Mapping the unseen: a data-driven visualization framework for curriculum analysis

Curriculum mapping is essential for ensuring coherence and constructive alignment across learning outcomes, assessments and educational standards. Yet, the inherent complexity of curricula often turns this process into a time-consuming, technically demanding task, generating outputs that are comprehensive but rarely intuitive or actionable for educators, designers, accreditors, or institutional reviewers. To address these limitations, this study introduces a novel, visualization-driven framework that integrates flow analysis, coverage and alignment matrices, and hierarchical structural analysis into a unified system. Grounded in established pedagogical principles and informed by data visualization theory, the framework offers both qualitative interpretations and quantitative diagnostics through metrics such as the Curriculum Redundancy Index (CRI) and Curriculum Alignment Score (CAS). A suite of interactive, web-based applications were purpose-built to operationalize the framework, providing real-time, filter-driven exploration of complex curricular relationships without requiring advanced technical expertise. The publicly accessible curricular explorer platform (https://apps.odc.edu.om/cmap/demo/) demonstrates these capabilities using a synthetic but structurally realistic dataset that mirrors a full undergraduate program. The system enables users to trace curricular progression, identify misalignment and explore hierarchical depth through qualitative visual metaphors and quantitative indicators. This enhances curriculum mapping’s accuracy and usability, transforming it from static documentation into a dynamic, interactive process that supports rapid audits, evidence-based decisions and continuous quality assurance across disciplines.