Implementation of a cloud-based intelligent tutoring system



Abstract:

This thesis focuses on the implementation of a cloud-based intelligent tutoring system, which aims to enhance the learning experience of students by providing personalized and adaptive learning materials. The system utilizes artificial intelligence and machine learning algorithms to analyze the performance and preferences of individual students, thereby tailoring the content and pacing of instruction to meet their unique needs.

The first chapter of this thesis provides an introduction to the topic, discussing the significance of the study in the context of improving educational outcomes and addressing the individualized needs of diverse learners. The research objectives and hypothesis are outlined, along with a discussion of the limitations of the study.

Chapter two reviews existing literature on intelligent tutoring systems and cloud computing, providing a theoretical framework for the research. Chapter three details the research methodology, including data collection methods, algorithm development, and system implementation.

Chapter four presents the findings of the study, including an analysis of student performance and user satisfaction with the system. The implications of the findings are discussed in relation to the research objectives and hypothesis.

In the final chapter, a summary of the study’s key findings is presented, along with recommendations for future research and implications for practice. The thesis concludes with a discussion of the significance of the research and potential benefits of implementing a cloud-based intelligent tutoring system in educational settings.

Table of Contents:

Chapter 1: Introduction
– Significance of the Study
– Objectives of Study
– Study Hypothesis
– Limitations of Study

Chapter 2: Literature Review
– Intelligent Tutoring Systems
– Cloud Computing in Education
– Theoretical Framework

Chapter 3: Research Methodology
– Data Collection Methods
– Algorithm Development
– System Implementation

Chapter 4: Discussion of Findings
– Analysis of Student Performance
– User Satisfaction
– Implications of Findings

Chapter 5: Summary, Recommendation, and Conclusion
– Summary of Findings
– Recommendations for Future Research
– Implications for Practice


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