This thesis introduces a self-learning system for web-based NodeJS programming with an automated assistance mechanism, aimed at empowering students to engage in autonomous study and enhance their understanding and proficiency in database connection, web programming, and RESTful APIs on the NodeJS Express framework. Through the evaluation of the self-learning system, it is evident that students benefit from the clear and concise learning materials, leading to increased engagement levels and better understanding. By providing automated assistance, students receive real-time feedback on their code, allowing them to quickly detect and correct mistakes. By providing an automatic support mechanism and extensive learning resources, the research contributes to the growth of web-based NodeJS programming education. This study contributes to the profession by bridging the gap between traditional classroom instruction and practical application, helping students to gain the skills they need to succeed in the ever-changing landscape of web development. Future enhancements include more simplified learning materials and better resource management for third-party connections. The data support the notion that a clear and succinct learning strategy improves student engagement and comprehension, providing a promising avenue for self-directed learning in NodeJS programming.