Analysis of Special Children Education Using Data Mining Approach

Abstract

Data mining is a method that gives valuable information where we can improve and achieve the goal. Existing data mining techniques are applied in education for analyzing the performance of students, classes, and institutions. This helps the teachers and management to identify where they lag and improvise. Some mining techniques are used to predict and identify special children’s categories. In this paper, data mining techniques are applied for the special children to predict the achievement in special school study for child categories like Mental Retardation (MR), autism, and cerebral palsy using the collected assessment detail. Vocational training is considered an essential aspect for special school children for their future survival. Data mining methods are applied to mine the most achievable and essential training factors as a pattern by apriori rule mining and high utility pattern mining algorithms, based on their assessment achieved from the age 10–14, where Madras developmental programming scale is followed in special schools. We can predict which category children are achieving necessary factors, which helps identify the alternate methods to train the particular activity. Essential factors that cannot achieve at the same level will be trained in the next prevocational level to reach their vocational training. The prediction that we have made will be helpful for the teachers to train the children concerning their achievements.