Abstract
To tackle with the blindness of random questions choosing for exercise and test of the on-line learning system, this paper clusters questions exploiting various feature subsets and parameters via K-means. For the test data of ACM Online Judge system, the features of temporal fluctuations mean of time consumption and repeat submission rate are used to make the question categorization and automatic recommendation come true. The experimental results suggest that the proposed method is simple but effective, and by which an on-line test platform can realize functions such as individuation teaching, intelligently questions choosing, teaching instruction, automatically paper constructing and paper difficult prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chonghui, G., Fengzhan, T.: Data Mining Tutorial. Tsinghua University Press, pp. 107–121 (2012)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn, pp. 11–12. Machine Press, Beijing (2003)
Aiwu, Z., Baolou, C., Yan, W.: Study and improve on k-means algorithm. Comput. Technol. Dev. 22(10), 101–104 (2012)
Jigui, S., Jie, L., Lianyu, Z.: Clustering algorithms research. J. Softw. 19, 48–61 (2008)
Jiaxia, S., Xueyong, L.: The algorithm and design of the test difficulty coefficient determined by classical test theory. China Sci. Technol. Inf. 19(1), 44–45 (2009)
Zhijie, L., Yuanxiang, L., Feng, W., Li, K.: Accelerated multi task online learning algorithm for big data stream. J. Comput. Res. Dev. 52(11), 25–45 (2015)
Zhexue, H.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2, 283–304 (1998)
Yiling, H., Xiaoqing, G., Chun, Z.: Modeling and mining of online learning behavior analysis. Open Educ. Res. 20(2), 102 (2014)
Barbara, D.: Using Self-similarity to cluster large data sets. Data Min. Knowl. Disc. 7, 123–152 (2003)
Modha, D.S., Spangler, W.S.: Feature weighting. k-means clustering. Mach. Learn. 52, 217–237 (2003)
Acknowledgments
This research was supported by National Natural Science Foundation of China (No. 61302128), the Youth Science and Technology Star Program of Jinan City (201406003), the Teaching Reform Research Project in Undergraduate College of Shandong Province (2016), Industry-University Cooperative Education Project of Ministry of Education (No. 201601023018), the Scientific Research Fund of Jinan University (No. XKY1622) and Teaching Research Project of Jinan University (No. J1638)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fan, Y., Xu, T., Dong, L., Wang, D. (2017). The Study on Grade Categorization Model of Question Based on on-Line Test Data. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_69
Download citation
DOI: https://doi.org/10.1007/978-3-319-63312-1_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63311-4
Online ISBN: 978-3-319-63312-1
eBook Packages: Computer ScienceComputer Science (R0)