Abstract

To increase the quality of education and to find solution to problems arising from complex educational dataset and competitive environment among the academic institutions, Educational Data Mining is receiving great attention. Student’s performance is of great concern to the higher education. In this paper, we have applied two approaches for educational data mining. The first approach is based on self-organizing map (SOM) which is a type of ANN (Artificial neural network) that is trained using unsupervised learning to produce low-dimensional views of high-dimensional data. Using this approach, we have clustered students based on certain attributes into natural classes so that similar classes are grouped together. The second approach uses pattern recognition through two-layer feedforward network to classify inputs into a set of target categories.