Using cluster analysis for data mining in educational technology research

Educational Technology Research and Development

Volumn 60, Issue 3, 2012, Pages 383-398

  Antonenko, Pavlo D ^a   Toy, Serkan ^b   Niederhauser, Dale S ^c  

^a OKLAHOMA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF MISSOURI KANSAS CITY   (United States)

^c IOWA STATE UNIVERSITY   (United States)

Author keywords

Cluster analysis; Data mining; Learner profiles; Online learning environments; Server logs

Indexed keywords

EID: 84861197190     PISSN: 10421629     EISSN: 15566501     Source Type: Journal    
DOI: 10.1007/s11423-012-9235-8     Document Type: Article

References (34)

1. Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills: Sage Press.
2. Barab, S. A., Bowdish, B. E., & Lawless, K. A. (1997). Hypermedia navigation: profiles of hypermedia users. Educational Technology Research and Development, 45(3), 23-42.
3. Belland, B. R., French, B., & Ertmer, P. A. (2009). Validity and problem-based learning research: a review of instruments used to assess intended learning outcomes. Interdisciplinary Journal of Problem-based Learning, 3(1), 59-89.
4. Belland, B. R., Glazewski, K. D., & Richardson, J. C. (2010). Problem-based learning and argumentation: testing a scaffolding framework to support middle school students' creation of evidence-based arguments. Instructional Science,. doi: 10. 1007/s11251-010-9148-z.
5. Clark, R. E. (2010). Cognitive and neuroscience research on learning and instruction: recent insights about the impact of non-conscious knowledge on problem solving, higher order thinking skills and interactive cyber-learning environments. Presented at the International Conference on Education Research (ICER), Seoul.
6. Cronbach, L. J., & Gleser, G. C. (1953). Assessing similarity between profiles. Psychological Bulletin, 50, 456-473.
7. Donner, A., & Koval, J. J. (1980). The estimation of intraclass correlation in the analysis of family data. Biometrics, 36(1), 19-25.
8. Everitt, B. S., Landau, S., & Leese, M. (2009). Cluster analysis (4th ed.) London: Arnold.
9. Facione, P. A., & Facione, N. C. (1994). Holistic critical thinking scoring rubric. Millbrae: California Academic Press.
10. Feldon, D. F. (2007). Implications of research on expertise for curriculum and pedagogy. Educational Psychology Review, 19(2), 91-110.
11. Fielding, A. H. (2007). Cluster and classification techniques for the biosciences. Cambridge: Cambridge University Press.
12. Gijbels, D., Dochy, F., van den Bossche, P., & Segers, M. (2005). Effects of problem-based learning: a meta-analysis from the angle of assessment. Review of Educational Research, 75(1), 27-61.
13. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a survey. ACM Computing Surveys, 31, 264-323.
14. Jeong, H., & Hmelo-Silver, C. E. (2010). Productive use of learning resources in an online problem-based learning environment. Computers in Human Behavior, 26, 84-99.
15. Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65-94.
16. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63-85.
17. Kim, M. C., & Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): bridging research and theory with practice. Computers & Education, 56, 403-417.
18. Kumsaikaew, P., Jackman, J., & Dark, V. J. (2006). Task relevant information in engineering problem solving. Journal of Engineering Education, 95, 227-239.
19. Lawless, K., & Kulikowich, J. (1996). Understanding hypertext navigation through cluster analysis. Journal of Educational Computing Research, 14(4), 385-399.
20. Leighton, J. P. (2004). Avoiding misconception, misuse, and missed opportunities: the collection of verbal reports in educational achievement testing. Educational Measurement: Issues and Practice, 23, 6-15.
21. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.) Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281-297). Berkley: University of California Press.
22. Milligan, G. W. (1980). A review of Monte Carlo tests of cluster analysis. Multivariate Behavioral Research, 16, 379-407.
23. Milligan, G. W., & Cooper, M. C. (1987). Methodology review: clustering methods. Applied Psychological Measurement, 11, 329-354.
24. Ng, R. T., & Han, J. (1994). Efficient and effective clustering methods for spatial data mining. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.) Proceedings of the Twentieth International Conference on Very Large Databases (pp. 144-155). Santiago: Morgan Kaufmann.
25. Niederhauser, D. S., Antonenko, P., Ryan, S., Jackman, J., Ogilvie, C., Marathe, R., & Kumsaikaew, P. (2007). Solution strategies of more and less successful problem solvers in an online problem-based learning environment. Presented at the annual conference of the American Educational Research Association. Chicago, IL.
26. Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. London: Academic Press.
27. Norušis, M. (2005). SPSS 13. 0 statistical procedures companion. Englewood Cliffs: Prentice Hall.
28. Ryan, S., Jackman, J., Kumsaikaew, P., Dark, V., & Olafsson, S. (2007). Use of information in collaborative problem solving. In D. H. Jonassen (Ed.), Learning to solve complex, scientific problems (pp. 187-204). Mahwah, NJ: Lawrence Erlbaum Associates.
29. Schrader, P. G., & Lawless, K. A. (2007). Dribble files: methodologies to evaluate learning and performance in complex environments. Performance Improvement, 46(1), 40-48.
30. Sokal, R. R., & Michener, C. D. (1958). A statistical method for evaluating systematic relationships. The University of Kansas Scientific Bulletin, 38, 1409-1438.
31. Stevens, R. H. (2007). Quantifying student's scientific problem solving efficiency and effectiveness. Cognition and Learning, 5, 325-337.
32. Toy, S. (2008). Online ill-structured problem-solving strategies and their influence on problem-solving performance. Unpublished doctoral dissertation, Iowa State University, Ames, IA.
33. Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of American Statistical Association, 58(301), 236-244.
34. Webb, A. (2002). Statistical pattern recognition. Hoboken: John Wiley.