Abstract

This paper evaluates the effectiveness of a personalized adaptive tutoring system using knowledge tracing as an intelligent
style toward increase beginner studying. Outcomes. Conventional learning methods regularly implement a repaired uniform
teaching methodology, which fails to address individual learning differences. To overcome this limitation, the proposed system
leverages knowledge tracing techniques to model and monitor a student’s learning progress over time. Knowledge Tracing
enables the system to estimate a learner’s mastery level by analysing their responses to questions and interactions with
learning content. Based on this continuous assessment, the system dynamically adapts the difficulty level, content sequence,
and type of instructional materials to suit each student’s needs. The study explores various knowledge tracing models,
including probabilistic and machine learning-based approaches, for accurate prediction of student performance.