Fifteen years of constraint-based tutors: what we have achieved and where we are going

Original Paper

Abstract

Fifteen years ago, research started on SQL-Tutor, the first constraint-based tutor. The initial efforts were focused on evaluating Constraint-Based Modeling (CBM), its effectiveness and applicability to various instructional domains. Since then, we extended CBM in a number of ways, and developed many constraint-based tutors. Our tutors teach both well- and ill-defined domains and tasks, and deal with domain- and meta-level skills. We have supported mainly individual learning, but also the acquisition of collaborative skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. Our current research focuses on building affect-sensitive and motivational tutors. Over the period of fifteen years, CBM has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.

Keywords

Constraint-based modeling Constraint-based tutors Authoring Affective modeling Metacognitive skills Collaborative learning 

References

  1. Aleven, V., Koedinger, K.:Limitations of student control: do students know when they need help? In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) Proceedings of ITS 2000, pp. 292–303 (2000)Google Scholar
  2. Aleven, V., Ogan, A., Popescu, O., Torrey, C., Koedinger, K.: Evaluating the effectiveness of a tutorial dialogue system for self-explanation. In: Lester, J., Vicario, R.M., Paraguacu, F. (eds.) Proceedings of ITS2004, pp. 443–454 (2004)Google Scholar
  3. Amalathas S., Mitrovic A., Saravanan R., Evison D. et al.: Developing an intelligent tutoring system for palm oil in ASPIRE. In: Wong, S.L. (eds) Proceedings of 18th International Conference on Computers in Education, pp. 101–103. APSCE, Putrajaya, Malaysia (2010)Google Scholar
  4. Anderson J.R., Boyle C.F., Corbett A.T., Lewis M.W.: Cognitive modeling and intelligent tutoring. Artif. Intell. 42, 7–49 (1990)CrossRefGoogle Scholar
  5. Anderson J.R., Corbett A.T., Koedinger K.R., Pelletier R.: Cognitive tutors: lessons learned. Learn. Sci. 4(2), 167–207 (1995)CrossRefGoogle Scholar
  6. Angros R., Johnson W.L., Rickel J., Scholer A.: Learning domain knowledge for teaching procedural skills. In: Gini, M., Ishida, T., Castelfranchi, C., Johnson, W.L. (eds) Autonomous Agents and Multiagent Systems., pp. 1372–1378. ACM, New York (2002)Google Scholar
  7. Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A. (eds.) Proceedings of 14th International Conference on Artificial Intelligence in Education, pp. 41–48 (2009)Google Scholar
  8. Baghaei N., Mitrovic A., Irvin W.: Problem-solving support in a constraint-based intelligent tutoring system for UML. Technol. Instr. Cogn. Learn. 4(2), 113–137 (2006)Google Scholar
  9. Baghaei N., Mitrovic A., Irwin W.: Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. Comput. Support. Collab. Learn. 2(2–3), 159–190 (2007)CrossRefGoogle Scholar
  10. Barros B., Verdejo M.F.: Analysing student interaction processes in order to improve collaboration: the DEGREE approach. Artif. Intell. Educ. 11, 221–241 (2000)Google Scholar
  11. Barrow D., Mitrovic A., Ohlsson S., Grimley M. et al.: Assessing the impact of positive feedback in constraint-based ITSs. In: Woolf, B. (eds) Proceedings of 9th International Conference on ITS 2008, LCNS 5091, pp. 250–259. Springer-Verlag, Heidelberg (2008)Google Scholar
  12. Billingsley, W., Robinson, P.: Towards an intelligent online book for discrete mathematics. In: Proceedings of International Conference Active Media Technology, pp. 291–296 (2005)Google Scholar
  13. Billingsley, W., Robinson, P., Ashdown, M., Hanson, C.: Intelligent tutoring and supervised problem solving in the browser. In: Proceedings of International Conference on WWW/Internet, pp. 806–811 (2004)Google Scholar
  14. Blessing S.B.: Programming by demonstration authoring tool for model-tracing tutors. Artif. Intell. Educ. 8, 233–261 (1997)Google Scholar
  15. Boyer K.E., Phillips R., Wallis M., Vouk M., Lester J.: Balancing cognitive and motivational scaffolding in tutorial dialogues. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoi, S. (eds) Proceedings Intelligent Tutoring Systems, pp. 239–249. Springer-Verlag, Berlin (2008)CrossRefGoogle Scholar
  16. Brown J.S., Burton R.R.: Diagnostic models for procedural bugs in basic mathematical skills. Cogn. Sci. 2, 155–192 (1978)CrossRefGoogle Scholar
  17. Brusilovsky, P., Schwartz, E., Weber, G.: A tool for developing adaptive electronic textbooks on WWW. In: Proceedings of WebNet-96, AACE, (1996)Google Scholar
  18. Bull, S.: Supporting learning with open learner models. In: Proceedings of 4th Hellenic Conference in Information and Communication Technologies in Education, pp. 47–61 (2004)Google Scholar
  19. Bull, S., Hghiem, T.: Helping learners to understand themselves with a learner model open to students, peers and instructions. In: Proceedings of International Conference on Intelligent Tutoring Systems, pp. 5–13 (2002)Google Scholar
  20. Cade W.L., Copeland J.L., Person N.K., D’Mello S.: Dialogue modes in expert tutoring. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds) Proceedings on Intelligent Tutoring Systems, pp. 470–479. Springer-Verlag, New York, NY (2008)CrossRefGoogle Scholar
  21. Constantino-Gonzalez M.A., Suthers D., Escamilladelos Santos J.: Coaching web-based collaborative learning based on problem solution differences and participation. Artif. Intell. Educ. 13(2–4), 263–299 (2003)Google Scholar
  22. Cook, R., Kay, J.: The Justified User Model. In: Proceedings on UM 1994, pp. 145–150 (1994)Google Scholar
  23. Czarkowski M., Kay J., Potts S.: Scrutability as a core interface element. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds) Proceedings of 12th International Conference on Artificial Intelligence in Education, pp. 783–785. IOS Press, Amsterdam (2005)Google Scholar
  24. D’Mello S., Graesser A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Model. User-Adapt. Interact. 20(2), 147–187 (2010)CrossRefGoogle Scholar
  25. D’Mello S., Picard R., Graesser A.: Toward an affect-sensitive autotutor. IEEE Intell. Syst. 22, 53–61 (2007)CrossRefGoogle Scholar
  26. Desmarais, M., Baker, R.: Learner models’ adapting to student skills and behavioral factors. User Model. User-Adapt. Interact. 22 (this issue) (2012)Google Scholar
  27. Di Eugenio B., Fossati D., Ohlsson S., Cosejo D.: Towards explaining effective tutorial dialogues. In: Taatgen, N.A., van Rijn, H. (eds) Proceedings of 31th Annual Conference of the Cognitive Science Society, pp. 1430–1435. Cognitive Science Society, Austin, TX (2009)Google Scholar
  28. Dimitrova V.: StyLE-OLM: interactive open learner modelling. Artif. Intell. Educ. 13(1), 35–78 (2003)Google Scholar
  29. du Boulay B., Avramides K., Luckin R., Martinez-Miron E., Rebolledo-Mendez G., Carr A.: Towards systems that care: a conceptual framework based on motivation, metacognition and affect. Artif. Intell. Educ., 20(3), 197–229 (2010)Google Scholar
  30. Duan D., Mitrovic A., Churcher N. et al.: Evaluating the effectiveness of multiple open student models in EER-tutor. In: Wong, S.L. (eds) Proceedings of 18th International Conference on Computers in Education ICCE 2010, pp. 86–88. APSCE, Putrajaya (2010)Google Scholar
  31. Elmasri R., Navathe S.B.: Fundamentals of Database Systems. Addison-Wesley, Reading (2006)Google Scholar
  32. Forgas J.P.: Affect and cognition. Perspect. Psychol. Sci. 3, 94–101 (2008)CrossRefGoogle Scholar
  33. Galvez, J., Guzman, E., Conejo, R., Millan, E.: Student knowledge diagnosis using item response theory and constraint-based modeling. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A. (eds.) Proceedings of 14th International Conference on Artificial Intelligence in Education, pp. 291–298 (2009a)Google Scholar
  34. Galvez J., Guzman E., Conejo R.: A blended e-learning experience in a course of object oriented programming fundamentals. Knowl. Based Syst. 22(4), 279–286 (2009b)CrossRefGoogle Scholar
  35. Graesser A.C., VanLehn K., Rose C.P., Jordan P.W., Harter D.: Intelligent tutoring systems with conversational dialogue. AI Mag. 22(4), 39–51 (2001)Google Scholar
  36. Goleman D.: Emotional Intelligence. Bantam Books, New York (1995)Google Scholar
  37. Hartley, D., Mitrovic, A.: Supporting learning by opening the student model. In: Cerri, S., Gouarderes, G., Paraguacu, F. (eds.) Proceedings of 6th Internationa Conference on Intelligent Tutoring Systems ITS 2002. LCNS, vol. 2363, pp. 453–462. Biarritz, France (2002)Google Scholar
  38. Holland J., Mitrovic A., Martin B.: A constraint-based tutor for Java. In: Kong, S.C., Ogata, H., Arnseth, H.C., Chan, C.K.K., Hirashima, T., Klett, F., Lee, J.H.M., Liu, C.C., Looi, C.K., Milrad, M., Mitrovic, A., Nakabayashi, K., Wong, S.L., Yang, S.J.H. (eds) Proceedings of 17th International Conference on Computers in Education ICCE 2009, pp. 142–146. Asia-Pacific Society for Computers in Education, Hong Kong (2009)Google Scholar
  39. Holt, P., Dubs, S., Jones, M., Greer, J.: The state of student modeling. In: Student Modeling: The Key to Individualized Knowledge-Based Instruction, pp. 3–39. Springer-Verlag, Heidelberg (1994)Google Scholar
  40. Inaba, A., Mizoguchi, R.: Learners’ roles and predictable educational benefits in collaborative learning; an ontological approach to support design and analysis of CSCL. In: Lester, J., Vicari, R.M., Paraguacu, F. (eds.) Proceedings of ITS 2004, pp. 285–294 (2004)Google Scholar
  41. Jarboe S.: Procedures for enhancing group decision making. In: Hirokawa, B., Poole, M. (eds) Communication and Group Decision Making, pp. 345–383. Sage Publications, Thousand Oaks, CA (1996)Google Scholar
  42. Jerman, P., Soller, A., Muhlenbrock, M.: From mirroring to guiding: a review of state of the art technology for supporting collaborative learning. In: Dillenbourg, P., Eurelings, A., Hakkarainen, K. (eds.) European Perspectives on CSCL (CSCL 2001), pp. 324–331 (2001)Google Scholar
  43. Kay, J.: Learner know thyself: student models to give learner control and responsibility. In: Halim, Z., Ottomann, T., Razak, Z. (eds) Proceedings of International Conference on Computers in Education, pp. 17–24 (1997)Google Scholar
  44. Klein J., Moon Y., Picard R.: This computer responds to user frustration: theory, design, and results. Interact. Comput. 14, 119–140 (2002)CrossRefGoogle Scholar
  45. Koedinger K.R., Anderson J.R., Hadley W.H., Mark M.A.: Intelligent tutoring goes to the big city. Artif. Intell. Educ. 8, 30–43 (1997)Google Scholar
  46. Kort B., Reilly R.: An affective module for an intelligent tutoring system. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds) Proceedings of ITS 2002. LNCS, vol. 2363, pp. 955–962. Springer, Heidelberg (2002)Google Scholar
  47. Le, N.-T.: A constraint-based assessment approach for free-form design of class diagrams using UML. In: Ashley, K., Pinkwart, N., Lynch, C. (eds.) Proceedings of Workshop on Intelligent Tutoring Systems for Ill-Defined Domains, 8th International Conference on ITS, pp. 11–19 (2006)Google Scholar
  48. Le N.-T., Menzel W., Pinkwart N.: Evaluation of a constraint-based homework assistance system for logic programming. In: Kong, S.C., Ogata, H., Arnseth, H.C., Chan, C.K.K., Hirashima, T., Klett, F., Lee, J.H.M., Liu, C.C., Looi, C.K., Milrad, M., Mitrovic, A., Nakabayashi, K., Wong, S.L., Yang, S.J.H. (eds) Proceedings of 17th International Conference on Computers in Education, pp. 51–58. APSCE, Putrajaya (2009)Google Scholar
  49. Mabbott, A., Bull, S.: Student preferences for editing, persuading and negotiating the open learner model. IN: Proceedings of ITS 2006, pp. 481–490 (2006)Google Scholar
  50. Mabbott A., Bull S. et al.: Comparing student-constructed open learner model presentations to the domain. In: Luckin, R. (eds) Proceedings Artificial Intelligence in Education, pp. 281–288. IOS Press, Chris (2007)Google Scholar
  51. Major N., Ainsworth S., Wood D.: REDEEM: exploiting symbiosis between psychology and authoring environments. Artif. Intell. Educ. 8(3–4), 317–340 (1997)Google Scholar
  52. Martin B., Mitrovic A.: Domain modeling: art or science?. In: Hoppe, U., Verdejo, F., Kay, J. (eds) Proceeindgs of 11th International Conference on Artificial Intelligence in Education AIED 2003, pp. 183–190. IOS Press, Amsterdam (2003)Google Scholar
  53. Martin B., Mitrovic A.: The effect of adapting feedback generality in ITSs. In: Wade, V., Ashman, H., Smyth, B (eds) Proceedings of AH 2006. LNCS, vol. 4018., pp. 192–202. Springer, Heidelberg (2006)Google Scholar
  54. Martin B., Kirkbride T., Mitrovic A., Holland J., Zakharov K.: An intelligent tutoring system for medical imaging. In: Bastiaens, T., Dron, J., Xin, C. (eds) Proceedings of World Conferences E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 502–509. AACE, Vancouver, CA (2009)Google Scholar
  55. Mathews, M., Mitrovic, A.: Investigating the effectiveness of problem templates on learning in ITSs. In: Luckin, R., Koedinger, K., Greer, J. (eds.) Proceedings of Artificial Intelligence in Education, pp. 611–613 (2007)Google Scholar
  56. Mathews M., Mitrovic A.: Does framing a problem-solving scenario influence learning?. In: Kong, S.C., Ogata, H., Arnseth, H.C., Chan, C.K., Hirashima, T., Klett, F., Lee, J.H.M., Liu, C.C., Looi, C.K., Milrad, M., Mitrovic, A., Nakabayashi, K., Wong, S.L., Yang, S.J.H (eds) Proceedings of 17th International Conference on Computers in Education ICCE 2009, pp. 27–34. Asia-Pacific Society for Computers in Education, Hong Kong (2009)Google Scholar
  57. Matsuda N., Cohen W., Sewall J., Lacerda G., Koedinger K.: Evaluating a simulated student using real students data for training and testing. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modelling 2007, pp. 61–70. Springer, Berlin (2007)Google Scholar
  58. Mayo M., Mitrovic A.: Using a probabilistic student model to control problem difficulty. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Proceedings of Intelligent Tutoring Systems, pp. 524–533. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  59. Mayo M., Mitrovic A.: Optimising ITS behaviour with bayesian networks and decision theory. Artif. Intell. Educ. 12(2), 124–153 (2001)Google Scholar
  60. McManus M., Aiken R.: Monitoring computer-based problem solving. Artif. Intell. Educ. 6(4), 307–336 (1995)Google Scholar
  61. Menzel W.: Constraint-based modeling and ambiguity. Artif. Intell. Educ. 16(1), 29–63 (2006)Google Scholar
  62. Milik N., Marshall M., Mitrovic A.: Teaching logical database design. In: Ikeda, M., Ashley, K., Chan, T.-W. (eds) Proceedings of ITS 2006 ERM-Tutor. LNCS, vol. 4053, pp. 707–709. Springer, Heidelberg (2006)Google Scholar
  63. Mills, C., Dalgarno, B.: A conceptual model for game-based intelligent tutoring systems. In ICT: providing choices for learners and learning. In: Proceedings of ASCILITE, pp. 692–701. Singapore (2007)Google Scholar
  64. Millis, B., Evens, M., Freedman, R.: Implementing directed lines of reasoning in an intelligent tutoring system using the atlas planning environment. In: International Conference on Information Technology, pp. 729–733 (2004)Google Scholar
  65. Mitrovic, A.: Learning SQL with a computerized tutor. In: 29th ACM SIGCSE technical symposium, pp. 307–311 (1998a)Google Scholar
  66. Mitrovic A.: A knowledge-based teaching system for SQL. In: Ottmann, T., Tomek, I. (eds) Proceedings of ED-MEDIA’98, pp. 1027–1032. AACE, Vancouver, CA (1998b)Google Scholar
  67. Mitrovic, A.: Experiences in implementing constraint-based modeling in SQL-Tutor. In: Goettl, B., Halff, H., Redfield, C., Shute, V. (eds.), Proceedings of ITS’98, pp. 414–423 (1998c)Google Scholar
  68. Mitrovic A.: An intelligent SQL tutor on the web. Artif. Intell. Educ. 13(2), 173–197 (2003)Google Scholar
  69. Mitrovic, A.: The effect of explaining on learning: a case study with a data normalization tutor. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Proceedings of Conference on Artificial Intelligence in Education, pp. 499–506 (2005)Google Scholar
  70. Mitrovic A., Martin B.: Evaluating adaptive problem selection. In: De Bra, P., Nejdl, W. (eds) Proceedings of Adaptive Hypermedia and Adaptive Web-Based Systems. LNCS, vol. 3137, pp. 185–194. Springer-Verlag, Heidelberg (2004)CrossRefGoogle Scholar
  71. Mitrovic A., Martin B.: Evaluating the effect of open student models on self-assessment. Artif. Intell. Educ. 17(2), 121–144 (2007)Google Scholar
  72. Mitrovic A., Ohlsson S.: Evaluation of a constraint-based tutor for a database language. Artif. Intell. Educ. 10(3–4), 238–256 (1999)Google Scholar
  73. Mitrovic, A., Weerasinghe, A.: Revisiting the ill-definedness and consequences for ITSs. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A. (eds) Proceedings of 14th International Conference on Artificial Intelligence in Education, pp. 375–382 (2009)Google Scholar
  74. Mitrovic A., Koedinger K., Martin B.: A comparative analysis of cognitive tutoring and constraint-based modeling. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds) Proceedings of Conference on User Modeling, LNAI 2702 , pp. 313–322. Springer-Verlag, Heidelberg (2003)Google Scholar
  75. Mitrovic A., Suraweera P., Martin B., Weerasinghe A.: DB-suite: experiences with three intelligent, web-based database tutors. J. Interact. Learn. Res. 15(4), 409–432 (2004)Google Scholar
  76. Mitrovic, A., Suraweera, P., Martin, B., Zakharov, K., Milik, N., Holland, J.: Authoring constraint-based tutors in ASPIRE. In: Ikeda, M., Ashley, K., Chan, T.-W. (eds.) Proceedings of ITS 2006. LNCS, vol. 4053, pp. 41–50 (2006)Google Scholar
  77. Mitrovic A., Martin B., Suraweera P.: Intelligent tutors for all: constraint-based modeling methodology, systems and authoring. IEEE Intell. Syst. 22(4), 38–45 (2007)CrossRefGoogle Scholar
  78. Mitrovic, A., McGuigan, N., Martin, B., Suraweera, P., Milik, N., Holland, J.: Authoring constraint-based systems in ASPIRE: a case study of a capital investment tutor. In: Proceedings of ED-MEDIA 2008, pp. 4607–4616 (2008)Google Scholar
  79. Mitrovic A., Martin B., Suraweera P., Zakharov K., Milik N., Holland J., McGuigan N.: ASPIRE: an authoring system and deployment environment for constraint-based tutors. Artif. Intell. Educ. 19(2), 155–188 (2009)Google Scholar
  80. Mitrovic, A., Williamson, C., Bebbington, A., Mathews, M., Suraweera, P., Martin, B., Thomson, D., Holland, J.: An Intelligent Tutoring System for Thermodynamics. EDUCON 2011 Amman, Jordan, pp. 378–385 (2011)Google Scholar
  81. Muldner K., Burleson W., VanLehn K.: Yes!: using tutor and sensor data to predict moments of delight during instructional activities. In: DeBra, P., Kobsa, A., Chin, D. (eds) User modeling, adaptation and personalization. LCNS, vol. 6075, pp. 170–195. Springer, Heidelberg (2009)Google Scholar
  82. Murray T.: Expanding the knowledge acquisition bottleneck for intelligent tutoring systems. Artif. Intell. Educ. 8(3), 222–232 (1997)Google Scholar
  83. Murray T.: Authoring intelligent tutoring systems: an analysis of the state of the art. Artif. Intell. Educ. 10(1), 98–129 (1999)Google Scholar
  84. Murray T.: An overview of intelligent tutoring system authoring tools: updated analysis of the state of the art. In: Murray, T., Blessing, S., Ainsworth, S. (eds) Authoring tools for advanced technology learning environments, pp. 491–545. Kluwer Academic Publishers, Norwell, MA (2003)Google Scholar
  85. Ogata, H., Matsuura, K., Yano, Y.: Active knowledge awareness map: visualizing learners activities in a web based CSCL environment. In: Procedings of International Workshop on New Technologies in Collaborative Learning, pp. 89–97 (2000)Google Scholar
  86. Oh Y., Gross M.D., Ishizaki S., Do Y.-L.: Constraint-based  design  critic  for  flat-pack furniture design. In: Kong, S.C., Ogata, H., Arnseth, H.C., Chan, C.K.K., Hirashima, T., Klett, F., Lee, J.H.M., Liu, C.C., Looi, C.K., Milrad, M., Mitrovic, A., Nakabayashi, K., Wong, S.L., Yang, S.J.H. (eds) Proceedings of 17th International Conference on Computers in Education, pp. 19–26. APSCE, Chris (2009)Google Scholar
  87. Ohlsson S.: Constraint-based student modeling. Artif. Intell. Educ. 3(4), 429–447 (1992)Google Scholar
  88. Ohlsson S.: Learning from performance errors. Psychol. Rev. 103, 241–262 (1996)CrossRefGoogle Scholar
  89. Ohlsson S., Bee N.: Strategy variability: a challenge to models of procedural learning. In: Birnbaum, L. (eds) Proceedings of International Confernce of the Learning Sciences, pp. 351–356. AACE, Vancouver, CA (1991)Google Scholar
  90. Ohlsson S., Mitrovic A.: Fidelity and efficiency of knowledge representations for intelligent tutoring systems. Technol. Instr. Cogn. Learn. 5(2), 101–132 (2007)Google Scholar
  91. Ohlsson S., Di Eugenio B., Chow B., Fossati D., Lu X., Kershaw T.: Beyond the code-and-count analysis of tutoring dialogues. In: Luckin, R., Koedinger, K., Greer, J. (eds) Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work, pp. 349–356. IOS Press, Amsterdam (2007)Google Scholar
  92. Perez-Martin, D., Alfonseca, E., Rodriguez, P., Pascual-Nieto, I.: Automatic generation of students conceptual models from answers in plain text. In: Procedings of UM 2007, pp. 329–333. Springer-Verlag, Heidelberg (2007)Google Scholar
  93. Petry, P.G., Rosatelli, M.: AlgoLC: a learning companion system for teaching and learning algorithms. In: Ikeda, M., Ashley, K., Chan, T.-W. (eds.) Proceedings of ITS 2006. LNCS, vol. 4053, pp. 775–777 (2006)Google Scholar
  94. Picard R.W.: Affective Computing. MIT Press, Cambridge, MA (1997)Google Scholar
  95. Picard R.W.: Toward computers that recognize and respond to user emotion. IBM Syst. J. 39(3–4), 705–719 (2010)Google Scholar
  96. Riccucci, S., Carbonaro, A., Casadei, G.: An architecture for knowledge management in intelligent tutoring systems. In: Proceedings of IADIS International Congress on Cognition and Exploratory Learning in Digital Age, pp. 473–476 (2005)Google Scholar
  97. Roll I., Aleven V., Koedinger K.: The invention lab: using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In: Aleven, V., Kay, J., Mostow, J. (eds) ITS 2010, Part I. LNCS, vol. 6094, pp. 115–124. Springer-Verlag, Berlin, Heidelberg (2010)Google Scholar
  98. Rosatelli, M., Self, J., Thirty, M.: LeCS: a collaborative case study system. In: Proceedings of 5th International Conference on Intelligent Tutoring Systems, pp. 242–251 (2000)Google Scholar
  99. Rosatelli M., Self J.: A collaborative case study system for distance learning. Artif. Intell. Educ. 14(1), 1–29 (2004)Google Scholar
  100. Siddappa, M., Manjunath A.S.: Intelligent tutor generator for intelligent tutoring systems. In: Proceedings of World Congress on Engineering and Computer Science, pp. 578–583 (2008)Google Scholar
  101. Shute, V.J.: DNA—Uncorking the bottleneck in knowledge elicitation and organization. In: Proceedings of ITS-98, pp. 146–155 (1998)Google Scholar
  102. Sleeman D., Kelly A.E., Martinak R., Ward R.D., Moore J.L.: Studies of diagnosis and remediation with high school algebra students. Cogn. Sci. 13, 551–568 (1989)CrossRefGoogle Scholar
  103. Soller A.: Supporting social interaction in an intelligent collaborative learning system. Artif. Intell. Educ. 12, 40–62 (2001)Google Scholar
  104. Soller, A., Lesgold, A.: Knowledge acquisition for adaptive collaborative learning environments. In: Proceedings of AAAI Fall Symposium: Learning How to Do Things, Cape Cod, MA (2000)Google Scholar
  105. Spohrer J.C., Soloway E., Pope E.: A goal/plan analysis of buggy pascal programs. Hum.-Comput. Interact. 1, 163–205 (1985)CrossRefGoogle Scholar
  106. Suraweera, P., Mitrovic, A.: KERMIT: a constraint-based tutor for database modelling. In: Cerri, S.A., Gouarderes, G., Paraguacu, F. (eds.) Proceedings of 6th International Conference on Intelligent Tutoring Systems, pp. 376–387 (2002)Google Scholar
  107. Suraweera P., Mitrovic A.: An intelligent tutoring system for entity relationship modelling. Artif. Intell. Educ. 14(3–4), 375–417 (2004)Google Scholar
  108. Suraweera P., Mitrovic A., Martin B.: The role of domain ontology in knowledge acquisition for ITSs. In: Lester, J., Vicari, R.M., Paraguacu, F. (eds) Proceedings of 7th Interenational Conference on Intelligent Tutoring Systems ITS 2004. LNCS, vol. 3220, pp. 207–216. Springer-Verlag, Maceio (2004)Google Scholar
  109. Suraweera P., Mitrovic A., Martin B.: A knowledge acquisition system for constraint-based intelligent tutoring systems. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds) Proceedings Artificial Intelligence in Education AIED 2005, pp. 638–645. IOS Press, Amsterdam (2005)Google Scholar
  110. Suraweera, P., Mitrovic, A., Martin, B.: Constraint authoring system: an empirical evaluation. In: Luckin, R., Koedinger, K., Greer, J. (eds.) Proceedings 13th International Conference Artificial Intelligence in Education AIED 2007, Los Angeles, pp. 451–458 (2007)Google Scholar
  111. Suraweera P., Mitrovic A., Martin B.: Widening the knowledge acquisition bottleneck for constraint-based tutors. Artif. Intell. Educ. 20(2), 137–173 (2010)Google Scholar
  112. Tecuci G.: Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. Morgan Kaufmann, San Francisco (1998)Google Scholar
  113. Thomson D., Mitrovic A.: Preliminary evaluation of a negotiable student model in a constraint-based ITS. Res. Prac. Technol. Enhanc. Learn. 5(1), 19–33 (2010)CrossRefGoogle Scholar
  114. VanLehn K.: The behaviour of tutoring systems. Artif. Intell. Educ. 16(3), 227–265 (2006)Google Scholar
  115. VanLehn K., Lynch C., Schulze K., Shapiro J.A., Shelby R., Taylor L., Treacy D., Weinstein A., Wintersgill M.: The andes physics tutoring system: lessons learned. Artif. Intell. Educ. 15, 147–204 (2005)Google Scholar
  116. Wang, T., Mitrovic, A.: Using neural networks to predict students’ behaviour. In: Kinshuk, Lewis, R., Akahori, K., Kemp, R., Okamoto, T., Henderson, L., Lee, C.-H. (eds.) Proceedings of International Conference on Computers in Education, pp. 969–973 (2002)Google Scholar
  117. Webb N.M., Troper J.D., Fall R.: Constructive activity and learning in collaborative small groups. J. Educ. Psychol. 87, 406–423 (1995)CrossRefGoogle Scholar
  118. Weerasinghe A., Mitrovic A.: Facilitating deep learning through self-explanation in an open-ended domain. Int. J. Knowl.-Based Intell. Eng. Syst. 10(1), 3–19 (2006)Google Scholar
  119. Weerasinghe, A., Mitrovic, A., Martin, B.: A preliminary study of a general model for supporting tutorial dialogues. In: Proceedings of International Conference on Computers in Education, pp. 125–132 (2008)Google Scholar
  120. Weerasinghe A., Mitrovic A., Martin B.: Towards individualized dialogue support for ill-defined domains. Artif. Intell. Educ. 19(4), 357–379 (2009)Google Scholar
  121. Weerasinghe A., Mitrovic A. et al.: Evaluating the effectiveness of adaptive tutorial dialogues in database design. In: Wong, S.L. (eds) Proceedings of 18th International Conference on Computers in Education, pp. 33–40. APSCE, Malaysia (2010)Google Scholar
  122. Woo C.W., Evens M., Freedman R., Glass M., Shim L.S., Zhang Y., Zhou Y., Michael J.: An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning. Artif. Intell. Med. 38(1), 25–46 (2005)CrossRefGoogle Scholar
  123. Woolf, B.P.: Building intelligent interactive tutors: student-centered strategies for revolutionizing E-learning. Morgan Kaufmann, Massachusetts (2009)Google Scholar
  124. Zakharov K., Mitrovic A., Ohlsson S.: Feedback micro-engineering in EER-tutor. In: Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J. (eds) Proceedings of 12th International Conference on Artificial Intelligence in Education, pp. 718–725. IOS Press, Amsterdam (2005)Google Scholar
  125. Zakharov, K., Mitrovic, A., Johnston, L.: Pedagogical agents trying on a caring mentor role. In: Luckin, R., Koedinger, K., Greer, J. (eds.) Proceedings of 13th International Conference on Artificial Intelligence in Education AIED 2007, Los Angeles, pp. 59–66 (2007)Google Scholar
  126. Zakharov K., Mitrovic A., Johnston L. et al.: Towards emotionally-intelligent pedagogical agents. In: Woolf, B. (eds) Proceedings of 9th International Conference on ITS 2008. LCNS, vol. 5091, pp. 19–28. Springer-Verlag, Heidelberg (2008)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Intelligent Computer Tutoring Group, Department of Computer Science and Software EngineeringUniversity of CanterburyChristchurchNew Zealand

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