Middle School Students Creativity Assessment with Neural Networks

Abstract:

Williams Creativity Test B (WCTB) and Adolescent Scientific Creativity Scale (ASCS) were used to measure the creative affective and scientific creativity for 550 middle school students. SOM network was used to cluster the measurement data of scientific creativity. 550 middle school students were clustered to three categories. In these students, 70% of them were used as training group, and the other as testing group. Probabilistic neural network (PNN) and multinomial logistic regression (MLR) were used for modeling and testing. Risk-taking, curiosity, imagination and complexity scores used as input and independent variable, scientific creative categories used as output and dependent variable. The result showed the Percent Correct (PC) of PNN was higher than the PC of MLR.

• Periodical: Advanced Materials Research (Volumes 271-273)
• Pages: 1443-1446
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.271-273.1443
• Online since: July 2011
• Authors: Jia Yuan Yu
• Keywords: Creativity, Genetic Algorithm (GA), Neural Network (NN)

References

[1] W. Hu: The Development and Training of Scientific Creativity for Adolescence (Beijing Normal University Publishing House, China 2003).

[2] C. Zhao, and Z. Chen: New Development of Creative Education (Shandong People's Publishing House, China 2002).

[3] J. Duan: Experimental study of creativity training for primary school. Journal of China Education, Vol. 6, (2001), pp.24-28.

[4] W. Zhang: Creativity Development of Students (Huayi Publishing House, China 1999).

[5] X. Qin, and Yuan: Text clustering method based on genetic algorithm and SOM network. Computer Applications, Vol. 28, (2008), pp.757-760.

DOI: 10.3724/sp.j.1087.2008.00757

[6] M. Cyders, G. Smith, and N. Spillane: Integration of impulsivity and positive mood to predict risky behavior. Psychological Assessment, Vol. 19, (2007), pp.107-118.

DOI: 10.1037/1040-3590.19.1.107