Designing Explainable Decision Support for Academic Study Progression in Higher Education : A Rule-Based Decision Support Approach to Study Progression and Understanding Degree Requirements

Higher education programs are governed by complex planning to comply with degree requirements that students must interpret when planning their studies. Although student information systems provide accurate records of completed courses, they offer limited support for understanding how these records relate to formal degree requirements, often leading to uncertainty about academic progression and eligibility. This thesis investigates how a rule-based decision support system for course progression can be designed to ensure that students understand and trust its feedback regarding the fulfilment of degree requirements. The thesis adopts a decision support perspective in which formal academic regulations are explicitly represented using declarative rule-based reasoning, rather than relying on automated or data-driven decision-making approaches. Study planning is modelled as a combinatorial optimisation problem and implemented using Answer Set Programming to reason about prerequisites, credit accumulation, and degree constraints. The system generates multiple valid study trajectories and derives explanations directly from its reasoning process. These explanations are presented through an interactive interface that supports progressive disclosure, allowing users to access different levels of detail as needed. The system is evaluated through a formative pilot study with a study counsellor and an exploratory user study with students. The results indicate that formally correct, transparently explained, and interactively accessible feedback supports student understanding and calibrated trust. Minimal explanations aid orientation, while detailed explanations are essential for resolving uncertainty and supporting informed decision-making.The thesis demonstrates that rule-based reasoning combined with explanation-driven interaction can provide effective decision support for academic study planning, complementing existing administrative systems and human advising without replacing them.