Conceptual modeling with neural network for giftedness identification and education

Abstract

Today, gifted and talented education becomes an important part of school education. All school staff has increased awareness and knowledge about that. They develop a special program for identification of gifted student and a curriculum for them. In addition, existing gifted education pays too much attention to their curriculum, such as a curriculum compacting, acceleration, and an ability clustering. Currently, the identification of gifted student mainly depends on a simple identification test based on their age. But, the test results could not reveal the "potentially gifted" students. In this paper, we proposed a neural network model for identification of gifted student. With a specially designed questionnaire, we measure implicit capabilities of giftedness and cluster the students with similar characteristics. The neural network and data mining techniques are applied to extract a type of giftedness and their characteristics. To evaluate our model, we apply our model to the science and liberal art filed in Korea to identify gifted student and their type of giftedness.