Aptitude Training System Using Decision Trees

Abstract

This paper presents the development of an adaptive online aptitude training system utilizing decision trees for students preparing for campus placements. The system aims to assess and enhance students' aptitude and technical skills through personalized learning experiences. Leveraging a vast question bank, the system employs an adaptive algorithm analyzing users' responses, time spent, and performance data from pre-assessments to tailor subsequent assessments. By integrating AI-based question selection, the system ensures relevance and effectiveness in assessing student proficiency. The focus lies on providing targeted exercises to address individual weaknesses, thereby optimizing preparation for campus interviews. This paper underscores the efficacy of decision tree algorithms in delivering personalized aptitude training, contributing to students' success in securing placements. Keywords: Adaptive Learning, Decision Trees, Aptitude Training, Personalized Education