A Proposed Perspective for Developing Science Curriculum for the Upper Primary Grades in Accordance to Saudi Arabia's Vision for 2030: An Analytical and Descriptive Study according to Delphi Method
Bibliographic record
Abstract
Saudi Arabia's Vision for 2030 in development is exceedingly concerned with curriculum development. It believes that the current curriculum does no longer support the students' preparation for both life and work. Therefore, the present study aimed to reach a consensus by a set of educational experts on the importance of providing the science curriculum for the fifth and sixth primary grades with a content that can help achieve the requirements of the Kingdom's Vision for 2030 in development. In addition, it aimed to study what perceptions these educational experts have towards developing the science curriculum that can better achieve this vision. The study followed the descriptive approach and the use of Delphi method. It was applied to a group of (28) educational experts over three rounds that started on October 8, and ended on November 9, 2017. Findings indicated that the provision degree of the requirements of Saudi Arabia's Vision for 2030 in development in the science curriculum for the upper primary grades was too weak. Responses of the educational experts concerning developing the science curriculum to achieve the requirements of this vision, at the end of the third round, ranged between strongly agree and agree. At the end, a perspective for the development of the science curriculum was proposed depending on the viewpoints of the educational experts who participated in the present study.Keywords: Saudi Arabia's Vision for 2030, development of science curriculum, primary grades.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".