Co-clustering by bipartite spectral graph partitioning for out-of-tutor prediction

Proceedings of the 5th International Conference on Educational Data Mining, EDM 2012

Volumn , Issue , 2012, Pages

  Trivedi, Shubhendu ^a   Pardos, Zachary A ^a   Sárközy, Gábor N ^a   Heffernan, Neil T ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

Bootstrap aggregation; Dynamic assessment; Ensemble learning; Out of tutor prediction; Spectral co clustering

Indexed keywords

DATA MINING; FORECASTING; GRAPH THEORY; STATISTICAL METHODS;

DIMENSIONALITY REDUCTION; DISTRIBUTED REPRESENTATION; DYNAMIC ASSESSMENT; ENSEMBLE LEARNING; OBJECTIVE FUNCTIONS; ONE-WAY CLUSTERING; SPECTRAL CO; SPECTRAL GRAPH PARTITIONING;

STUDENTS;

EID: 85084016136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Felder, R.M., and Brent R.,Understanding Student Differences, Journal of Engineering Education, Vol. 94, No. 1, 2005, pp. 57-72.
2. Bransford, J.D., Brown, A.L., and Cocking, R., eds., How People Learn: Brain, Mind, Experience, and School, Washington, D.C.: National Academy Press, 2000.
3. Pardos, Z. A., Trivedi, S., Heffernan. N. T., and Sárközy. G. N., Clustered Knowledge Tracing, In the Proceedings of the 11th International Conference on Intelligent Tutoring Systems 2012, Chania, Greece.
4. Trivedi, S., Pardos, Z. A., Heffernan, N. T., Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions, G. Biswas et al. (Eds.): AIED 2011, LNAI 6738, In The proceedings of the 15th International Conference on Artificial Intelligence in Education 2011, Auckland, New Zealand, pp. 377-384.
5. Trivedi, S., Pardos, Z. A., Sárközy, G. N., Heffernan, N. T., Spectral Clustering in Educational Data Mining, In Proceedings of the 4th International Conference on Educational Data Mining 2011, Eindhoven Netherlands, pp. 129-138.
6. Trivedi, S., Pardos, Z. A., and Heffernan, N. T., The Utility of Clustering in Prediction Tasks, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics (under review).
7. Hartigan, J. A., Wong, M. A., Algorithm AS 136: A K-Means Clustering Algorithm, Journal of the Royal Statistical Society, Series C (Applied Statistics), 1979, 28 (1): pp. 100-108.
8. Hartigan, J. A., Direct Clustering of a Data Matrix, Journal of the American Statistical Association, 67(337): pp. 123-129, 1972.
9. Dhillon, I. S., S, Mallela., and Kumar, R., A divisive Information-Theoretic Feature Clustering Algorithm for text classication. Journal of Machine Learning Research, 3(4): pp. 1265-1287, 2003.
10. Dhillon, I. S., Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, pp. 269-274, 2001.
11. Hanisch, D., Zein, A., Zimmer, R., Lengauer, T., Co-clustering of biological networks and gene expression data. Bioinformatics 18 (Suppl.), 2002, pp. 145-154.
12. D’Haeseleer, P., Liang, S., and Somoyogi, R., Genetic Network Inference: From co-expression clustering to reverse engineering, Bioinformatics, 16, 2000, pp. 707-726.
13. George, T., and Merugu, S., A scalable collaborative ltering framework based on co-clustering. In Proceedings of the IEEE Conference on Data Mining, pp. 625-628, 2005.
14. Dhillon, I., Mallela, S., and Modha, D., Information-theoretic co-clustering. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (KDD), pp. 89-98, 2003.
15. Banerjee, A., Dhillon, I. S., Ghosh, J., Merugu, S., and Modha, D. S, A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation, Journal of Machine Learning Research, 8, pp. 1919-1198, 2007.
16. J. Shi, and J. Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), pp. 888-905, 2000.
17. Bengio, Y., Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1): pp. 1-127, Now Publishers, 2009.
18. Dietterich, T.G., Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) First International workshop on Multiple Classifier Systems. LNCS, pp. 1-15. Springer, New York, 2000.
19. Dietterich, T.G., An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization, Machine Learning 40, pp. 139-157, 2000.
20. Breiman, L., Random Forests, Machine Learning 45(1), pp. 5-32, 2001.
21. Grigerenko, E.L., Steinberg, R.J.: Dynamic Testing. Psychological Bulletin 124, pp. 75-111, 1998.
22. Campione, J. C., Brown, A. L.: Dynamic Assessment: One Approach and some Initial Data. Technical Report. No. 361. Cambridge, MA. Illinois University, Urbana, Center for the Study of Reading. ED 269735, 1985.
23. Feng, M., Heffernan, N.T., Koedinger, K.R.: Addressing the assessment challenge in an online system that tutors as it assesses. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 19(3), 2009.
24. Mohar, B., The Laplacian spectrum of graphs. In Graph theory, combinatorics, and applications. Vol. 2 (Kalamazoo, MI, 1988), New York: Wiley, 1991, pp. 871-898.