Developing an intelligent tutoring system using machine learning algorithms for personalized learning experiences

Thesis Abstract

Abstract
This thesis presents the development of an intelligent tutoring system (ITS) utilizing machine learning algorithms to enhance personalized learning experiences. The field of education has been significantly impacted by advancements in technology, with intelligent tutoring systems emerging as a promising tool to support individualized learning. This research aims to leverage machine learning techniques to create a system that can adapt to the unique needs and preferences of each learner, providing tailored instruction and feedback. The introduction section provides an overview of the motivation behind this research, highlighting the growing importance of personalized learning in the educational landscape. The background of the study delves into the evolution of intelligent tutoring systems and the role of machine learning in enhancing their capabilities. The problem statement identifies the existing challenges in traditional educational approaches and emphasizes the need for personalized learning solutions. The objectives of the study outline the specific goals of developing an intelligent tutoring system that can dynamically adjust its content and delivery based on individual learner characteristics. The limitations of the study acknowledge the constraints and potential challenges that may arise during the system development and evaluation process. The scope of the study defines the boundaries within which the research will be conducted, focusing on the implementation and testing of the ITS using selected machine learning algorithms. The significance of the study highlights the potential impact of the developed ITS on improving learning outcomes, engagement, and overall educational experiences for learners. The structure of the thesis provides a roadmap for the subsequent chapters, detailing the organization and flow of the research work. The definition of terms clarifies key concepts and terminology used throughout the thesis. The literature review chapter synthesizes existing research on intelligent tutoring systems, machine learning algorithms, and personalized learning approaches. It examines relevant studies and frameworks to inform the design and implementation of the ITS. The research methodology chapter outlines the approach, data collection methods, system architecture, algorithm selection, and evaluation criteria used in developing the intelligent tutoring system. The discussion of findings chapter presents the results of implementing the ITS, including system performance metrics, user feedback, and comparisons with traditional instructional methods. It analyzes the effectiveness of the machine learning algorithms in personalizing learning experiences and addresses any challenges or limitations encountered during the system development process. In conclusion, this thesis summarizes the key findings, contributions, and implications of developing an intelligent tutoring system using machine learning algorithms for personalized learning experiences. It reflects on the significance of the research outcomes and suggests future directions for improving and expanding the ITS to benefit a wider range of learners in diverse educational settings.