A AI based Model for Achieving High Reliability Faculty Performance Using Various Machine Learning Algorithms - M. Garces, Charlene Grace (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The objective of this research is aimed to predict performance through using machine learning algorithms. Faculty performance is quintessential to ensure effective pedagogical and educational objectives. Nonetheless, the evaluation of teachers has been used to be a manual and temperamental task for school administrators The concern about the student evaluation instrument which is most widely applied tool to evaluate faculty performances in a university is generally grounded on students not having enough experience and/or being affected by influence of the course and grades given by the teacher concerned and the course being compulsory or elective. In achieving the objectives, this research uses factors like the length of service, designation, academic rank, workload, and the demographic profile of the faculty. Loaded with the availability of the dataset, a data mining technique simulation in MATLAB R2021B software tool using various machine learning algorithms namely Support Vector Machine (SVM), Decision Tree – Fine Tree, and Ensemble- Bagged Tree. It is clearly observable that the Ensemble Bagged Tree algorithm emerged with the higher accuracy results.