A LEAN SIX SIGMA FRAMEWORK FOR FACULTY MONITORING AND EVALUATION: Incorporating Data Analytics, Machine Learning, and Process Management

Abstract

This study presents a Lean Six Sigma framework tailored for faculty monitoring and evaluation in educational institutions, integrating advanced data analytics, machine learning, and process management methodologies. Addressing the limitations of traditional faculty evaluation systems, this research introduces a streamlined, data-driven approach to enhance the effectiveness and reliability of performance assessments. The framework uniquely combines theoretical principles with practical applications, demonstrating its scalability and alignment with broader institutional goals such as accreditation, improved student outcomes, and strategic decision-making.
Key elements of the study include a comprehensive analysis of existing faculty evaluation methods, a detailed conceptual framework illustrating the integration of Lean Six Sigma and Natural Language Processing, and a critique of related literature grouped into thematic categories. The research utilizes a mixed-methods approach to validate the framework, incorporating pilot testing, triangulation, and system reliability measures. Findings underscore the potential of this innovative framework to drive improvements in faculty performance, institutional policies, and regional educational reforms, offering valuable insights for the broader educational technology community.

Keywords: Lean Six Sigma, faculty evaluation, data analytics, machine learning, process management, Natural Language Processing, educational technology, institutional performance, mixed-methods research, system scalability.