International Research Journal of Education and Technology

Faculty productivity plays a crucial role in academic institutions, directly influencing teaching quality, research output, and institutional performance. Traditional faculty evaluation methods often rely on manual assessments and static metrics, leading to inefficiencies and potential biases. To address these challenges, I propose a Faculty Productivity Analysis Dashboard, a data-driven web application designed to analyze and visualize faculty workload, research contributions, and teaching performance. Developed using Streamlit, the dashboard integrates structured datasets on faculty activities, including teaching hours, research publications, and administrative responsibilities. Through statistical analysis, I identify key workload trends, revealing that excessive teaching commitments often correlate with reduced research output. Additionally, I explore productivity models such as Maslow’s Hierarchy of Needs and the 80/20 Rule to understand factors influencing faculty performance. My findings demonstrate that interactive visualization tools can enhance decision-making in faculty assessment, helping administrators optimize workload distribution. This study contributes to data-driven faculty evaluation methodologies and lays the foundation for future improvements, such as AI-driven productivity predictions and sentiment analysis of student feedback.

Key Words:  Faculty productivity, data analytics, workload assessment, dashboard visualization, academic efficiency