Database-Driven Architecture for Academic Knowledge Modeling and Automated Practice Paper Generation

Abstract: Students in diploma-level technical education often find it difficult to access academic content that is properly organized according to exam requirements. Most existing tools like study planners and question banks work separately and do not combine syllabus structure, previous year questions, and exam patterns in one place. This paper proposes a database architecture designed for managing academic content and supporting exam preparation. The system organizes syllabus elements such as subjects, units, topics, and intended learning outcomes using a structured relational model. It also includes previous year questions that are mapped to syllabus units and classified using Bloom’s taxonomy. In addition, the system represents exam patterns using slot-based structures and blueprint constraints. This helps in retrieving structured data and allows automatic generation of practice papers. Overall, the design shows how a well-organized database can support better academic understanding, question mapping, and exam focused preparation.

Keywords: Database-driven architecture, Academic knowledge modeling, Practice paper generation, Bloom’s taxonomy, Question mapping, Exam pattern modeling