Data-driven modelling of students' interactions in an ILE

Educational Data Mining 2008 - 1st International Conference on Educational Data Mining, Proceedings

Volumn , Issue , 2008, Pages 87-96

  Mavrikis, Manolis ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA COLLECTION; DATA-DRIVEN; LOGISTIC REGRESSIONS; SATISFACTORY PREDICTIONS;

DATA MINING; DECISION TREES; LOGISTICS;

STUDENTS;

EID: 80052265500     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Aleven, V. and Koedinger, K. (2000). Limitations of student control: Do students know when they need help? In ITS, volume 1839 of LNCS, pages 292-303.
2. J. R. Anderson, A. T. Corbett, K. R. Koedinger, and R. Pelletier. Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2):167-207, 1995.
3. R.S. Baker, A.T. Corbett, K.R. Koedinger, and A.Z. Wagner. Off-task behavior in the cognitive tutor classroom: When students "game the system". In Proceedings of ACM CHI 2004 Conference on Human Factors in Computing Systems, 24-29 April 2004, Vienna, Austria, pages 383-390. ACM Press, New York, 2004.
4. R.R. Bouckaert. Bayesian networks in Weka. Technical report, Computer Science Department University of Waikato, 2004.
5. C. Conati, A. Gertner, K. VanLehn, and M. Druzdzel. On-line student modeling for coached problem solving using bayesian networks. In A. Jameson, C. Paris, and C. Tasso, eds, User Modeling: Proceedings of the 6th International conference, 1997.
6. A.T. Corbett and J.R Anderson. Student modeling and mastery learning in a computer-based programming tutor. ITS, volume 608 of LNCS, pages 413-420, 1992.
7. M R. Lepper, M.Woolverton, D L. Mumme, and J. Gurtner. Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S.P. Lajoie and S.J. Derry, eds, Computers as Cognitive Tools, pages 75-107. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1993.
8. J. Lin, E. Keogh, S Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. Data Mining and Knowledge Discovery, 2003.
9. J. Martin and K. VanLehn. Student assessment using bayesian nets. International Journal of Human-Computer Studies, 42:575-591, 1995.
10. M. Mavrikis. Logging, replaying and analysing students' interactions in a web-based ILE to improve student modelling. In Proceedings of the 12th International Conference on Artificial Intelligence in Education, page 967, Amsterdam, 2005.
11. M. Mavrikis. Modelling Students' Behaviour and Affective States in ILEs through Educational Data Mining. PhD thesis, The University of Edinburgh, 2008.
12. M.Mavrikis and A.Maciocia. WaLLiS: a web-based ILE for science and engineering students studying mathematics. Supplemental Proceedings of the 11th International Conference on Artificial Intelligence in Education, AIED2003, Vol. VIII, Workshop on Advanced Technologies for Mathematics Education, pages 505-513, 2003.
13. M.Mavrikis, A.Maciocia, and J. Lee. Towards predictive modelling of student affect from web-based interactions. In Proceedings of the 13th International Conference on Artificial Intelligence in Education, pages 169-176, Los Angeles, California, 2007.
14. R.C. Murray and K. VanLehn. Effects of dissuading unnecessary help requests while providing proactive help. In Proceedings of the 12th International Conference on Artificial Intelligence In Education, pages 887-889., Amsterdam, 2005.
15. R.S. Newman. Students' help seeking during problem solving: Influences of personal and contextual achievement goals. Journal of Educational Psychology, 90:644-658, 1998.
16. K. Porayska-Pomsta, M. Mavrikis, and H. Pain. Diagnosing and acting on student affect: the tutor's perspective. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 18(1-2):125-173, 2008.
17. K. VanLehn and J. Martin. Evaluation of an assessment system based on bayesian student modeling. Int. Journal of AI in Education, 42:575-592, 1998.
18. T. Verma and J. Pearl. An algorithm for deciding if a set of observed independencies has a causal explanation. In Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pages 323-330, 1992.
19. B. Weiner. Human motivation: Metaphors, theories, and research. Sage, 1992.
20. Ian H. Witten and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2nd edition edition, 2005.
21. H. Wood and D. Wood. Help seeking, learning and contigent tutoring. Computers & Education, 33:153-169, 1999.
22. L. Yu and L. Huan. Feature selection for high-dimensional data: A fast correlationbased filter solution. In International Conference on Machine Learning, 2003.