Predicting the status of the graduating students in distance learning with the help of data mining methods:Amasya University Sample

Correct orientation of students during education is very crucial for preventing future failures. Students who cannot graduate at the time may cause negative impacts on the family and the country's economy as well as the decrease of the young labor force. This situation requires the studies to be held concerning the students who cannot graduate in time. Analyzing the educational data related to the students is included in this kind studies. Considering the accumulation of large number of educational data in higher education institutions, it becomes more important to analyze these data with various methods. In data mining, which is one of the methods used to analyze the data, estimation, classification and clustering methods are benefited. In this study, inferences were tried to be made about whether the students who enrolled to Amasya University Distance Education Child Development, Medical Documentation and Secretariat Associated Degree Programs in 2016-2017 will be able to graduate on time or not. Decision tree, Naive Bayes, Support vector machine, Random forest and Artificial neural networks algorithms are used for estimation. The classification performance criteria (accuracy, kappa, recall, precision, f-measure) which occurs as the result of the analysis with algorithms are included comparatively in the study.