Using Localized Features for Analyzing College Students’ Imagination
Chuan-Ming Liu 1  
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Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TAIWAN
Online publish date: 2019-02-01
Publish date: 2019-02-01
EURASIA J. Math., Sci Tech. Ed 2019;15(4):em1700
The analysis of imagination has become popular in recent years because imagination is one of the key components of creativity and innovation. For extracting students’ implicit degrees and thought processes of imagination, we use frequent pattern mining and association rule extraction to localize the features and explain the deep meanings of imagination in the study. By our observations, these two methods may sometimes explore meaningless frequent patterns and rules on a global sparse dataset. In order to eliminate such phenomena when mining with these two methods, we use a localized feature approach called forecast with clustering and integration (FCI) to improve the drawbacks of two methods on a sparse dataset. The approach consists of two strategies. One is clustering and the other is the prediction based on integration from (1) frequent patterns, (2) association rule pruning with correlation, and (3) forecast with linear regression. The former strategy can reduce the number of samples and highlight the features of imagination and the latter strategy can prune meaningless information and predict the trend of scores from imagination input data. Experimental results show both proposed approaches can localize special features, thereby providing supervisors with meaningful information about students’ degrees and thought processes of imagination.
Aher, S. B., & Lobo, L. (2011). Data mining in educational system using weka. In International Conference on Emerging Technology Trends (ICETT), 3, 20–25.
Cant, J. (2012). IERG— A brief guide to imaginative education. Retrieved from
Eisner, E. (2001). The educational imagination: On the design and evaluation of school programs (3rd edition). (pp. 1–389). Pearson.
Ge, J., & Xia, Y. (2016). Distributed sequential pattern mining in large scale uncertain databases. PacificAsia Conference on Knowledge Discovery and Data Mining, Springer, pp 17–29.
Hahsler, M., & Karpienko, R. (2017). Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3), 317–335.
Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent pattern tree approach. Data mining and knowledge discovery, 8(1), 53–87.
Ho, H. C., Wang, C. C., & Cheng, Y. Y. (2013). Analysis of the scientific imagination process. Thinking Skills and Creativity, 10, 68–78.
Huang, C. Y., Kao, Y. S., Lu, H. H., & Wu, M. J. (2017). Curriculum development for enhancing the imagination in the technology commercialization process. Eurasia Journal of Mathematics, Science and Technology Education, 13(9), 6249–6283.
Inokuchi, A., Washio, T., & Motoda, H. (2000). An apriori-based algorithm for mining frequent substructures from graph data. Principles of Data Mining and Knowledge Discovery, 13–23.
Khan, K., Rehman, S. U., Aziz, K., Fong, S., Sarasvady, S., & Vishwa, A. (2014). Dbscan: Past, present and future. Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference, IEEE, pp. 232–238.
Kurumalla, S., & Rao, P. S. (2016a). K-nearest neighbor based dbscan clustering algorithm for image segmentation. Journal of Theoretical and Applied Information Technology, 92(2), 395.
Lin, H. H., & Tsau, S. Y. (2012). The development of an imaginative thinking scale. Imagination, Cognition and Personality, 32(3), 207–238.
Lindqvist, G. (2003). Vygotsky’s theory of creativity. Creativity Research Journal, 15(2-3), 245–251.
Safar, A., & Alkhezzi, F. (2016). Students’ perspectives of the impact of online streaming media on teaching and learning at the college of education at kuwait university. Eurasia Journal of Mathematics, Science & Technology Education, 12(12), 2975–2989.
Salleb-Aouissi, A., Vrain, C., & Nortet, C. (2007). QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules. International Joint Conference on Artificial Intelligence (IJCAI), pp. 1035–1040.
Slater, S., Joksimovi´c, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2017). Tools for educational data mining: A review. Journal of Educational and Behavioral Statistics 42(1), 85–106.
Vygotsky, L. S. (2004). Imagination and creativity in childhood (english translation). Journal of Russian and east European psychology, 42(1), 7–97.
Wang, H., Chu, H., Huang, J., & Kang, S. (2010). A road to imagination: The ideal training model and its application in college teaching. In Proceedings of the TERA Conference on Education-Imagination of future education, Kaohsiung, Taiwan.
Williams, F. E. (1980). Creativity assessment packet. Buffalo, NY: D.O.K.