SMS 4: REGISTRAR DATA MANAGEMENT SYSTEM WITH PREDICTIVE ANALYTICS USING ANOMALY DETECTION

Abstract

Registrar offices play a vital role in the academic operations of higher education institutions by managing student admissions, records, and document processing. However, many continue to rely on manual or partially digitized systems, resulting in inefficiencies such as prolonged processing times, data discrepancies, and limited responsiveness to student needs. This study introduces a Registrar Data Management System (RDMS) designed to streamline these processes while integrating predictive analytics and anomaly detection to further improve data integrity and operational responsiveness. The project adopted an Agile project management approach to develop the RDMS. The process began with the Concept Phase, where system goals and scope were defined in collaboration with academic stakeholders. The system was designed to manage student registration, sectioning, grades, and transcript processing. During the Iteration Phase, core features were developed through multiple sprints, with each cycle focused on designing, implementing, and testing specific functionalities. After core development, the project entered the Release Phase, where a dedicated pre-development support team worked alongside the quality assurance team to identify and fix bugs. System validation was conducted to ensure performance and reliability. Integration with existing platforms, such as the Cashiering and Management Information System (MIS), was also completed during this stage. The system effectively automated the registrar's key operations, significantly reducing document processing time and minimizing data-related errors. Student records were managed with greater accuracy, and overall administrative efficiency improved. The inclusion of predictive analytics and anomaly detection enabled the early identification of data irregularities, supporting timely interventions and decision-making. The integration of predictive analytics and anomaly detection into the Registrar Data Management System substantially enhanced the accuracy, efficiency, and responsiveness of academic administrative functions. Real-time monitoring reduced the risk of human error, supported proactive decision-making, and improved data integrity. The system's modular architecture and use of web technologies also ensured adaptability, scalability, and long-term maintainability across institutional operations.