A framework for capturing distinguishing user interaction behaviours in novel interfaces

EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining

Volumn , Issue , 2011, Pages 159-168

  Kardan, S ^a   Conati, C ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Associative Rule Mining; Clustering; Student Modeling

Indexed keywords

ADAPTIVE INTERACTION; ASSOCIATIVE RULE; CLUSTERING; EDUCATIONAL SOFTWARE; LONG-TERM GOALS; MODELING PROCESS; PROOF OF CONCEPT; STUDENT MODELING; UNSUPERVISED CLUSTERING; USER INTERACTION; USER MODELING;

DATA MINING; MATHEMATICAL MODELS;

LEARNING SYSTEMS;

EID: 84857492059     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. AMERSHI, S. AND CONATI, C. 2009. Combining Unsupervised and Supervised Machine Learning to Build User Models for Exploratory Learning Environments. J. of Educational Data Mining 1, 1, 2.
2. AMERSHI, S., CARENINI, G., CONATI, C., MACKWORTH, A. AND POOLE, D. 2008. Pedagogy and Usability in Interactive Algorithm Visualizations - Designing and Evaluating CIspace. Interact Comput. 20, 1, 64-96.
3. BERNARDINI, A., AND CONATI, C. 2010. Discovering and Recognizing Student Interaction Patterns in Exploratory Learning Environments. Proc. ITS2010, Springer, 125-134.
4. CHANGCHIEN, S.W., AND LU, T. 2001. Mining association rules procedure to support on-line recommendation by customers and products fragmentation. Expert Systems with Applications 20, 325-335.
5. DUDA, R. O., HART, P. E., AND STORK, D., G. 2001. Pattern Classification. New York: Wiley- Interscience.
6. FREYBERGER, J., HEFFERNAN, N., AND RUIZ, C. 2004. Using association rules to guide a search for best fitting transfer models of student learning. Workshop on analyzing student-tutor interactions logs to improve educational outcomes at ITS conference, 1-4.
7. GARCÍA, E., ROMERO, C., VENTURA, S., AND CASTRO, C. D. 2009. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. UMUAI 19, 99-132.
8. GENG, L., AND HAMILTON, H. J. 2006. Interestingness measures for data mining: A survey. ACM Comput. Surv. 38, 3, 9.
9. HALL, M., EIBE, F., HOLMES, G., PFAHRINGER, B., REUTEMANN, P., WITTEN, I.H. 2009. The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10-18.
10. HUNT, E., AND MADHYASTHA, T. 2005. Data Mining Patterns of Thought. In Proc. the AAAI Workshop on Educational Data Mining.
11. KIM, K. AND AHN, H. 2008. A recommender system using GA k-means clustering in an online shopping market. Expert Syst. Appl. 34, 2, 1200-1209.
12. KÖCK, M., PARAMYTHIS, A. 2011. Activity sequence modelling and dynamic clustering for personalized elearning. User Model User-Adap Inter 21:51-97.
13. LIU, B., HSU, W., AND MA, Y. 1999. Mining association rules with multiple minimum supports. In Proc. KDD '99. ACM, 337-341.
14. MERCERON A., AND YACEF K. 2003. A web-based tutoring tool with mining facilities to Improve Teaching and Learning, AIED 2003, 201-208.
15. MILLIGAN G. W., AND COOPER, M. C. 1985. An examination of procedures for determining the number of clusters in a data set, Psychometrika, 50, 2, 159-179.
16. ROMERO, C., VENTURA, S., ZAFRA, A., AND DE BRA, P. 2009. Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Comput. Educ. 53, 3, 828-840.
17. SHANABROOK, D. H., COOPER, D. G., WOOLF, B. P., ARROYO, I. 2010. Identifying High-Level Student Behaviour Using Sequence-based Motif Discovery. In Proceedings of EDM'2010.191-200.
18. THABTAH, F. 2007. A review of associative classification mining. Knowl. Eng. Rev. 22, 1, 37-65.
19. YIN, X., AND HAN, J. 2003. CPAR: Classification based on predictive association rules. In Proc. of the Third SIAM International Conference on Data Mining, SIAM, 208-217.
20. ZHANGA, Y., AND JIAO, J. 2007. An associative classification-based recommendation system for personalization in B2C e-commerce applications, Expert Syst. Appl. 33, 2, 357-367.