Analyzing final grade anomalies in academic performance data

Abstract

achievement, informing policy decisions, and maintaining the integrity of educational assessments. However, anomalies within these records whether due to data entry mistakes, inconsistencies in grading criteria, or genuine outliers in student performance can adversely affect data quality and fairness. Identifying such anomalies efficiently and accurately is thus essential for ensuring reliability in academic evaluations. This research focuses specifically on detecting anomalies within final grade datasets. By applying advanced anomaly detection methods, the study aims to achieve three primary objectives: A. Data Quality Assurance Identify and rectify records affected by entry errors or inconsistencies. B. Enhanced Fairness Highlight potential irregularities in grading practices to ensure equitable treatment of students. C. Actionable Insights Provide educators and administrators with actionable insights regarding anomalies, facilitating timely and informed interventions. The significance of this work lies in its contribution to maintaining rigorous standards of data quality, transparency, and equity within educational institutions, ultimately promoting trust and reliability in academic assessments.