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

Year 2018, Volume: 13 Issue: 2, 61 - 68, 19.09.2018

Dönüş Şengür , Muhammed Turhan

Abstract

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

Keywords

Data mining, k-nearest neighbors, organization commitment, job satisfaction, action identification levels

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