Course Navigator: An Intelligent Course Recommendation System using Machine Learning

Abstract

The rapid expansion of digital learning platforms has significantly increased access to educational resources; however, it has also introduced complexity in course selection for engineering students. With thousands of courses available across platforms such as Udemy, NPTEL, and YouTube, students often struggle to identify relevant courses aligned with their academic background, skill level, and financial constraints. This paper presents a Smart Course Recommendation System developed using Python and a dynamic database to provide structured and personalized course suggestions. The system is based on three primary parameters: engineering branch, proficiency level (beginner, intermediate, expert), and budget range. A rule-based filtering approach is implemented to ensure simplicity, efficiency, and accuracy without requiring complex machine learning models. The system guarantees a minimum of ten recommendations and provides direct links to learning platforms. It is designed to be scalable, user-friendly, and suitable for academic environments, particularly for diploma and undergraduate engineering students. The proposed solution improves decision-making, reduces search time, and promotes cost-effective learning.

Introduction

The passage presents a Smart Course Recommendation System designed to help engineering students choose suitable online courses from platforms like Udemy, NPTEL, and YouTube.

It highlights a key problem: although online learning offers abundant resources, students struggle with decision overload, lacking guidance that considers their branch, skill level, and budget. Existing platforms use generic recommendation methods and fail to provide personalized, structured, and cost-aware suggestions.

To solve this, the proposed system uses a rule-based recommendation approach instead of complex machine learning models. It collects user inputs such as engineering branch, skill level, and budget, then filters a structured course database accordingly.

The system workflow includes:

• User registration and login
• Input collection and validation
• Database querying of courses (from multiple platforms)
• Rule-based filtering (branch match, skill level, budget constraints)
• Ranking and displaying at least 10 relevant courses

The architecture is built using a three-tier model:

• Presentation layer (user interface)
• Application layer (Flask backend logic)
• Data layer (SQLite/MySQL database)

The system is designed for simplicity, speed, and low computational cost, avoiding issues like cold-start problems seen in ML-based recommenders. It provides fast, personalized, and budget-aware course recommendations.

Conclusion

The development and implementation of the Smart Course Recommendation System represent a significant advancement in addressing the challenges associated with course selection in modern digital learning environments. With the exponential growth of online educational platforms such as Udemy, NPTEL, and YouTube, students are often overwhelmed by the vast number of available courses, leading to confusion, inefficient decision-making, and suboptimal learning outcomes. The proposed system successfully introduces a structured, rule-based recommendation mechanism that integrates three critical parameters: engineering branch, skill level, and budget constraints. By incorporating these factors into a unified filtering framework, the system ensures that the recommendations provided are not only relevant but also practical and affordable for students. Unlike traditional recommendation systems that rely heavily on complex machine learning models, the presented approach emphasizes simplicity, interpretability, and computational efficiency, making it highly suitable for academic environments and small-scale deployments. The implementation using Python and the Flask framework demonstrates that a lightweight architecture can effectively deliver personalized recommendations without requiring extensive computational resources or large-scale datasets. The modular design of the system—including user authentication, input processing, database management, and filtering logic—ensures scalability and ease of maintenance. Furthermore, the integration of a structured database enables efficient querying and retrieval of course information, thereby reducing response time and enhancing user experience. Experimental observations indicate that the system performs consistently across various input scenarios, accurately filtering courses and providing meaningful recommendations. The ability to guarantee a minimum number of course suggestions ensures usability, while the inclusion of direct links to learning platforms enhances accessibility and user convenience. From a broader perspective, this system contributes to the domain of personalized learning by demonstrating how rule-based intelligence can be effectively utilized to solve real-world educational problems. It bridges the gap between the abundance of online learning resources and the specific needs of students, thereby promoting informed decision-making and encouraging skill development. However, while the system achieves its intended objectives, certain limitations remain. The absence of real-time data integration and user behavior tracking restricts the level of personalization. Additionally, the reliance on a manually updated database may affect scalability in large-scale applications. Future enhancements may include the integration of machine learning techniques for adaptive recommendations, real-time APIbased course fetching, user profiling, and the development of a mobile application interface. Such improvements would further enhance the system’s capability to deliver dynamic, intelligent, and user-centric recommendations. In conclusion, the Smart Course Recommendation System provides a practical, efficient, and scalable solution to the problem of course selection. It highlights the potential of combining simple algorithmic approaches with structured data systems to create impactful applications in the field of education technology. The system not only simplifies the decision-making process for students but also contributes to the advancement of accessible and personalized digital learning ecosystems.

References

[1] Udemy, “Online Courses for Engineering Students,” [Online]. Available: https://www.udemy.com/. [Accessed: Mar. 21, 2026]. [2] YouTube, “Educational Courses and Tutorials,” [Online]. Available: https://www.youtube.com/. [Accessed: Mar. 21, 2026]. [3] Coursera, “Engineering and Computer Science Courses,” [Online]. Available: https://www.coursera.org/. [Accessed: Mar. 21, 2026]. [4] edX, “Professional Certificate Programs,” [Online]. Available: https://www.edx.org/. [Accessed: Mar. 21, 2026]. [5] GeeksforGeeks, “Programming and Engineering Courses,” [Online]. Available: https://www.geeksforgeeks.org/. [Accessed: Mar. 21, 2026]. [6] Kaggle, “Machine Learning and Data Science Resources,” [Online]. Available: https://www.kaggle.com/. [Accessed: Mar. 21, 2026]. [7] Python Software Foundation, “Python Documentation,” [Online]. Available: https://docs.python.org/3/. [8] Flask, “Flask Web Development Framework Documentation,” [Online]. Available: https://flask.palletsprojects.com/.

Copyright

Copyright © 2026 Prof. Sonali Patil , Nidhi Pawar, Sonali Kumbhar, Anushri Shetye, Dhanvi Sakharkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.