A data mining-driven model for job satisfaction prediction of school administrators in DepEd Surigao del Norte division

Abstract

Among managerial researches, job satisfaction has long been acclaimed as the prime index of worker’s wellbeing as well as a core motivational construct that has key implications for organizational and administrative policy. Talking about administrative policy, some existing problems identified in every organization especially in schools and institutions are the proliferation of data and how those data will be an aid for decision making and intervention programs. To bridge the gap, research is the viewed solution. However, despite the advent of technology, there are still researchers who rely on old methods and still used predefined queries and charts. They are often unlikely to accomplish a well-established knowledge extraction from databases using the old and traditional methods. As the regarded solution, the use of data mining techniques in the educational context is encouraged as it optimized the process of data extraction and knowledge discovery. In this paper, the use of predictive models such as Naïve Bayes, C4.5, and KNN algorithms were observed. The simulation result shows that the C4.5 algorithm is the optimal model for prediction of job satisfaction as perceived by the 157 school administrator-respondents from the Department of Education, Division of Surigao del Norte, Philippines. An accuracy of 80.89%, 74.52%, and 71.97% was obtained using C4.5, Naïve Bayes, and K-Nearest Neighbor (KNN) algorithms, respectively.