Object-oriented online course recommendation systems based on deep neural networks

In the era of widespread online learning platforms, students commonly face the challenge of navigating an extensive array of available courses. Identifying relevant and fitting options aligned with students' educational objectives and interests is highly complex. The impact of system maintainability and scalability on escalated development costs is often neglected in the literature. To tackle these issues, this paper introduces a comprehensive analysis and design of an object-oriented online course recommendation system. Employing a deep neural network algorithm for course recommendation, our system adeptly captures user preferences, course attributes, and intricate relationships between them. This methodology facilitates the delivery of personalized course recommendations precisely tailored to individual needs and preferences. The incorporation of object-oriented design principles such as encapsulation, inheritance, and polymorphism ensure modularity, maintainability, and extensibility, thereby easing future system enhancements and adaptations. The main contribution of this paper is to propose a new idea of an adaptive learning system that combines deep learning for personalized recommendations with object-oriented design for scalability and continuous improvement. This practical solution demonstrably enhances online learning experiences by tailoring recommendations to individual needs and evolving trends. Evaluation of the proposed system's performance utilizes real-world online course datasets, demonstrating its efficacy in furnishing accurate and personalized course recommendations, ultimately enhancing the overall learning experience for students.

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