Continuous Student Improvement Using Machine Learning Techniques

Abstract

Student Improvement model aims in identifying student behavior traits and patterns using multiple psychometrictests followed by suggesting ways to improve areas of their interest along with overall psychological balance by continuous testing at constant intervals of time. To identify the subject’s strengths, weaknesses, and behavioral interests, assessments are carried out on the principle of psychological assessments which assess the students by making them imagine their reactions in various psychological situations. We use different classification methods to predict psychological factors and the efficiency ismeasured by performance factors like recall, precision, and F1 score. It is mainly based on 3 Psychometric tests that are Big Five Personality, Benziger Thinking style assessment, and KOLBlearning preferences. These tests have a huge role to play in the fields of career guidance and employability. Our model evaluates an individual’s personality traits and learning styles and provides a descriptive analysis which helps in identifying students’ overall improvement.