Faculty attendance performance monitoring and decision support system using multiple linear regression algorithm

Abstract

The Faculty Attendance Performance Monitoring and Decision Support System Using Multiple Linear Regression Algorithm is a system designed to organize and support decisions on University scheduling of courses through analysis of faculty performance through multiple linear regression algorithm. The system allows profiling of faculty, buildings, courses, and schedules through input or through import though a batch file. Faculty Attendance and Monitoring enhanced though auto filtering and sorting based on the need of the attendance checker, streamlining only important schedules at the current time and place. Using Multiple Linear Regression Algorithm, the system then analyzes whether a faculty is best fit for a certain schedule or not. The analysis is can be used as a guide by the schedule coordinator on assignment of schedules during the beginning of the semester. The methodology used is Evolutionary Prototyping Model, a variant of a prototyping methodology where the prototype is not discarded rather used as an initial resource for developing the next prototype based on the customer’s feedback after testing the prototype. The method has six (6) key phases: Planning, Analysis, Design, Prototype Implementation, Customer Feedback, Final Testing and Implementation and Deployment. The processes from the Analysis Phase to the Customer Feedback Phase comprise the Prototype Building, the core of the system development. The Faculty Attendance Performance Monitoring and Decision Support System Using Multiple Linear Regression Algorithm provides the university the support in making the optimal decisions in the matter of scheduling and monitoring of faculty performance.