Advanced Data Aggregation in Online Education: a Contextual Web Parser Approach

Abstract

The paper presents a web aggregator system for collecting, filtering, and classifying data from educational platforms, focusing on online courses. It describes the development and testing of a system that uses contextual search to help users find courses matching their interests and knowledge level, while also handling spelling errors. The system's effectiveness is established through tests demonstrating its capability for rapid data collection and update, providing accurate and relevant results. The paper details the system's three-tier structure: data aggregation, user filtering, and user-system interaction for tailored course recommendations. The development involves a Python web server, a MariaDB database, a parser for non-formal education platforms, and a web application for client data presentation. In this paper also highlight the system's scalability and potential for integration with other educational platforms. Emphasize the importance of continuous updates to the database for maintaining relevance in a rapidly evolving online education landscape. Additionally, the paper discusses future enhancements, including the implementation of advanced machine learning algorithms for improved search accuracy and personalization, emphasizing the system's ongoing evolution to meet the dynamic needs of online learners.