The aim of all education systems is to train students who are equipped with knowledge. In that case, that student is able to determine the most suitable profession for him/her success in education and career that are related to this profession will be higher. Studies done up to this day have been focused on finding out the factors affecting the career choice of the student, but they have not suggested any method for determining the most suitable procession. It is not possible to obtain satisfying results from a system that does not lead students to appropriate higher education departments. In this context, a student-department matching system is proposed which aims to increase the success of the education systems in our study. The department of computer engineering was dealt with as a sample department and the proposed study was examined to determine whether a student was suitable for computer engineering or. The required data was obtained with the help of the questionnaire, and then a model of successful and unsuccessful students was created. Data mining algorithms such as C4.5, C-SVC, MLP, and Naïve Bayes are used during the test of the generated model. The best result was obtained by the C-SVC algorithm and the second best result by Naive Bayes. The lowest error rate achieved was 0.2700 and the highest accurate recognition rate was 73.00%.
Abstract-Less than optimal choice of the university department is one of the serious problems Turkish high school students have been suffering. There are a number of potential factors affecting the student's choice of her future profession. Some of these have received attention in the literature, but such studies do not always involve an investigation of the relationship between the factors analyzed and subsequent levels of academic achievement. The present study examines the relationship between the level of academic achievement and the students' abilities, interests and expectations, by using different data mining methods and classifiers, as a preliminary work to develop a system that will guide the student to selecting a career that will be a better match for her in the future. C4.5, SVM, Naive Bayes and MLP algorithms are used for the analysis; 10-fold cross validation and train-test validation are used as models to evaluate the classifiers results. The student feature set is obtained through questionnaires and psychometric tests. The questionnaire and the psychometric test were applied to 210 and 52 students respectively, from the Computer Engineering Department at Cumhuriyet University. The class was labeled either "successful" or "unsuccessful" with reference to the grades received by each student in computer engineering courses. The comparisons of various data mining algorithms, different data set results, and models used are presented and discussed.
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