Denir inanç ağını kullanarak öğrencilerin performans tahmini

Education research involving techniques of data mining is growing rapidly. Data mining techniques are known as educational information mining to explore hidden knowledge and patterns of student performance in educational backgrounds. This work seeks to develop an academic prediction model for the student for the UCI (University of California Irvine) machine learning repository dataset selected in this work for prediction. Internal marks are a combination of attendance marks, average marks from two examinations and marks of assignment. The teachers can therefore classify students and start predicting their performance at an early stage. To improve performance over time, systemic approaches can be adopted. Better results can be expected at the final examinations due to early predictions and solutions.