Educational institutions today rely heavily on digital systems to store and manage student information for academic, administrative, and regulatory purposes. Over the years, student data is entered and maintained by different departments such as admissions, examinations, accounts, and academic offices. Since multiple users handle data entry and updates at different times, the same student’s information may be recorded more than once. These duplicate or overlapping records often occur due to spelling differences, inconsistent data formatting, missing details, changes in personal information, or migration from one software system to another. Gradually, these issues reduce the overall accuracy and consistency of the institutional database. Duplicate student records can create serious problems in academic operations. They may result in incorrect student counts, inaccurate reports, conflicts in examination records, and confusion during result processing or certificate issuance. As the database grows larger, manually identifying and removing such duplicate entries becomes extremely challenging and time-consuming. Moreover, traditional database systems mainly depend on exact matching techniques, which fail to detect records that have small differences but actually refer to the same student. Therefore, there is a clear need for a more intelligent and flexible approach that can identify similarity patterns in student data and accurately detect duplicate records. This research focuses on the development of an intelligent identification approach designed to recognise repeated student profiles by examining resemblance across multiple attributes rather than relying on direct value equality. The proposed approach emphasises systematic data refinement to reduce inconsistencies caused by representation differences such as case sensitivity, spacing, and formatting variations. After refinement, student records are analyzed to evaluate how closely they resemble one another across selected attributes. Records that demonstrate strong resemblance patterns are identified as overlapping profiles and highlighted for further review.
Educational Record Reliability, Data Entry Variation Handling, Student Profile Similarity Mapping, Redundant Information Filtering, Intelligent Data Consistency Methods, Academic Database Optimisation, Identity Matching in Student Records, Information Normalisation Techniques, Record Overlap Analysis, and Institutional Data Quality Control.
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