International Journal Of Imaging

Abstract

EARLY WARNING SYSTEM FOR-AT RISK STUDENT IDENTIFICATION IN EDUCATION

Author: S.Harish, G.Naveen ,S.Pragnesh, M. Harikumar

Abstract:

The development of data analysis techniques and intelligent systems has significantly impacted education, leading to the rise of educational data mining (EDM). The Early Warning System (EWS) plays a crucial role in predicting at-risk students and analyzing learner performance by considering various socio-cultural, structural, and educational factors influencing dropout rates. Our project introduces a robust EWS model, built on an original database that ensures precision in selecting dropout indicators. Using the K-Nearest Neighbour (KNN) algorithm, our model achieved outstanding accuracy, exceeding 99.5% for the training set and 99.3% for the test set. Additionally, we explored alternative classification models to enhance prediction accuracy and improve intervention strategies. To make the results more accessible and actionable, we developed a Django-based application that visualizes predictive insights, enabling educators and policymakers to analyze student data efficien