Identifying learning behaviors by contextualizing differential sequence mining with action features and performance evolution

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

Volumn , Issue , 2012, Pages

  Kinnebrew, John S ^a   Biswas, Gautam ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; ITERATIVE METHODS; LEARNING SYSTEMS; LINEAR TRANSFORMATIONS; MATHEMATICAL TRANSFORMATIONS; PIECEWISE LINEAR TECHNIQUES; STUDENTS;

EXPLORATORY DATA MINING; ITERATIVE METHODOLOGY; LEARNING ACTIVITY; LEARNING BEHAVIOR; LEARNING BY TEACHING; LEARNING INTERACTIONS; MULTIPLE LEVELS OF DETAIL; PERFORMANCE EVOLUTIONS;

DATA MINING;

EID: 85084015190     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of The ACM SIGMOD International Conference on Management of Data, volume 22, pages 207–216. ACM, 1993.
2. R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the Eleventh IEEE International Conference on Data Engineering (ICDE), pages 3–14, 1995.
3. G. Biswas, H. Jeong, J. Kinnebrew, B. Sulcer, and R. Roscoe. Measuring Self-regulated Learning Skills through Social Interactions in a Teachable Agent Environment. Research and Practice in Technology-Enhanced Learning (RPTEL), 5(2):123–152, 2010.
4. G. Biswas, K. Leelawong, D. Schwartz, N. Vye, and T. Vanderbilt. Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence, 19(3):363–392, 2005.
5. J. Bransford, A. Brown, and R. Cocking, editors. How people learn. National Academy Press Washington, DC, Washington, D.C., 2000.
6. J. Ho, L. Lukov, and S. Chawla. Sequential pattern mining with constraints on large protein databases. In Proceedings of the 12th International Conference on Management of Data (COMAD), pages 89–100, 2005.
7. E. Keogh, S. Chu, D. Hart, and M. Pazzani. Segmenting time series: A survey and novel approach. In Data mining in time series databases, volume 57, pages 1–22. World Scientific, 2004.
8. J. S. Kinnebrew, K. M. Loretz, and G. Biswas. A contextualized, differential sequence mining method to derive students’ learning behavior patterns. Journal of Educational Data Mining, In Press, 2012.
9. K. Leelawong and G. Biswas. Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3):181–208, 2008.
10. D. Lo, S. Khoo, and C. Liu. Efficient mining of recurrent rules from a sequence database. In Proceedings of the 13th International Conference on Database Systems for Advanced Applications, pages 67–83. Springer-Verlag, 2008.
11. H. Mannila, H. Toivonen, and A. Inkeri Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259–289, 1997.
12. R. Martinez, K. Yacef, J. Kay, A. Al-Qaraghuli, and A. Kharrufa. Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. In Proceedings of the Fourth International Conference on Educational Data Mining, Eindhoven, Netherlands, 2011.
13. J. Nesbit, M. Zhou, Y. Xu, and P. Winne. Advancing log analysis of student interactions with cognitive tools. 12th Biennial Conference of the European Association for Research on Learning and Insruction (EARLI), 2007.
14. D. Perera, J. Kay, I. Koprinska, K. Yacef, and O. Zaïane. Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6):759–772, 2009.
15. J. R. Segedy, J. S. Kinnebrew, and G. Biswas. Supporting cognitive and metacognitive skills in complex, open-ended learning environments. Journal of Educational Psychology, In Review, 2012.
16. R. Srikant and R. Agrawal. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology (EDBT): Advances in Database Technology, pages 3–17. Springer-Verlag, 1996.
17. J.-M. Su, S.-S. Tseng, W. Wang, J.-F. Weng, J. Yang, and W.-N. Tsai. Learning portfolio analysis and mining for scorm compliant environment. Journal of Educational Technology and Society, 9(1):262–275, 2006.
18. B. Zimmerman. Theories of self-regulated learning and academic achievement: An overview and analysis. In B. Zimmerman and D. Schunk, editors, Self-regulated learning and academic achievement: Theoretical perspectives, pages 1–37. Erlbaum, Mahwah, NJ, 2001.