptive-analytical method to analyze the contents of the books using a content-analysis card. The results showed a very high percentage (92.20%) of twenty-first-century skills integration in eighth-grade math books; as a result, the percentage of technical literacy was (14.5%), and the rate of local and global citizenship skills was the lowest (7.6%). In addition, the results showed close ratios between critical thinking, creativity and problemsolving, leadership, and decision-making (13.1% – 13.7%). The math curriculum directs learners to access information through digital technologies and includes different mathematical problems in which digital technologies are used to achieve solutions with the highest percentage (25%- 23.6%). However, it was immediately apparent that the math curriculum content failed to motivate learners to develop their skills concerning environmental sustainability (7.8%) and negotiation skills (7.9%). Overall, the literature showed that the widespread opportunity for 21st-century skills in the mathematics curriculum impacts students’ abilities to obtain high grades in the TIMSS exams. This suggests that integrating science, technology, engineering, and mathematics education into mathematics curricula can provide convenience for students to evolve 21st-century skills in a consequential and effective way.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".