Learning and diagnosis of individual and class conceptual perspectives: An intelligent systems approach using clustering techniques

Computers and Education

Volumn 44, Issue 3, 2005, Pages 257-283

  Cheng, Shein Yung ^a   Lin, Chia Sheng ^a   Chen, Hsien Hsun ^a   Heh, Jia Sheng ^a  

^a CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Community; clustering; Concept map; Learning diagnosis; Misconception

Indexed keywords

ALGORITHMS; DATA REDUCTION; ERRORS; PROBLEM SOLVING; STUDENTS; TEACHING;

CLUSTERING; COMMUNITY; CONCEPT MAP; LEARNING DIAGNOSIS; MISCONCEPTION;

LEARNING SYSTEMS;

EID: 9644278042     PISSN: 03601315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compedu.2004.02.005     Document Type: Article

References (43)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large database. Proceedings of the 20th international conference on very large data bases, 487-499.
2. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In P. S. Yu & A. L. P. Chen (Eds.), Proceedings of the 11th international conference on data engineering (pp. 3-14). IEEE Computer Society Press.
3. Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational psychology: A cognitive view (2nd ed.). New York: Holt, Rinehart and Winston.
4. Baffes, P., & Mooney, R. (1996). Refinement-based student modeling and automated bug library construction. Journal of Artificial Intelligence in Education, 7(1), 75-116.
5. Burton, R. (1982). Diagnosing bugs in a simple procedural skill. In D. Sleeman & L. Brown (Eds.), Intelligent tutoring systems. London: Academic Press.
6. Chang, J. C., Hsu, C. K., Lin, Y. Y., Heh, J. S. Concept-based learning diagnosis and the application to distance testing. Journal of Computer Assisted Learning.
7. Clifford, M. (1981). Ed Syke: The frame game. Supplement to practicing educational psychology. New York: Houghton Mifflin.
8. Dansereau, D. (1978). The development of a learning strategies curriculum. In H. O'Neil (Ed.), Learning strategies (pp. 1-29). New York: Academic Press.
9. Dong, Da-Xian, (2002). Apply knowledge Map to develop physics Problem-Solving System. Unpublished master's thesis, Chung Yuan Christian University, Chung-Li, Taiwan, ROC.
10. Dunham, Margaret H. (2003). Data Mining Introductory and Advanced Topics. Prentice Hall. IBM. Surfaid analytics. (2000), www.surfaid/dfw.ibm.com/web/ index.html.
11. Dunham, Margaret H. (2003). Data Mining Introductory and Advanced Topics. Prentice Hall. IBM. Surfaid analytics. (2000), www.surfaid/dfw.ibm.com/web/ index.html.
12. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, New Jersey: Lawrence Erlbaum Accociates.
13. Gagné, E. D., Yekovich, C. W., & Yekovich, F. R. (1993). The cognitive psychology of school learning. New York: HarperCollins College Publishers.
14. Grimaldi, R. P. (1994). Discrete and combinatorial mathematics: an applied introduction (pp. 51-100) (3rd ed.). Addison-Wesley Publishing Company, Inc.
15. Hoppe, U. (1994). Deductive error diagnosis and inductive error generalization for intelligent tutoring systems. Journal of Artificial Intelligence in Education, 5(1), 27-49.
16. Han, J., Fu, Y., (1994). Dynamic generation and refinement of concept hierarchies for knowledge discovery in database. In Proceedings of the AAAI'94 workshop on knowledge discovery in databases (KDD'94), pp. 157-168.
17. Holley, C. D., Dansereau, D. F., McDonald, B. A., Garland, J. C., & Collins, K. W. (1979). Evaluation of a hierarchical mapping technique as an aid to prose processing. Contemporary Educational Psychology, 4, 227-237.
18. Hsu, C. K., Chang, J. C., Chang, M., Jehng, J. C., Heh, J. S. (2002). An approach for automatic learning and inference by knowledge map. In International conference on computer education. IBM. Surfaid analytics. Available: www.surfaid/dfw.ibm.com/web/index.html, 2000.
19. Jonassen, D. H., Reeves, T. C., Hong, N., Harvey, D., & Peters, K. (1997). Concept mapping as cognitive learning and assessment tools. Journal of Interactive Learning Research, 8(314), 289-308.
20. Jones, B. F., Palincsar, A. S., Ogle, D. S., & Carr, E. G. (1987). Strategic teaching and learning: cognitive instruction in the content areas. Alexandra, VA: Association for Supervision and Curriculum Development.
21. Keegan, D. (1996). Foundations of distance education. London: Routledge.
22. Kono, Y., Ikeda, M., & Mizoguchi, R. (1994). THEMIS: A nonmonotonic inductive student modeling system. Journal of Artificial Intelligence in Education, 5(3), 371-413.
23. Kuo, R., Chang, M., Dong, D. X., Yang, K. Y., Heh, J. S. (2002). Applying knowledge map to intelligent agents in problem solving systems. In World conference on educational multimedia, hypermedia and telecommunications.
24. Langley, P., Ohlson, S. (1984). Automated cognitive modeling. In Proceedings of the national conference on artificial intelligence, (pp. 193-197). Austin, Texas.
25. Mannila, H., Toivonen, H., & Verkamo, A. I. (1995). Discovering frequent episodes in sequences. Proceeding of the KDD conference, 210-215.
26. Marshall, S. P. (1995). Schemas in problem solving. Cambridge University Press.
27. Mayer, R. E. (1991). Educational psychology. Addison-Wesley Publishing Company, Inc.
28. McCreight, E. M. (1976). A space economical suffix tree construction algorithm. Journal of the ACM, 23(2), 262-297.
29. Moore, M. G. (1993). Is teaching like flying. A total systems view of distance education. American Journal of Distance Education, 7(1), 1-10.
30. Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge London: Cambridge University Press.
31. Peters, O. (1983). Distance teaching and industrial production: A comparative interpretation in outline. In D. Sewart, D. Keegan, & B. Holmberg (Eds.), Distance education: International perspectives (pp. 95-113). London: Croom-Helm.
32. Preece, P. F. W. (1976). Mapping cognitive Structure: A comparison of methods. Journal of Educational Psychology, 68, 1-8.
33. Quillian, M. R., (1966). Semantic memory. Unpublished Ph. D. dissertation, Carnegie Institute of Technology: Pittsburg.
34. Robert, L. L., & Norman, E. G. (1995). Measurement and assessment in teaching (pp. 115-198) (7th ed.). Prentice-Hall, Inc.
35. Sison, R., Numao, M., Shimura, M. (1998). Detecting errors in novice programs via unsupervised multistrategy learning. In Proceedings of the Fourth International Workshop on Multistrategy Learning, pp. 135-140.
36. Sison, R., & Shimura, M. (1998). Student modeling and machine learning. International Journal of Artificial Intelligence in Education, 9(1-2), 128-158.
37. Sleeman, D. H., & Brown, J. S. (1982). Introduction: Intelligent tutoring systems. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 1-11). London: Academic Press.
38. Sleeman, D., Hirsh, H., Ellery, I., & Kim, I. (1990). Extending domain theories: Two case studies in student modeling. Machine Learning, 5, 11-37.
39. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. (2000). Web usage mining: Discovery and applications of usage patterns form web data. SIGKDD Explorations, 12-23.
40. Wedemeyer, C. A. (1977). Independent study. In A. S. Knowles (Ed.), The international encyclopedia of higher education. Boston: Northeastern University.
41. Wenger, E. (1987). Artificial intelligence and tutoring systems. Los Altos, CA: Morgan Kaufmann.
42. Xiao, Y., & Dunham, M. H. (2001). Efficient mining of traversal patterns. Data and Knowledge Engineering., 39(2), 191-214.
43. Zaïane, O. R. (1999). Resource and knowledge discovery from the internet and multimedia repositories. Technical report, PhD Dissertation, Simon Fraser University, March 1999.