Analyzing Students’ Educational Information to Evaluate Their Success via Using Data Mining Method (Case Study: Faculty of Management and Industrial Engineering, Shahrood University of Technology)

Knowledge acquisition about the behavior patterns of the educational status of students can have a significant impact on the successful management of educational institutes and provides appropriate services in accordance with current and future status of students. This knowledge is hidden in educational data and could be extracted via using data mining method. In this paper, a data mining model to analyze students’ academic achievement based on the data related to before and after the students’ entrance to university was proposed. To tackle this problem, an advanced hierarchical clustering method was developed. In addition, a forecasting model to determine the belonging of each student to the each considered cluster was provided. The proposed model was evaluated on a case study of the Faculty of Management in Shahrood University of Technology. The obtained results were validated by experts and university administrators. The students were classified in different clusters via considering theirs specific features. The results indicated that some of the considered features related to before and after entering university have significant impacts on determining the students’ educational achievements and also their assigned cluster. Some decision rules are proposed for educational administrators to improve the overall students’ educational achievements and transferring students from clusters with poor academic situations to better ones such as applied rules for transferring students with special status. In addition, an efficient forecasting model for assessment of students' achievement states was proposed. The obtained results were verified by experts.