Data mining techniques for identifying students at risk of failing a computer proficiency test required for graduation

Australasian Journal of Educational Technology

Volumn 27, Issue 3, 2011, Pages 481-498

  Tsai, Chih Fong ^a   Tsai, Ching Tzu ^b   Hung, Chia Sheng ^c   Hwang, Po Sen ^b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^c NANHUA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79959973927     PISSN: None     EISSN: 14495554     Source Type: Journal    
DOI: 10.14742/ajet.956     Document Type: Article

References (32)

1. Budayan, C., Dikmen, I. & Birgonul, M. T. (2009). Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping. Expert Systems with Applications, 36(9), 11772-11781.
2. David, O. & Yong, S. (2007). Introduction to business data mining. Boston: McGraw-Hill/Irwin.
3. Guo, Q. & Zhang, M. (2009). Implement web learning environment based on data mining. Knowledge-Based Systems, 22(6), 439-442.
4. Guruler, H., Istanbullu, A. & Karahasan, M. (2010). A new student performance analysing system using knowledge discovery in higher educational databases. Computers & Education, 55(1), 247-254. http://dx.doi.org/10.1016/j.compedu.2010.01.010
5. Hamdi, M. S. (2007). MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students. Applied Soft Computing, 7(3), 746-771. http://hdl.handle.net/10576/10576
6. Han, J. & Kamber, M. (2006). Data mining: Concepts and techniques. New York: Morgan Kaufman.
7. Hannon, C. (2001). Information literacy in the undergraduate curriculum. Educause Quarterly, 24(4), 41-42. http://www.educause.edu/ir/library/pdf/EQM0146.pdf
8. Hosseini, S. M. S., Maleki, A. & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264. http://dx.doi.org/10.1016/j.eswa.2009.12.070
9. Jain, A. K. (2009). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666. http://dx.doi.org/10.1016/j.patrec.2009.09.011
10. Kanungo, T., Mount, D.M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A.Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881-892. http://dx.doi.org/10.1109/TPAMI.2002.1017616
11. Kao, Y.-T., Zahara, E. & Kao, I-W. (2008). A hybridized approach to data clustering. Expert Systems with Applications, 34(3), 1754-1762. http://www.sciencedirect.com/science/journal/09574174
12. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69. http://dx.doi.org/10.1007/BF00337288
13. Kohonen, T. (1989). Self-organization and associative memory, 3rd edition. New York: Springer-Verlag.
14. Kohonen, T. (2001). Self-organizing maps, 3rd edition. New York: Springer-Verlag.
15. Lee, M. W., Chen, S. Y., Chrysostomou, K. & Liu, X. (2009). Mining students' behavior in webbased learning programs. Expert Systems with Applications, 36(2), 3459-3464. http://dx.doi.org/10.1016/j.eswa.2008.02.054
16. Markov, Z. & Larose, D. T. (2007). Data mining the web: Uncovering patterns in web content, structure, and usage. New York: John Wiley & Sons.
17. Mingoti, S. A. & Lima, J. O. (2006). Comparing SOM neural network with Fuzzy c-means, Kmeans and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174(3), 1742-1759.
18. O'Hanlon, N. (2002). Net knowledge: Performance of new college students on an Internet skills proficiency test. The Internet and Higher Education, 5(1), 55-66. http://dx.doi.org/10.1016/S1096-7516(02)00066-0
19. Paterlini, S. & Krink, T. (2006). Differential evolution and particle swarm optimisation in partitional clustering. Computational Statistics & Data Analysis, 50(5), 1220-1247. http://dx.doi.org/10.1016/j.csda.2004.12.004
20. Qiu, D. (2010). A comparative study of the K-means algorithm and the normal mixture model for clustering: Bivariate homoscedastic case. Journal of Statistical Planning and Inference, 140(7), 1701-1711. http://dx.doi.org/10.1016/j.jspi.2009.12.025
21. Romero, C. & Ventura, S. (2006). Data mining in e-learning. Southampton. UK: WIT Press.
22. Romero, C. & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. http://dx.doi.org/10.1016/j.eswa.2006.04.005
23. Romero, C., Ventura, S. & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers and Education, 51(1), 368-384. http://dx.doi.org/10.1016/j.compedu.2007.05.016
24. Romero, C., Ventura, S., Zafra, A. & de Bra, P. (2009). Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers & Education, 53(3), 828-840. http://dx.doi.org/10.1016/j.compedu.2009.05.003
25. San, O. M., Huynh, V.-N. & Nakamori, Y. (2004). An alternative extension of the k-means algorithm for clustering categorical data. International Journal of Applied Mathematics and Computer Science, 14(2), 241-247.
26. Shih, C.-C., Chiang, D.-A., Lai, S.-W. & Hu, Y.-W. (2009). Applying hybrid data mining techniques to web-based self-assessment system of Study and Learning Strategies Inventory. Expert Systems with Applications, 36(3), 5523-5532. http://www.amcs.uz.zgora.pl/?action=paper&paper=198
27. Tang, T. & McCalla, G. (2005). Smart recommendation for an evolving e-learning system. International Journal on E-Learning, 4(1), 105-129.
28. Tesch, D., Murphy, M. & Crable, E. (2006). Implementation of a basic computer skills assessment mechanism for incoming freshmen. Information Systems Education Journal, 4(13), 1-11. http://isedj.org/4/13/ISEDJ.4(13).Tesch.pdf
29. Tsai, C.-F., Lin, Y.-C. & Wang, Y.-T. (2009). Discovering stock trading preferences by selforganizing maps and decision trees. International Journal on Artificial Intelligence Tools, 18(4), pp. 603-611. http://dx.doi.org/10.1142/S0218213009000299
30. Verhey, M. P. (1999). Information literacy in an undergraduate nursing curriculum: Development, implementation, and evaluation. Journal of Nursing Education, 38(6), 252-259.
31. Wang, Y.-H., Tseng, M.-H. & Liao, H.-C. (2009). Data mining for adaptive learning sequence in English language instruction. Expert Systems with Applications, 36(4), 7681-7686. http://dx.doi.org/10.1016/j.eswa.2008.09.008
32. Yang, F., Sun, T. & Zhang, C. (2009). An efficient hybrid data clustering method based on Kharmonic means and particle swarm optimization. Expert Systems with Applications, 36(6), 9847-9852. http://dx.doi.org/10.1016/j.eswa.2009.02.003