The Community for Technology Leaders
RSS Icon
Issue No.04 - Oct.-Dec. (2013 vol.6)
pp: 364-377
Philippe Fournier-Viger , Dept. of Comput. Sci., Univ. of Moncton, Moncton, NB, Canada
Roger Nkambou , Dept. of Comput. Sci., Univ. of Quebec in Montreal, Montreal, QC, Canada
Engelbert Mephu Nguifo , Dept. of Comput. Sci., Univ. Blaise-Pascal, Clermont-Ferrand, France
Andre Mayers , Dept. of Comput. Sci., Univ. of Sherbrooke, Sherbrooke, QC, Canada
Usef Faghihi , Dept. of Comput. Sci., Sull Ross State Univ., Alpine, TX, USA
To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.
Expert systems, Learning systems, Computer aided diagnosis, Problem-solving, Training, Data models,tutoring feedback, Computer-assisted instruction, intelligent tutoring systems, ill-defined domains
Philippe Fournier-Viger, Roger Nkambou, Engelbert Mephu Nguifo, Andre Mayers, Usef Faghihi, "A multiparadigm intelligent tutoring system for robotic arm training", IEEE Transactions on Learning Technologies, vol.6, no. 4, pp. 364-377, Oct.-Dec. 2013, doi:10.1109/TLT.2013.27
[1] B.P. Woolf, Building Intelligent Interactive Tutors: Student Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann, 2009.
[2] C. Lynch, K. Ashley, V. Aleven, and N. Pinkwart, "Defining Ill-Defined Domains: A Literature Survey," Proc. Ill-Defined Domains Workshop, pp. 1-10, 2006.
[3] H.A. Simon, "Information-Processing Theory of Human Problem-Solving," Handbook of Learning and Cognitive Processes, vol. 5, W.K. Estes, ed., John Wiley & Sons, 1978.
[4] V. Aleven, B. McLaren, J. Sewall, and K. Koedinger, "The Cognitive Tutor Authoring Tools (CTAT): Preliminary Evaluation of Efficiency Gains," Proc. Eighth Int'l Conf. Intelligence Tutoring Systems, pp. 61-70, 2006.
[5] K.R. Koedinger, J.R. Anderson, W.H. Hadley, and M.A. Mark, "Intelligent Tutoring Goes to School in the Big City," Int'l J. Artificial Intelligence in Education, vol. 8, pp. 30-43, 1995.
[6] A. Mitrovic, M. Mayo, P. Suraweera, and B. Martin, "Constraint-Based Tutors: A Success Story," Proc. Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (IEA AIE '01), pp. 931-940, 2001.
[7] W. Clancey, "Use of MYCIN's Rules for Tutoring," Rule-Based Expert Systems, B. Buchanan and E.H. Shortliffe, eds., Addison-Wesley, 1984.
[8] A. Graesser, P. Wiemer-Hastings, K. Wiemer-Hastings, D. Harter, and N. Person, "Using Latent Semantic Analysis to Evaluate the Contribution of Students in Autotutor," Interactive Learning Environments, vol. 8, pp. 149-169, 2000.
[9] T. Barnes and J. Stamper, "Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data," Proc. Ninth Int'l Conf. Intelligent Tutoring Systems, pp. 373-382, 2008.
[10] P. Fournier-Viger, R. Nkambou, and E. Mephu Nguifo, "Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator," Handbook of Educational Data Mining, C. Romero et al., eds., pp. 451-465, CRC Press, 2010.
[11] K. Belghith, F. Kabanza, R. Nkambou, and L. Hartman, "An Intelligent Simulator for Tele-Robotics Training," IEEE Int'l J. Trans. Learning Technologies, vol. 5, no. 1, pp. 11-19, Jan.-Mar. 2012.
[12] P. Fournier-Viger, R. Nkambou, and A. Mayers, "Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System," IEEE Trans. Learning Technologies, vol. 1, no. 1, pp. 63-74, Jan.-Mar. 2008.
[13] N. Burgess, "Spatial Memory: How Egocentric and Allocentric Combine," Trends Cognitive Sciences, vol. 10, no. 12, pp. 551-557, 2006.
[14] L. Nadel and O. Hardt, "The Spatial Brain," Neuropsychology, vol. 18, no. 3, pp. 473-476, 2004.
[15] B. Tversky, "Cognitive Maps, Cognitive Collages, and Spatial Mental Models," Proc. Int'l Conf. Spatial Informaton Theory (COSIT '93), pp. 14-24, 1993.
[16] G. Gunzelmann and R.D. Lyon, "Mechanisms for Human Spatial Competence," Proc. Int'l Conf. Spatial Cognition V: Reasoning, Action, Interaction, pp. 288-307, 2006.
[17] V. Aleven, N. Pinkwart, K. Ashley, and C. Lynch, "Supporting Self-Explanation of Argument Transcripts: Specific v. Generic Prompts," Proc. Intelligent Tutoring Systems for Ill-Defined Domains Workshop (ITS '06), 2006.
[18] C. Lynch, K. Ashley, N. Pinkwart, and V. Aleven, Proc. Workshop AIED Applications in Ill-Defined Domains (AIED '07), 2007.
[19] V. Aleven, K. Ashley, C. Lynch, and N. Pinkwart, Proc. ITS for Ill-Defined Domains Workshop (ITS '08), 2008.
[20] P. Fournier-Viger, R. Nkambou, and E. Mephu Nguifo, "Building Intelligent Tutoring Systems for Ill-Defined Domains," Advances in Intelligent Tutoring Systems, R. Nkambou, R. Mizoguchi, and J. Bourdeau, eds., pp. 81-101, Springer, 2010.
[21] A. Mitrovic and A. Weerasinghe, "Revisiting Ill-Definedness and the Consequences for ITSs," Proc. 14th Int'l Conf. Artificial Intelligence in Education, pp. 375-382, 2009.
[22] V. Aleven, "Using Background Knowledge in Case-Based Legal Reasoning: A Computational Model and an Intelligent Learning Environment," Artificial Intelligence, vol. 150, pp. 183-237, 2003.
[23] K.D. Ashley, R. Desai, and J.M. Levine, "Teaching Case-Based Argumentation Concepts Using Dialectic Arguments vs. Didactic Explanations," Proc. Sixth Int'l Conf. Intelligent Tutoring Systems, pp. 585-595, 2002.
[24] E. Walker, A. Ogan, V. Aleven, and C. Jones, "Two Approaches for Providing Adaptive Support for Discussion in an Ill-Defined Domain," Proc. ITS for Ill-Defined Domains Workshop (ITS '08), 2008.
[25] T. Dragon, B.P. Woolf, D. Marshall, and T. Murray, "Coaching within a Domain Independent Inquiry Environment," Proc. Eighth Int'l Conf. Intelligent Tutoring Systems, pp. 144-153, 2006.
[26] R. Hodhod and D. Kudenko, "Interactive Narrative and Intelligent Tutoring for Ethics Domain," Proc. ITS for Ill-Defined Domains Workshop (ITS '08), 2008.
[27] C.-Y. Chou, T.W. Chan, and C.J. Lin, "Redefining the Learning Companion: The Past, Present, and Future of Educational Agents," Computers and Education, vol. 40, pp. 255-269, 2003.
[28] V. Kodaganallur, R. Weitz, and D. Rosenthal, "An Assessment of Constraint-Based Tutors: A Response to Mitrovic and Ohlsson's Critique of "A Comparison of Model-Tracing and Constraint-Based Intelligent Tutoring Paradigms," Int'l J. Artificial Intelligence in Education, vol. 16, pp. 291-321, 2006.
[29] S. Moritz and G. Blank, "Generating and Evaluating Object-Oriented Design for Instructors and Novice Students," Proc. ITS for Ill-Defined Domains Workshop (ITS '08), pp. 35-45, 2008.
[30] J. O'Keefe and L. Nadel, The Hippocampus as a Cognitive Map. Oxford, 1978.
[31] A.D. Ekstrom et al., "Cellular Networks Underlying Human Spatial Navigation," Nature, vol. 425, pp. 184-187, 2003.
[32] J.S. Taube, R.U. Muller, and J.B. Ranck, "Head-Direction Cells Recorded from the Postsubiculum in Freely Moving Rats. 1. Description and Quantitative Analysis," J. Neuroscience, vol. 10, no. 2, pp. 420-435, 1990.
[33] J.S. Taube, R.U. Muller, and J.B. Ranck, "Head-Direction Cells Recorded from the Postsubiculum in Freely Moving Rats. 2. Effects of Environmental Manipulations," J. Neuroscience, vol. 10, no. 2, pp. 436-447, 1990.
[34] A. Terrazas et al., "Self-Motion and the Hippocampal Spatial Metric," J. Neuroscience, vol. 25, pp. 8085-8096, 2005.
[35] L. McNaughton et al., "Path Integration and the Neural basis of the Cognitive Map," Nature Rev. Neuroscience, vol. 7, pp. 663-678, 2006.
[36] D. Carruth et al., "Symbolic Model of Perception in Dynamic 3D Environments," Proc. 25th Army Science Conf., 2006.
[37] M. Harrison and C.D. Schunn, "ACT-R/S: Look Ma, No 'Cognitive Map!'" Proc. Fifth Int'l Conf. Cognitive Modeling, pp. 129-134, 2003.
[38] R.M.J. Byrne and P.N. Johnson-Laird, "Spatial Reasoning," J. Memory and Language, vol. 28, pp. 564-575, 1989.
[39] G. Gunzelmann and R.D. Lyon, "Mechanisms of Human Spatial Competence," Proc. Int'l Conf. Spatial Cognition, 2006.
[40] D. Redish, A.N. Elga, and D.S. Touretzky, "A Coupled Attractor Model of the Rodent Head Direction System," Network: Computation in Neural Systems, vol. 7, no. 4, pp. 671-685, 1996.
[41] J.R. Anderson, Rules of the Mind: Hillsdale. Erlbaum, 1993.
[42] A. Mayers, B. Lefebvre, and C. Frasson, "MIACE: A Human Cognitive Architecture," ACM SIGCUE Outlook, vol. 27, no. 2, pp. 61-77, 2001.
[43] U. Faghihi, P. Fournier-Viger, and R. Nkambou, "CELTS: A Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions," Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends, A.P. Ayala, ed., pp. 339-365, Springer, 2013.
[44] V. Aleven, B. McLaren, J. Sewall, and K. Koedinger, "A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors," Int'l J. Artificial Intelligence in Education, vol. 16, pp. 291-321, 2006.
[45] G. Sanchez and J.C. Latombe, "A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking," Proc. Int'l Symp. Robotics Research (ISRR '01), pp. 403-417, 2003.
[46] M. Likhachev, D. Ferguson, A. Stentz, and S. Thrun, "Anytime Dynamic A∗: An Anytime Replanning Algorithm," Proc. Int'l Conf. Automated Planning and Scheduling, 2005.
[47] N. Le, F. Loll, and N. Pinkwart, "Operationalizing the Continuum between Well-Defined and Ill-Defined Problems for Educational Technology," IEEE Trans. Learning Technologies, vol. 6, no. 3, pp. 258-270, 2013.
[48] D.H. Jonassen, "Instructional Design Models for Well-Structured and Ill-Structured Problem-Solving Learning Outcomes," Educational Technology Research and Development, vol. 45, no. 1, pp. 65-94, 1997.
[49] J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2006.
[50] Y. Hirate and H. Yamana, "Generalized Sequential Pattern Mining with Item Intervals," J. Computers, vol. 1, no. 3, pp. 51-60, 2006.
[51] D. Yuan, K. Lee, H. Cheng, G. Krishna, Z. Li, X. Ma, Y. Zhou, and J. Han, "CISpan: Comprehensive Incremental Mining Algorithms of Closed Sequential Patterns for Multi-Versional Software Mining," Proc. Eighth SIAM Int'l Conf. Data Mining, pp. 84-95, 2008.
[52] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. Int'l Conf. Data Eng., pp. 3-14, 1995.
[53] J. Wang, J. Han, and C. Li, "Frequent Closed Sequence Mining without Candidate Maintenance," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 8, pp. 1042-1056, Aug. 2007.
[54] P. Songram, V. Boonjing, and S. Intakosum, "Closed MultiDimensional Sequential Pattern Mining," Proc. Third Int'l Conf. Information Technology: New Generations, pp. 512-517, 2006.
[55] H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, and U. Dayal, "Multi-Dimensional Sequential Pattern Mining," Proc. 10th Int'l Conf. Information and Knowledge Management, pp. 81-88, 2001.
[56] I. Roll, V. Aleven, and K. Koedinger, "The Invention Lab: Using a Hybrid of Model Tracing and Constraint-Based Modeling to Offer Intelligent Support in Inquiry Environments," Proc. 10th Int'l Conf. Intelligent Tutoring Systems, pp. 115-124, 2010.
[57] V. Dimitrova, G.I. McCalla, and S. Bull, "Open Learner Models: Future Research Directions," Int'l J. Artificial Intelligence in Education, vol. 17, no. 3, pp. 217-226, 2007.
[58] N. Matsuda, W. Cohen, J. Sewall, G. Lacerda, and K. Koedinger, "Performance with SimStudent: Learning Cognitive Skills from Observation," Proc. 13th Int'l Conf. Artificial Intelligence in Education, pp. 467-478, 2007.
[59] P. Suraweera, A. Mitrovic, and B. Martin, "Constraint Authoring System: An Empirical Evaluation," Proc. 13th Int'l Conf. Artificial Intelligence in Education, pp. 451-458, 2007.
94 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool