Unsupervised and supervised machine learning in user modeling for intelligent learning environments

International Conference on Intelligent User Interfaces, Proceedings IUI

Volumn , Issue , 2007, Pages 72-81

  Amershi, Saleema ^a   Conati, Cristina ^a,b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

Eyetracking; Intelligent learning environments; Unsupervised and supervised machine learning; User modeling

Indexed keywords

DATA ACQUISITION; INTELLIGENT SYSTEMS; MATHEMATICAL MODELS; USER INTERFACES;

ADAPTIVE COACH FOR EXPLORATION (ACE); EYE TRACKING DATA; INTELLIGENT LEARNING ENVIRONMENTS; USER MODELING;

LEARNING SYSTEMS;

EID: 34648823337     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1216295.1216315     Document Type: Conference Paper

References (22)

1. Amershi, S. and Conati, C. Automatic Recognition of Learner Groups in ELEs. In ITS (Jhongli, Taiwan), 2006, pp. 463-472.
2. Baker, R.S., Corbett, A.T., and Koedinger, K.R. Detecting Student Misuse of ITSs. In ITS (MaceioAlagoas, Brasil), 2004, pp. 531-540.
3. Beck, J., Stern, M., and Haugsjaa, E., Applications of AI in Education. ACM Crossroads 3, 1 (1996), 11-16.
4. Bunt, A. and Conati, C., Probabilistic Student Modeling to Improve Exploratory Behavior. UMUAI 13, 3 (2003), 269-309.
5. Burton, R.R. DEBUGGY: Diagnosis of Errors in Basic Mathematical Skills. In ITS, 1982, pp. 157-183.
6. Cohen, J., Statistical Power Analysis for the Behavioral Sciences. 2 ed. Lawrence Earlbaum Associates, Hillsdale, 1988.
7. Conati, C and Merten, C., Gaze-Tracking for User Modeling in Intelligent Learning Environments: an Empirical Evaluation. To Appear in Knowledge Based Systems, Special Issue on Techniques and Advances in Intelligent User Interfaces.
8. Dash, M. and Liu, H. Feature Selection for Clustering. In PACKDDM (Kyoto, Japan), 2000, pp. 110-121.
9. de Vicente, A. and Pain, H. Informing the Detection of the Students' Motivational State: An Empirical Study. In ITS (Biarritz, San Sebastian), 2002, pp. 933-943.
10. Duda, R.O., Hart, P.E., and Stork, D.G., Pattern Classification. 2 ed. Wiley-Interscience, NY, 2001.
11. Gorniak, P.J. and Poole, D. Building a Stochastic Dynamic Model of Application Use. In UAI (Stanford, CA), 2000, pp. 230-237.
12. Hunt, E. and Madhyastha, T. Data Mining Patterns of Thought. In AAAI Workshop on Educational Data Mining (Menlo Park, California), 2005, pp. 31-39.
13. Jain, A.K., Duin, R.P.W., and Mao, J., Statistical Pattern Recognition: A Review. IEEE Pattern Analysis and Machine Intelligence 22, 1 (2000), 4-37.
14. Kushmerick, N. and Lau, T. Automated Email Activity Management: An Unsupervised Learning Approach. In IUI (San Diego, California), 2005, pp. 67-74.
15. Lange, T., Braun, M., Roth, V., Buhmann, J., Stability-Based Model Selection. In NIPS (Whistler, BC), 2003, pp. 617-624.
16. Paliouras, G., Papatheodorou, C., Karkaletsis, V., Spyropoulos, C., Discovering User Communities on the Internet Using Unsupervised ML Techniques. Interacting with Computers 14, 6 (2002), 761-791.
17. Perkowitz, M. and Etzioni, O., Towards Adaptive Web Sites: Conceptual Framework and Case Study. AI 118, 1-2 (2000), 245-275.
18. Shute, V.J., A Comparison of Learning Environments: All That Glitters... in Computers as Cognitive Tools, S.P. Lajoie and S.J. Deny, Editors., Lawrence Erlbaum Associates: Hillsdale, NJ, 1993.
19. Sison, R. and Shimura, M., Student Modeling and Machine Learning. AIED 9, (1998), 128-158.
20. Sison, R., Numao, M., and Shimura, M., Multistrategy Discovery and Detection of Novice Programmer Errors. ML 38, (2000), 157-180.
21. Talavera, L. and Gaudioso, E. Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces. In Workshop on AI in CSCL (Valencia, Spain), 2004, pp. 17-23.
22. Weiss, G.M. and Provost, F., The Effect of Class Distribution on Classifier Learning: An Empirical Study. Rutgers Univ., 2001.