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Introduction

With the increasing demand for programming skills in today’s technology-driven world, the need for effective and efficient ways to learn programming has become more critical than ever. Traditional methods of teaching programming often lack personalized assistance and feedback, making it challenging for students to grasp complex concepts and skills. This has led to the development of intelligent tutoring systems (ITS) for programming, which aim to provide individualized instruction and support to students.

This thesis presents the design and implementation of an intelligent tutoring system for programming, which incorporates adaptive learning techniques to tailor instruction to the needs and abilities of each student. By leveraging machine learning algorithms and educational theories, the system aims to improve the learning experience and outcomes of students studying programming.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Intelligent Tutoring Systems
2.2 Programming Education and Challenges
2.3 Adaptive Learning in Education
2.4 Machine Learning Techniques in Education
2.5 Previous ITS for Programming
2.6 Evaluation Methods for ITS
2.7 Pedagogical Theories in Programming Education
2.8 Cognitive Load Theory
2.9 Gamification in Education
2.10 Collaborative Learning in Programming

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Analysis
3.3 User Modeling
3.4 Content Recommendation System
3.5 Feedback Mechanism
3.6 Personalized Learning Paths
3.7 Adaptive Assessment
3.8 Integration of Educational Theories
3.9 User Interface Design
3.10 System Evaluation Framework

Chapter 4: System Implementation
4.1 Development Environment
4.2 Frontend Development
4.3 Backend Development
4.4 Database Design
4.5 Machine Learning Implementation
4.6 User Authentication and Authorization
4.7 API Integration
4.8 Testing and Quality Assurance

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications of the Study
5.3 Future Research Directions
5.4 Conclusion and Final Remarks

Thesis Overview

This thesis focuses on the development of an intelligent tutoring system for programming, aiming to enhance the learning experience and outcomes of students through personalized instruction and feedback. By incorporating adaptive learning techniques and machine learning algorithms, the system provides tailored support to each student based on their individual needs and abilities. The thesis includes a comprehensive literature review, system design and methodology, system implementation, and a conclusion summarizing the findings and implications of the study. The ultimate goal of this research is to contribute to the field of education technology and improve the effectiveness of programming education.

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