Ontology-Guided Learning Assessment: A Multi-Indicator and Feedback-Driven Approach
Ghada Ben Khalifa, Lilia Cheniti Belcadhi, Alicia García-Holgadob · Procedia Computer Science · 2025
This paper examines assessment indicators for evaluating learning outcomes in higher education and proposes a structured framework that categorizes them into seven key areas: Content Quality, Engagement Metrics, Study Time, Assessment Indicators, Difficulty and Efficiency, Participation Metrics, and Motivation and Tracking Indicators. This framework helps educators better understand student performance and engagement. Building on this, an ontological model is introduced to standardize and organize these indicators, enhancing interoperability and supporting automated reasoning for educational decision-making. It is compatible with Learning Management Systems and includes mechanisms for generating automated feedback for timely and personalized interventions. An empirical evaluation shows the model’s effectiveness in identifying at-risk students and providing tailored feedback. The paper concludes by discussing the framework’s implications for improving assessment practices and future directions involving advanced analytics and artificial intelligence in learning outcome assessments.