Application of Artificial Neural Networks to Assess Student Happiness

Abstract

The purpose of this study is to develop an analytical assessment approach to identify the main factors that affect graduate students' happiness level. Two methods, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), were employed for analytical modeling. A sample of 118 students at a small non-profit private university constituted the survey pool. Various factors including education, school facilities, health, social activities, and family were taken into consideration as a result of literature review in happiness assessment. A total of 32 inputs and 1 output variables were identified during survey design phase. Following survey conduction, data collection, cleaning, and preparation; MLR and ANNs were built. ANN models provided better classification performance with over 0.7 Rsquare and a smaller standard error of estimate compared to MLR. Major policy areas to improve student happiness levels were identified as career services, financial aid, parking and dining services.