article

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

Author:

Antonija MitrovicAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 22, Issue 1-2

Pages 39 - 72

https://doi.org/10.1007/s11257-011-9105-9

Published: 01 April 2012 Publication History

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.

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).

[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).

[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. (ed.) Proceedings of 18th International Conference on Computers in Education, pp. 101-103. APSCE, Putrajaya, Malaysia (2010).

[4]

Anderson, J.R., Boyle, C.F., Corbett, A.T., Lewis, M.W.: Cognitive modeling and intelligent tutoring. Artif. Intell. 42, 7-49 (1990).

Digital Library

[5]

Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. Learn. Sci. 4(2), 167-207 (1995).

[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).

[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).

[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).

[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).

[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).

[11]

Barrow, D., Mitrovic, A., Ohlsson, S., Grimley, M. et al.: Assessing the impact of positive feedback in constraint-based ITSs. In: Woolf, B. (ed.) Proceedings of 9th International Conference on ITS 2008, LCNS 5091, pp. 250-259. Springer-Verlag, Heidelberg (2008).

[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).

[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).

[14]

Blessing, S.B.: Programming by demonstration authoring tool for model-tracing tutors. Artif. Intell. Educ. 8, 233-261 (1997).

[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).

[16]

Brown, J.S., Burton, R.R.: Diagnostic models for procedural bugs in basic mathematical skills. Cogn. Sci. 2, 155-192 (1978).

[17]

Brusilovsky, P., Schwartz, E., Weber, G.: A tool for developing adaptive electronic textbooks on WWW. In: Proceedings of WebNet-96, AACE, (1996).

[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).

[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).

[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).

[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).

[22]

Cook, R., Kay, J.: The Justified User Model. In: Proceedings on UM 1994, pp. 145-150 (1994).

[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).

[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).

Digital Library

[25]

D'Mello, S., Picard, R., Graesser, A.: Toward an affect-sensitive autotutor. IEEE Intell. Syst. 22, 53-61 (2007).

Digital Library

[26]

Desmarais, M., Baker, R.: Learner models' adapting to student skills and behavioral factors. User Model. User-Adapt. Interact. 22 (this issue) (2012).

[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).

[28]

Dimitrova, V.: StyLE-OLM: interactive open learner modelling. Artif. Intell. Educ. 13(1), 35-78 (2003).

Digital Library

[29]

du Boulay, B., Avramides, K., Luckin, R., Martinez-Miron, E., Rebolledo-Mendez, G., Carr, A.: Towards systems that care: a conceptual framework based onmotivation, metacognition and affect. Artif. Intell. Educ., 20(3), 197-229 (2010).

Digital Library

[30]

Duan, D., Mitrovic, A., Churcher, N. et al.: Evaluating the effectiveness of multiple open student models in EER-tutor. In: Wong, S.L. (ed.) Proceedings of 18th International Conference on Computers in Education ICCE 2010, pp. 86-88. APSCE, Putrajaya (2010).

[31]

Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems. Addison-Wesley, Reading (2006).

[32]

Forgas, J.P.: Affect and cognition. Perspect. Psychol. Sci. 3, 94-101 (2008).

[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).

[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).

Digital Library

[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).

Digital Library

[36]

Goleman, D.: Emotional Intelligence. Bantam Books, New York (1995).

[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).

[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. (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).

[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).

[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).

[41]

Jarboe, S: Procedures for enhancing group decision making. In: Hirokawa, B., Poole, M. Communication and Group Decision Making, pp. 345-383. Sage Publications, Thousand Oaks, CA (1996).

[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).

[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).

[44]

Klein, J., Moon, Y., Picard, R.: This computer responds to user frustration: theory, design, and results. Interact. Comput. 14, 119-140 (2002).

[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).

[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).

[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).

[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. (eds.) Proceedings of 17th International Conference on Computers in Education, pp. 51-58. APSCE, Putrajaya (2009).

[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).

[50]

Mabbott, A., Bull, S. et al.: Comparing student-constructed open learner model presentations to the domain. In: Luckin, R. (ed.) Proceedings Artificial Intelligence in Education, pp. 281-288. IOS Press, Chris (2007).

[51]

Major, N., Ainsworth, S., Wood, D.: REDEEM: exploiting symbiosis between psychology and authoring environments. Artif. Intell. Educ. 8(3-4), 317-340 (1997).

[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).

[53]

Martin, B., Mitrovic, A.: The effect of adapting feedback generality in ITSs. In: Wade, V., Ashman, H., Smyth, B. (eds.) Proceedings ofAH2006. LNCS, vol. 4018, pp. 192-202. Springer, Heidelberg (2006).

[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).

[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).

[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. (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).

[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).

[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).

[59]

Mayo, M., Mitrovic, A.: Optimising ITS behaviour with bayesian networks and decision theory. Artif. Intell. Educ. 12(2), 124-153 (2001).

[60]

McManus, M., Aiken, R.: Monitoring computer-based problem solving. Artif. Intell. Educ. 6(4), 307-336 (1995).

Digital Library

[61]

Menzel, W.: Constraint-based modeling and ambiguity. Artif. Intell. Educ. 16(1), 29-63 (2006).

Digital Library

[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).

[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).

[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).

[65]

Mitrovic, A.: Learning SQL with a computerized tutor. In: 29th ACM SIGCSE technical symposium, pp. 307-311 (1998a).

[66]

Mitrovic, A:. A knowledge-based teaching system for SQL. In: Ottmann, T., Tomek, I. Proceedings of ED-MEDIA'98, pp. 1027-1032. AACE, Vancouver, CA (1998b).

[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).

[68]

Mitrovic, A.: An intelligent SQL tutor on the web. Artif. Intell. Educ. 13(2), 173-197 (2003).

Digital Library

[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).

[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).

[71]

Mitrovic, A., Martin, B.: Evaluating the effect of open student models on self-assessment. Artif. Intell. Educ. 17(2), 121-144 (2007).

Digital Library

[72]

Mitrovic, A., Ohlsson, S.: Evaluation of a constraint-based tutor for a database language. Artif. Intell. Educ. 10(3-4), 238-256 (1999).

[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).

[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).

[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).

[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).

[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).

Digital Library

[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).

[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).

Digital Library

[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).

[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. User modeling, adaptation and personalization. LCNS, vol. 6075, pp. 170-195. Springer, Heidelberg (2009).

[82]

Murray, T.: Expanding the knowledge acquisition bottleneck for intelligent tutoring systems. Artif. Intell. Educ. 8(3), 222-232 (1997).

[83]

Murray, T.: Authoring intelligent tutoring systems: an analysis of the state of the art. Artif. Intell. Educ. 10(1), 98-129 (1999).

[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).

[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).

[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. (eds.) Proceedings of 17th International Conference on Computers in Education, pp. 19-26. APSCE, Chris (2009).

[87]

Ohlsson, S.: Constraint-based student modeling. Artif. Intell. Educ. 3(4), 429-447 (1992).

[88]

Ohlsson, S.: Learning from performance errors. Psychol. Rev. 103, 241-262 (1996).

[89]

Ohlsson, S., Bee, N.: Strategy variability: a challenge to models of procedural learning. In: Birnbaum, L. (ed.) Proceedings of International Confernce of the Learning Sciences, pp. 351-356. AACE, Vancouver, CA (1991).

[90]

Ohlsson, S., Mitrovic, A.: Fidelity and efficiency of knowledge representations for intelligent tutoring systems. Technol. Instr. Cogn. Learn. 5(2), 101-132 (2007).

[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 thatWork, pp. 349-356. IOS Press, Amsterdam (2007).

[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).

[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).

[94]

Picard, R.W.: Affective Computing. MIT Press, Cambridge, MA (1997).

[95]

Picard, R.W.: Toward computers that recognize and respond to user emotion. IBM Syst. J. 39(3-4), 705-719 (2010).

[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).

[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).

[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).

[99]

Rosatelli, M., Self, J.: A collaborative case study system for distance learning. Artif. Intell. Educ. 14(1), 1-29 (2004).

[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).

[101]

Shute, V.J.: DNA--Uncorking the bottleneck in knowledge elicitation and organization. In: Proceedings of ITS-98, pp. 146-155 (1998).

[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).

[103]

Soller, A.: Supporting social interaction in an intelligent collaborative learning system. Artif. Intell. Educ. 12, 40-62 (2001).

[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).

[105]

Spohrer, J.C., Soloway, E., Pope, E.: A goal/plan analysis of buggy pascal programs. Hum.-Comput. Interact. 1, 163-205 (1985).

Digital Library

[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).

[107]

Suraweera, P., Mitrovic, A.: An intelligent tutoring system for entity relationship modelling. Artif. Intell. Educ. 14(3-4), 375-417 (2004).

Digital Library

[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 IntelligentTutoring Systems ITS2004. LNCS, vol. 3220, pp. 207-216. Springer-Verlag,Maceio (2004).

[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).

[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).

[111]

Suraweera, P., Mitrovic, A., Martin, B.: Widening the knowledge acquisition bottleneck for constraint-based tutors. Artif. Intell. Educ. 20(2), 137-173 (2010).

Digital Library

[112]

Tecuci, G.: Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. Morgan Kaufmann, San Francisco (1998).

[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).

[114]

VanLehn, K.: The behaviour of tutoring systems. Artif. Intell. Educ. 16(3), 227-265 (2006).

Digital Library

[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).

Digital Library

[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).

[117]

Webb, N.M., Troper, J.D., Fall, R.: Constructive activity and learning in collaborative small groups. J. Educ. Psychol. 87, 406-423 (1995).

[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).

Digital Library

[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).

[120]

Weerasinghe, A., Mitrovic, A., Martin, B.: Towards individualized dialogue support for ill-defined domains. Artif. Intell. Educ. 19(4), 357-379 (2009).

Digital Library

[121]

Weerasinghe, A., Mitrovic, A. et al.: Evaluating the effectiveness of adaptive tutorial dialogues in database design. In: Wong, S.L. (ed.) Proceedings of 18th International Conference on Computers in Education, pp. 33-40. APSCE, Malaysia (2010).

[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).

[123]

Woolf, B.P.: Building intelligent interactive tutors: student-centered strategies for revolutionizing E-learning. Morgan Kaufmann, Massachusetts (2009).

[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).

[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).

[126]

Zakharov, K., Mitrovic, A., Johnston, L. et al.: Towards emotionally-intelligent pedagogical agents. In: Woolf, B. (ed.) Proceedings of 9th International Conference on ITS 2008. LCNS, vol. 5091, pp. 19-28. Springer-Verlag, Heidelberg (2008).

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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 22, Issue 1-2

April 2012

216 pages

ISSN:0924-1868

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Copyright © Copyright © 2012 Springer Science+Business Media B.V.

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Kluwer Academic Publishers

United States

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Published: 01 April 2012

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https://dl.acm.org/doi/10.1145/3510456.3514159
Evans ALiu JWang ZZheng M(2022)Designing a Supportive IDE to Help Novices Recognise and Recover from Programming MisconceptionsProceedings of the 2022 ACM Conference on International Computing Education Research - Volume 210.1145/3501709.3544277(30-31)Online publication date: 7-Aug-2022
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