Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...

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An MD-Database Mined MD-Sequences ID Dimensions Sequences Dimensions Sequences 1 true, novice *, novice, 2 true, expert *, * 3 false, novice � *, novice 4 false, interm. true, * 5 true, novice true, novice 6 true, expert
55 Sequential Pattern Mining/Partial Problem Spaces [3] Lynch, C., Ashley, K., Aleven, V. and Pinkwart, N. (2006). Defining Ill-Defined Domains; A literature survey. Proc. of the Intelligent Tutoring Systems for Ill-Defined Domains Workshop. pp.1-10. ITS’2006. [4] Kabanza, F., Nkambou, R. & Belghith, K. (2005), Path-planning for Autonomous Training on Robot Manipulators in Space, Proc. of IJCAI 2005. [5] Fournier-Viger, P., Nkambou, R. & Mayers., A. (2008). A Framework for Evaluating Semantic Knowledge in Problem-Solving-Based Intelligent Tutoring Systems. Proc. of FLAIRS 2008, AAAI press, pp. 409-414. [6] McLaren, B. & al. (2004). Bootstrapping Novice Data: Semi-Automated Tutor Authoring Using Student Log Files. Proc. of the Workshop on Analyzing Student-Tutor Logs. ITS’2004. [7] Jarivs, M., Nuzzo-Jones, G. & Heffernan, N.T. (2006) Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems. Proc. of ITS’2006, pp. 541-553. [8] Agrawal, R. & Srikant, R.: (1995). Mining Sequential Patterns. Proc. Int. Conf. on Data Engineering, pp. 3-14. [9] Pei, J., Han, J. et al. (2004). Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. Knowledge and Data Engineering, 16(10), 1-17. [10] Hirate, Y., Yamana, H. (2006). Generalized Sequential Pattern Mining with Item Intervals, Journal of Computers, 1(3), pp. 51-60. [11] MacQueen, J.B. (1967). Some Methods for Classification and Analysis of Multivariate Observations, Proc. 5 th Berkeley Symposium on Mathematic Statistics and Probability, pp. 281-297. [12] Pinto, H et al. (2001), Multi-Dimensional Sequential Pattern Mining, Proc. Int. Conf. Information and Knowledge Management (CIKM2001), pp. 81-88. [13] Wang, J., Han, J & Li, C. (2007). Frequent Closed Sequence Mining without Candidate Maintenance, IEEE Transactions on Knowledge and Data Engineering, 19(8), pp.1042-1056 Acknowledgment. Our thanks go to the FQRNT and the NSERC for their logistic and financial support. The authors also thank the current/past members of GDAC/PLANIART for their work in RomanTutor, and Severin Vigot for integrating the algorithm in RomanTutor.
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Intelligent Tutoring Systems for Il
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iii Aleven, Ashley, Lynch & Pinkwar
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v Ashley Intelligent Tutoring Syste
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vii Ashley Methods for Assessment a
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Two Approaches for Providing Adapti
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Page 43 and 44: Generating and Evaluating Object-Or
Page 45 and 46: 37 Moritz & Blank using them to der
Page 47 and 48: 39 Moritz & Blank searching for phr
Page 49 and 50: 41 Moritz & Blank as a related term
Page 51 and 52: 43 Moritz & Blank to match on data
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Page 55 and 56: 47 Fournier-Viger, Nkambou, & Mephu
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Page 65 and 66: 57 Lynch, Pinkwart, Ashley, & Aleve
Page 77 and 78: 69 Gauthier, Naismith, Lajoie, & Wi
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