Global perspectives on School-Based Assessment (SBA): A systematic review of international practices and challenges
Bibliographic record
Abstract
This systematic review explores the implementation, philosophical foundations, methodologies, and challenges of School-Based Assessment (SBA) across 12 diverse educational contexts, including Finland, Canada, New Zealand, Hong Kong, Singapore, South Africa, the Caribbean, Australia, Malaysia, China, the United States, and Sri Lanka. Drawing from policy documents, empirical research, and academic literature, the study reveals that SBA has emerged as a global educational reform strategy aimed at promoting student-centered learning, formative assessment, and holistic evaluation. The review categorizes SBA systems according to underlying philosophies—such as constructivist and learner-centered models prevalent in Western nations versus the standardized, centralized frameworks dominant in many Asian countries. Despite these variations, SBA is universally acknowledged for its potential to foster deep learning, intrinsic motivation, and meaningful student engagement. However, implementation is hindered by shared challenges including high teacher workload, limited assessment literacy, insufficient training, and the difficulty of achieving consistency in assessment practices. A comparative analysis of 48 key publications from 12 countries using a PRISMA-style approach reveals both commonalities and regional differences in SBA, providing a structured foundation for global policy development. Recommendations underscore the importance of capacity-building, consistent moderation, and integrated policy support to ensure effective, equitable implementation and strong policy support to enable SBA to realize its full potential in enhancing student-centered learning.
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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 | 0.085 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".