Aim/Purpose: This study develops an automated framework for evaluating computer science curriculum alignment with educational standards, addressing assessment challenges in rapidly evolving technical fields. Background: Educational institutions struggle with time-consuming, subjective manual curriculum evaluation processes that are difficult to standardize, particularly when technological innovations frequently outpace curriculum updates. Methodology: The ONTO-ALIGN framework employs three complementary matching techniques: direct matching (H1), semantic analysis using WordNet and similarity algorithms (H2), and approximate matching for related concepts. The system evaluates course descriptions against the standards of the Thai Qualifications Framework (TQF: HEd) and Computer Science Curricula 2023 (CS2023). Contribution: This research provides the first comprehensive framework integrating multiple ontology linking techniques within a unified assessment system, bridging pedagogical theory with automated assessment technology for systematic curriculum evaluation. Findings: Validation through five computer science courses demonstrates effectiveness in identifying alignment gaps, with 82.9% agreement with expert assessments. The multi-method strategy proved valuable: direct matching identified 40.9% of correspondences, semantic analysis captured 29.3%, and approximate matching detected 17.1% of alignments. Recommendations for Practitioners: Educational institutions can implement multi-level curriculum assessments that consider both exact terminology matches and semantic relationships, enabling more frequent and systematic evaluations than traditional manual approaches. Recommendation for Researchers: Future research should explore the integration of machine learning with multiple matching techniques for increased accuracy in evolving knowledge domains. Impact on Society: By improving curriculum alignment with established standards, this framework enhances educational quality in computer science, leading to better-prepared graduates for an increasingly digital society. Future Research: Integrating machine learning with multiple matching techniques for increased accuracy in evolving knowledge domains represents a key advancement opportunity.
Namahoot et al. (Wed,) studied this question.