A COMPARATIVE STUDY OF PREDICTING TEACHING SCORE BY USING CLASSIFICATION ALGORITHMS

Datamining and analysis

Today is Educational Age. Education is an essential element for the progress of country. Educational institutions/ colleges have goals to deliver quality education so, one way to improve the level of education quality in higher education is that measure good performance of teachers’ with higher teaching skill. Teacher training, learning time of related area and teaching experience are very important for qualified teacher. Data mining is used in higher education for analysis, predicting and evaluating the performance of student, teacher and other that have a role in the educational system. Mining in educational environment is called Educational Data Mining (EDM). Educational data mining is concerned with developing new methods to discover knowledge from educational database. There are varieties of popular data mining task within the educational data mining e.g. classification, clustering, outlier detection, association rule, prediction etc. The proposed system can able to predict teaching skill of teachers’ using classification model under the data set of a colleagues and students evaluation and also identify factors that affect the qualification of teachers. This paper emphasizes decision tree (J48), Random Forest and Naive Bayes classification algorithms for classification model using Weka tool. Weka is data mining software that uses a collection of machine learning algorithms. These algorithms can be applied directly to the data or called from the Java code. Weka is a collection of tools for regression, clustering, association, data pre-processing, classification, and visualization. In this paper, data set is collected information and results of a survey about 65 teachers and questionaries 200 students on teacher's behaviors in classroom, university of Computer Studies, Kalay (Burma).

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