A SYSTEMATIC AGILE FRAMEWORK FOR TEST-DRIVEN ONTOLOGY VALIDATION IN ACADEMIC PERFORMANCE ANALYTICS AND DECISION-MAKING

The rapid growth of educational data from diverse e-learning platforms such as Learning Management Systems (LMS) and Student Information Systems (SIS) presents challenges for universities in integrating and analyzing this data to monitor student performance, assess course effectiveness, and optimize faculty resource allocation. Ontologies provide a robust framework for enabling semantic interoperability and facilitating the integration of heterogeneous data sources for Learning Analytics (LA) and decision-making purposes. This study introduces the SPC_Academic_Performance ontology, a domain-specific ontology developed to consolidate and analyze academic performance data. To ensure the reliability and accuracy of the SPC_Academic_Performance ontology, we adopt the Test-Driven Development Ontology (TDDOnto2) methodology. TDDOnto2 systematically integrates validation techniques into the ontology development process, focusing on consistency checking and property testing. By applying TDDOnto2, this study aims to address common challenges such as logical inconsistencies and incomplete property definitions, ensuring the ontology’s robustness for data integration and retrieval. The findings contribute to developing a systematic ontology validation framework that supports reliable ontology-driven analytics and informed decision-making in higher education. This approach ensures that the proposed ontology can effectively map and retrieve data from heterogeneous sources, ultimately enhancing the accuracy and utility of Learning Analytics in academic performance monitoring and resource management.