Neural Network Model for Performance Evaluation of Academic Staff of Tertiary Institutions

Abstract

Recent interest in Human Resource management in Universities is to develop model for evaluating performance of academic staff. In this study, Artificial Neural network was used for evaluating performance of academic staff of tertiary institutions. Fifteen Human Resource Metrics were considered which were classified under three main factors namely: Research, Teaching and service. These are the major' Human Resource foci of the tertiary institutions. The datasets were divided into three: train, validation and test data. The train data was presented to Supervised Neural network to approximate the fifteen Human Resource variables. Data from ten (10) randomly selected institutions were used to test and validate the system. The learning parameters for the training and testing of the survey data varied from 0.07 to 0.1 and the momentum parameter approached zero value (0.01 to 0.03). Root Mean Square Error (RMSE) was computed for both the parametric models (Principal Component 0.80 and Factor Analysis 0.15). The result revealed that Multilayer Perceptron Neural Network (MPNN) with' back propagation algorithm got better outcome when compared with those parametric models. Experimental results in this study demonstrated that MPNN based model can closely predict the Human Resource metrics. Considering the maximum percentage of correct output with minimum RSME at 90%, the Performance Evaluation Model was found to be optimal model for predictive power for the academic staff of tertiary institutions.