Computer science

The Senior High School (SHS) admission process in Ghana remains largely dependent on manual, paper-based workflows and fragmented digital systems that are unable to scale to the demands of a growing student population. This research proposes the design and implementation of a Cloud-Based Intelligent Admission Decision Support System (CIADSS) aimed at transforming and streamlining the SHS enrollment process. The proposed system integrates cloud computing infrastructure with machine learning algorithms to automate applicant screening, predict student placement suitability, and provide data-driven recommendations to admission officers. By leveraging cloud platforms such as Google Cloud or AWS, the system will ensure scalability, high availability, and real-time access to admission data across multiple schools and administrative offices. The intelligent layer, powered by supervised learning models, will analyse applicant academic histories, school preferences, and available placement slots to generate optimal, fair, and transparent admission decisions. A web-based dashboard will provide a user-friendly interface for school administrators, admission officers, and policy stakeholders. The research will employ a mixed-methods approach comprising system design, prototyping, and empirical evaluation through structured questionnaires and performance benchmarking. The expected outcome is a robust, scalable, and equitable admission support tool that significantly reduces administrative burden, minimises human error, and improves the overall efficiency and transparency of the SHS enrollment system in Ghana.