Development of a Past Questions Repository System

Abstract

Limited access to past examination question papers and the slow response time of existing question paper repositories have contributed significantly to poor academic preparation, leading to increased course failure rates among university students. Many traditional repositories suffer from inefficient search mechanisms, delayed retrieval, and restricted availability, making it difficult for students to obtain relevant past questions when needed. This paper presents the implementation of an Artificial Neural Network (ANN) within an electronic repository of past examination question papers in a university setting to address these challenges. The repository represents a significant advancement in academic resource management by providing a streamlined, user-friendly, and highly accessible platform for students and staff. Through an intuitive interface and efficient search tools, users can easily navigate extensive collections of question papers spanning multiple disciplines, courses, and semesters. The integration of ANN technology enhances accessibility and system performance by reducing search time by up to 5 ms through automated classification of student queries, thereby minimizing delays associated with complex database operations. The system also incorporates security measures to ensure the confidentiality and integrity of stored question papers while promoting environmental sustainability by reducing reliance on printed materials.