Identifying students’ characteristic learning behaviors in an intelligent tutoring system fostering self-regulated learning

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

Volumn , Issue , 2012, Pages

  Bouchet, François ^a   Azevedo, Roger ^a   Kinnebrew, John S ^b   Biswas, Gautam ^b  

^a MCGILL UNIVERSITY   (Canada)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTER AIDED INSTRUCTION; DATA MINING; EDUCATION COMPUTING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LEARNING SYSTEMS; MAXIMUM PRINCIPLE;

ACTIVITY PATTERNS; EXPECTATION - MAXIMIZATIONS; FEEDBACK MECHANISMS; INTELLIGENT TUTORING SYSTEM; LEARNING BEHAVIOR; PRELIMINARY ANALYSIS; REAL-TIME ADAPTATION; SELF-REGULATED LEARNING;

STUDENTS;

EID: 85084015233     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the Eleventh IEEE International Conference on Data Engineering (ICDE), pages 3–14, 1995.
2. R. Azevedo, R. Behnagh, M. Duffy, J. M. Harley, and G. J. Trevors. Metacognition and self-regulated learning in student-centered leaning environments. In D. Jonassen and S. Land, editors, Theoretical foundations of student-center learning environments, pages 216–260. Erlbaum, Mahwah, NJ, 2nd edition, 2012.
3. R. Azevedo, J. G. Cromley, D. C. Moos, J. A. Greene, and F. I. Winters. Adaptive content and process scaffolding: A key to facilitating students’ self-regulated learning with hypermedia. Psychological Testing and Assessment Modeling, 53:106–140, 2011.
4. R. Azevedo and R. Feyzi-Behnagh. Dysregulated learning with advanced learning technologies. Journal of e-Learning and Knowledge Society, 7(2):9–18, 2011.
5. R. Azevedo, A. Johnson, A. Chauncey, and A. C. Graesser. Use of hypermedia to convey and assess self-regulated learning. In B. Zimmerman and D. Schunk, editors, Handbook of self-regulation of learning and performance, pages 102–121. Routledge, New York, 2011.
6. R. Azevedo, D. C. Moos, A. Johnson, and A. Chauncey. Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4):210–223, 2010.
7. R. Azevedo and A. M. Witherspoon. Self-regulated learning with hypermedia. In D. J. Hacker, J. Dunlosky, and A. C. Graesser, editors, Handbook of metacognition in education, pages 319–339. Routledge, Mahwah, NJ, 2009.
8. F. Bouchet, J. M. Harley, G. J. Trevors, and R. Azevedo. Clustering and profiling students according to their interactions with an intelligent tutoring system fostering Self-Regulated learning. Journal of Educational Data Mining, (submitted), 2012.
9. S. D’Mello, B. Lehman, J. Sullins, R. Daigle, R. Combs, K. Vogt, L. Perkins, and A. Graesser. A time for emoting: When Affect-Sensitivity is and isn’t effective at promoting deep learning. In V. Aleven, J. Kay, and J. Mostow, editors, Intelligent Tutoring Systems, volume 6094, pages 245–254. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010.
10. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10–18, Nov. 2009.
11. J. S. Kinnebrew, K. M. Loretz, and G. Biswas. A differential sequence mining method contextualized by student performance evolution to derive learning behavior patterns. Journal of Educational Data Mining, (submitted), 2012.
12. 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.
13. 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.
14. J. C. Nesbit, Z. Mingming, Y. Xu, and P. H. Winne. Advancing log analysis of student interactions with cognitive tools. In 12th Biennial Conference of the European Association for Research on Learning and Insruction (EARLI), Budapest, Hungary, 2007.
15. D. Perera, J. Kay, I. Koprinska, K. Yacef, and O. R. Zaane. Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. on Knowl. and Data Eng., 21(6):759–772, June 2009.
16. C. Romero, S. Ventura, A. Zafra, and P. d. Bra. Applying web usage mining for personalizing hyperlinks in web-based adaptive educational systems. Computers & Education, 53(3):828–840, Nov. 2009.
17. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In P. Apers, M. Bouzeghoub, and G. Gardarin, editors, Advances in Database Technology - EDBT’96, volume 1057, pages 1–17. Springer-Verlag, Berlin/Heidelberg, 1996.
18. T. Y. Tang and G. McCalla. Student modeling for a web-based learning environment: a data mining approach. In Proc. of the 18th national conference on Artificial intelligence, page 967968, Menlo Park, CA, USA, 2002. American Association for Artificial Intelligence.
19. P. H. Winne. Self-regulated learning viewed from models of information processing. In B. Zimmerman and D. Schunk, editors, Self-regulated learning and academic achievement: Theoretical perspectives, pages 153–189. Erlbaum, Mahwah, NJ, 2001.
20. P. H. Winne and A. Hadwin. Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, and A. C. Graesser, editors, Metacognition in educational theory and practice, pages 277–304. Erlbaum, Hillsdale, NJ, 1998.
21. P. H. Winne and A. Hadwin. The weave of motivation and self-regulated learning. In D. Schunk and B. Zimmerman, editors, Motivation and self-regulated learning: Theory, research and applications, pages 297–314. Routledge, 2008.
22. B. Zimmerman and D. Schunk, editors. Handbook of self-regulation of learning and performance. Routledge, New York, 2011.