Student modeling

Studies in Computational Intelligence

Volumn 308, Issue , 2010, Pages 267-279

  Woolf, Beverly Park ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957271475     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-14363-2_13     Document Type: Article

References (41)

1. Anderson, J. R.: The Architecture of Cognition. Harvard University Press, Cambridge (1983)
2. Anderson, J. R., Reiser, B.: The Lisp Tutor. BYTE 10, 159-175 (1985)
3. Anderson, J. R., Boyle, C. F., Farrell, R., Reiser, B. J.: Cognitive principles in the design of computer tutors. In: Morris, P. (ed.) Modelling Cognition. Wiley, Chichester (1987)
4. Arroyo, I., Woolf, B.: Inferring Learning and Attitudes from a Bayesian Network of Log File Data. In: Looi, C. K., McCalla, G., Bredeweg, B. Breuker, J. (eds.) Twelfth International Conference on Artificial Intelligence in Education, Amsterdam (2005)
5. Arroyo, I., Beal, C. R., Murray, T., Walles, R., Woolf, B. P.: Web-Based Intelligent Multimedia Tutoring for High Stakes Achievement Tests. In: James, R. M. V., Lester, C., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 468-477. Springer, Heidelberg (2004) (Pubitemid 39245921)
6. Beal, C. R.: Boys and Girls: The Development of Gender Roles. McGraw Hill, New York (1994)
7. Brachman, R., Levesque, H.: Knowledge Representation and Reasoning. Morgan Kaufmann (part of Elsevier's Science and Technology Division) (2004)
8. Bredeweg, B., Arroyo, I., Carney, C., Mavrikis, M., Timms, M.: Intelligent Environments. In: Global Resources for Online Education Workshop, Brighton, UK (2009)
9. Brusilovsky, P.: The Construction and Application of Student Models in Intelligent Tutoring Systems. Journal of computer and systems sciences international 32(1), 70-89 (1994)
10. Burleson, W.: Affective Learning Companions: Strategies for Empathetic Agents with Real-Time Multimodal Affective Sensing to Foster Meta-Cognitive and Meta-Affective Approaches to Learning, Motivation, and Perseverance. MIT PhD Thesis. (2006), http://affect.media.mit.edu/publications.php
11. Clancey, W.: Qualitative Student Models. Annual Review of Computer Science 1, 381-450 (1986)
12. Corbett, A. T., Anderson, J. R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253-278 (1995)
13. Eliot, C.: An Intelligent Tutoring System Based Upon Adaptive Simulation, Computer Science Department. University of Massachusetts, Amherst (1996)
14. Ferguson, K., Arroyo, I., Mahadevan, S., Woolf, B., Barto, A.: Improving Intelligent Tutoring Systems: Using EM to Learn Student Skill Levels. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 453-462. Springer, Heidelberg (2006)
15. IJAIED 2009 special issue on Authoring IEEE-TLT 2009 (2009)
16. Johns, J., Woolf, B.: A Dynamic Mixture Model to Detect Student Motivation and Proficiency. In: Conference on Artificial Intelligence (AAAI 2006), pp. 2-8. AAAI Press, Menlo Park (2006) (Pubitemid 44705280)
17. Kay, J.: Lies, Damned Lies and Stereotypes: Pragmatic Approximations of Users. In: Proceedings of the Fourth International Conference on User Modeling, pp. 175-184 (1994)
18. Kobsa, A. (ed.): Adaptive Web 2007. LNCS, vol. 4321. Springer, Heidelberg (2007)
19. Koedinger, K., Corbett, A.: Cognitive Tutors. In: Sawyer, K. (ed.) The Cambridge Handbook of the Learning Sciences. Cambridge University Press, Cambridge (2006)
20. Leonhardt, D.: Adding Art to the Rigor of Statistical Science. The New York Times, New York (2001)
21. Martin, B.: Intelligent Tutoring Systems: The Practical Implementations of Constraint-Based Modeling. Computer Science. University of Cantebury, Christchurch (2001), http://coscweb2.cosc.canterbury.ac.nz/research/reports/PhdT heses/2003/phd-0301pdf
22. Mayo, M., Mitrovic, A.: Optimizing ITS Behavior with Bayesian Networks and Decision Theory. International Journal of Artificial Intelligence in Education 12, 124-153 (2001)
23. Mislevy, R. J., Gitomer, D. H.: The role of probability-based inference in an intelligent tutoring system. User Modeling and User Adapted Interaction 5 (3-4), 253-282 (1996)
24. Mitrovic, A.: Experience in Implementing Constraint-Based Modeling in SQL-Tutor. In: Goettl, B. P., Halff, H. M., Redfield, C. L., Shute, V. (eds.) ITS 1998. LNCS, vol. 1452, pp. 414-423. Springer, Heidelberg (1998) (Pubitemid 128115417)
25. Mitrovic, A., Ohlsson, S.: Evaluation of a Constraint-Based Tutor for a Database Language. Artificial Intelligence in Education 10 (3-4), 238-256 (1999)
26. Mitrovic, A., Suraweera, P., Martin, B., Weerasinghe, A.: DB-suite: Experiences with Three Intelligent, Web-based Database Tutors. Journal of Interactive Learning Research (JILR) 15(4), 409-432 (2004) (Pubitemid 351505999)
27. Nii, H. P.: Expert Systems Building Tools: Definitions (2009), http://www.wtec.org/loyola/kb/c3-s2.htm
28. Ohlsson, S.: Constraint-Based Student Modeling. In: Greer, J. E., McCalla, G. (eds.) Student Modeling: The Key to Individualized Knowledge-Based Instruction, pp. 167-189 (1994)
29. Ohlsson, S.: Learning from performance errors. Psychological Review 103, 241-262 (1996) (Pubitemid 126421429)
30. Pearl, J.: Probabilistic Reasoning in Intelligent Systems Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
31. Razzaq, L., Patvarczki, J., Almeida, S., Vartak, M., Feng, M., Heffernan, N., Koedinger, K.: The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Creation. IEEE Transaction on Learning Technologies 2(2), 157-166 (2009)
32. Rich: Users are Individuals: Individualizing User Models. International Journal of Man-Machine Studies 18, 199-214 (1983)
33. Self, J. A.: Bypassing the intractable problem of student modelling. In: Frasson, C., Gauthier, G. (eds.) Intelligent tutoring systems: at the crossroads of artificial intelligence and education. Ablex Publishing, Norwood (1990)
34. Self, J. A.: Formal Approaches to Student Modeling. In: McCalla, G., Greer, J. E. (eds.) Student Models: The Key to Individual Educational Systems. Springer, New York (1994)
35. Shapiro, S.: Encyclopedia of Artificial Intelligence, 2nd edn. John Wiley & Sons, Chichester (1992)
36. Shute, V. J., Graf, E. A., Hansen, E.: Designing adaptive, diagnostic math assessments for individuals with and without visual disabilities. In: PytlikZillig, L., Bruning, R., Bodvarsson, M. (eds.) Technology-based education: Bringing researchers and practitioners. Information Age Publishing, Greenwich (2005)
37. Shute, V. J., Zapata, D., Kuntz, D., Levy, R., Baker, R., Beck, J., Christopher, R.: Assessment: A Vision, Global Resources for Online Education (GROE), Tempe Arizona (2009)
38. Sison, R., Shimura, M.: Student Modeling and Machine Learning. International Journal of Artificial Intelligence in Education 9, 128-158 (1998)
39. Webb, G., Pazzani, M., Billsus, D.: Machine Learning for User Modeling in User Modeling and User-Adapted Interaction, Netherlands 11, 19, 29 (2001)
40. Woolf, B.: Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning, vol. 480. Morgan Kaufmann, Burlington (2008)
41. Yacci: A Grounded Theory of Student Choice in Information-Rich Learning Environments. Journal of Educational Multimedia and Hypermedia 3 (3-4), 327-350 (1994)