Integrating ontology and knowledge graphs for intelligent assessment and feedback in E-learning systems.

In many online courses, assessment is still reduced to a numerical score accompanied by brief, generic feedback. Learners with different difficulties often receive similar responses, while instructors have limited support for identifying at-risk students early. In this study, we present an ontology-driven knowledge graph framework for adaptive assessment and feedback. At its core is a reusable ontology that models learning outcomes, assessment tasks, learner characteristics, and feedback strategies. This ontology is instantiated as a knowledge graph and continuously updated with traces of learner activity, representing interactions and performance as semantically structured data. Assessment-to-learning-outcome alignment is defined through predefined mappings, enabling explicit reasoning over learner performance. Using SWRL rules and SPARQL queries, the system identifies at-risk learners, generates tailored feedback, and links each recommendation to its underlying evidence and reasoning steps. We evaluate the framework on the OULAD dataset. Compared with a simple threshold baseline, the approach achieves higher precision for detecting at-risk learners (0.80 vs. 0.68) and an overall F1 score of 0.77. In an expert-based evaluation, the generated feedback is judged more relevant (average gain of 0.7 points) and more explainable (average gain of 1.3 points), with substantial inter-rater agreement (κ = 0.69). On a knowledge graph of approximately 4.2 million triples, query times remain under one second, indicating that the approach can support near real-time feedback generation at institutional scale. These results suggest that ontology-driven knowledge graphs can provide a transparent and practical foundation for adaptive, explainable feedback in e-learning systems.