Conceptual modeling with neural network for giftedness identification and education

Authors:
Kwang Hyuk Im;Tae Hyun Kim;SungMin Bae;Sang Chan Park
Affiliations:
Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Department of Industrial & Management Engineering, HANBAT National University, Daejeon, Korea;Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 5
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Data mining: concepts and techniques

Data mining: concepts and techniques
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Machine Learning

Machine Learning
Feature Construction for Student Group Forming Based on Their Browsing Behaviors in an E-learning System

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.