Prediction Of The Action Identification Levels Of Teachers Based On Organizational Commitment And Job Satisfaction By Using K-Nearest Neighbors Method

Abstract

In this paper, the data mining techniques, which arequite hot in educational environment, are used to predict the actionidentification levels of the teachers. To this end, the organizationalcommitment and the job satisfaction levels are used as input to the data miningtechniques. The well-known k-nearest neighbors (k-NN) approaches are considereddue to their simple and non-parametric nature. Six different k-NN methodsnamely; fine, medium, coarse, cosine, cubic and weighted k-NN are consideredand the obtained results are evaluated based on the prediction accuracy score.A dataset, which covers both organizational commitment and the job satisfactionlevels of the teachers, is collected from 126 teachers. Extensive experimentalstudies are carried out with 5-fold cross validation test in MATLAB environmentand the obtained results are recorded accordingly. The obtained results showthat the proposed scheme is quite successful in prediction of the actionidentification levels. Especially, for some of the action identificationlevels, the obtained accuracy scores are 88.1%, 89.7% and 93.6%, respectivelywhich show the success of the proposed idea.