Curriculum and Policies of the Computer Science and Engineering Track in Saudi Secondary Education: International Alignment and Early Challenges
Bibliographic record
Abstract
Korea In alignment with Saudi Vision 2030 and the United Nations Sustainable Development Goal 4 (SDG 4), the Kingdom of Saudi Arabia introduced the Secondary Education Tracks System in 2021 as a cornerstone reform in secondary education.A central feature of this initiative is the Computer Science and Engineering track (CSE), designed to enhance computer science curricular relevance, equip students with 21st-century digital competencies, and prepare them for participation in the knowledge economy.This study evaluates the reform by focusing on two key dimensions: the alignment of the Computer Science and Engineering track's curriculum and policies with international best practices (e.g., Finland, Singapore, Ontario) and the challenges and opportunities observed during its early implementation as part of the Saudi secondary education tracks system.A mixed-methods design was employed, integrating survey data from 2,818 stakeholders-including students, teachers, administrators, parents, and policymakers-supported with document analysis.Findings reveal encouraging progress: the reform has diversified curricula, expanded flexible learning tracks, and improved engagement through technology-enhanced and project-based learning approaches.However, persistent challenges remain, particularly regarding infrastructure disparities, equitable access to specialized tracks, and the availability of qualified educators and academic advisors.This research underscores that Computer Science and Engineering track's curriculum and policies within Saudi Arabia's secondary education tracks is a transformative initiative with significant potential to align with global models, provided that implementation challenges are addressed.Overall, the Tracks System represents a transformative step toward a more responsive and globally competitive education system in Saudi Arabia.To sustain momentum, the study recommends intensified investment in teacher professional development, digital resources, and stronger integration with higher education and labor market pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".