Cultivating Personalized Learning: A Web-based Data Dashboard and Analytics Using Python with Streamlit and Pandas

This research aims to develop a system that cultivates personalized learning in data dashboards and analytics using Python with Streamlit and Pandas libraries. The system filled the gaps on what tools educators could use to keep up with the fast-changing education—implementing the educator’s methodologies with customized learning from learners’ needs. The researchers enforced the Waterfall Methodology Model. It is aligned since the system was developed in swift and short only. The requirements are minimal, the design is simple and user-friendly, and the implementation, testing, and deployment were swift but necessary. Additionally, the researchers utilized Likart 5-point scale evaluation to test the effectiveness of the system on educators. The evaluators found the system overall satisfactory with its functionality, usability, reliability, efficiency, and portability. The system is an open-source tool dashboard designed for educators to visualize assessment data, helping to identify learners' needs and strengths for personalized learning. It features CSV export (to reduce runtime and cloud-based errors), achievement rankings, and report printing, enhancing adaptability for both educators and students. The researchers recommend integrating artificial intelligence (AI) and automation into the system. This research addresses the diverse learning methodologies in education and the need for technology to aid educators. Using Python with Streamlit and Pandas to create a tool for measuring data-driven analytics for personalized learning in schools and universities.