Developing an Algorithm for Personalized Biotech Education: User Clustering and Preference Analysis to Enhance Online Learning Resources

Abstract

This paper introduces an innovative algorithm designed to enhance online learning for biotechnology by recommending personalized educational resources based on user preferences. Initially developed for diverse disciplines such as dance, the model utilizes advanced clustering techniques to analyze user preference data collected from a biotechnological online discussion forum. Tested on over 1 million users, the algorithm effectively identifies user segments with similar educational interests and needs, facilitating the formation of virtual learning communities within the biotech field. Employing k-means clustering, a popular method known for its robustness across various applications, the algorithm navigates through the complex patterns of learner interactions and preferences. Despite k-means' linear limitations, this study extends its utility by integrating density estimation techniques to enhance predictive accuracy and resource recommendation relevance. The results demonstrate the algorithm's capability to dynamically connect learners with the most relevant biotechnological resources, thereby fostering enhanced educational exchanges across geographic and professional boundaries. This tailored approach not only supports personalized learning paths but also promotes the formation of collaborative and interactive biotech learning communities, pivotal for the ongoing development of skills and knowledge in the field of commercial biotechnology.