AN INTELLENCE STUDENT IQ PREDICTION SYSTEM USING ASSESSMENT DATA

Abstract

This research aims to develop a comprehensive student assessment model for predicting Intelligence Quotient (IQ) using advanced machine learning techniques. The primary objective is to provide a holistic evaluation of students’ academic aptitude and career readiness. To ensure robustness, data is collected from multiple sources, including academic records, faculty evaluations, and socio-economic background information. Academic performance, represented through Grade Point Average (GPA) and subject-wise marks, provides a quantitative measure of students’ scholastic achievements. In addition, qualitative insights from professors enhance the evaluation by assessing key attributes such as analytical thinking, problem-solving ability, classroom participation, and overall behavioural patterns. These multi-dimensional features enable the model to capture both intellectual capabilities and behavioural characteristics that influence student potential.