Analysis and Implementation of Machine Learning Approaches for Adaptive Learning Using Personalized Assessment

A comparison of machine learning models developed for an adaptive learning platform is done, aiming to revolutionize educational methodologies in response to the diverse learning needs of individuals. The model dynamically adjusts the difficulty of test questions based on user performance, creating a personalized learning experience. The study evaluates and compares the model's performance across various machine learning algorithms, including Random Forest, Decision Tree, Naive Bayes and Logistic Regression. The findings reveal the model's high accuracy, ranging from 66% to 92%, showcasing its efficacy in adapting to individual capabilities. By generating user performance profiles, the model successfully tailors challenges to meet diverse learning needs, contributing to an optimized educational experience. Moreover, this research contributes to the field by demonstrating that the proposed model's simplicity, particularly when using the Decision Tree algorithm, yields comparable accuracy results to more complex models. Through extensive testing, the model consistently performed with accuracy ranging from 66% to 92%, even with a small preliminary test size of 21 questions. This simplicity, coupled with its ability to dynamically adjust difficulty levels, positions the model as a pragmatic and effective solution for adaptive learning. The study concludes by underlining the model's effectiveness in providing personalized learning experiences, ultimately enhancing user engagement and educational outcomes.