Published Paper Details:

Online education has seen the rise of EdTech freelancing platforms, which have become essential tools for learners, educators, and freelancers. These platforms function as dynamic marketplaces, providing a wide range of educational material and services to a worldwide audience. A key factor in their success is their ability to provide effortless content discovery and interaction through advanced search algorithms and optimization of user experience. This study explores the utilization of Term Frequency-Inverse Document Frequency (TF-IDF) algorithms to improve content discovery in EdTech freelance platforms. TF-IDF is a fundamental approach in information retrieval that offers a strong foundation for evaluating textual input and generating customized search results. TF-IDF allows platforms to prioritize material that fits with user preferences and search intent by giving weights to phrases based on their value within each learning resource compared to the total corpus. This study examines the installation, difficulties, and benefits of TF-IDF algorithms in enhancing search accuracy, user satisfaction, and overall platform efficiency. This paper explores the important role of semantic analysis in defining the future of online education by thoroughly examining TF-IDF-based search engines. TF-IDF plays a crucial role in driving innovation and change in EdTech freelancing platforms, as the need for individualized and accessible learning experiences increases. It promotes a worldwide culture of lifelong learning and information sharing