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Issue No.03 - July-Sept. (2013 vol.6)
pp: 258-270
Nguyen-Thinh Le , Dept. of Inf., Clausthal Univ. of Technol., Clausthal-Zellerfeld, Germany
F. Loll , Dept. of Inf., Clausthal Univ. of Technol., Clausthal-Zellerfeld, Germany
N. Pinkwart , Dept. of Inf., Clausthal Univ. of Technol., Clausthal-Zellerfeld, Germany
One of the most effective ways to learn is through problem solving. Recently, researchers have started to develop educational systems which are intended to support solving ill-defined problems. Most researchers agree that there is no sharp distinction but rather a continuum between well-definedness and ill-definedness. However, positioning a problem within this continuum is not always easy, which may lead to difficulties in choosing an appropriate educational technology approach. We propose a classification of the degree of ill-definedness of educational problems based on the existence of solution strategies, the implementation variability for each solution strategy, and the verifiability of solutions. The classification divides educational problems into five classes: 1) one single solution, 2) one solution strategy with different implementation variants, 3) a known number of typical solution strategies, 4) a great variety of solution strategies beyond the anticipation of a teacher where solution correctness can be verified automatically, and 5) problems whose solution correctness cannot be verified automatically. The benefits of this problem classification are twofold. First, it helps researchers choose or develop an appropriate modeling technique for educational systems. Second, it offers the learning technology community a communication means to talk about sorts of more or less ill-defined educational problems more precisely.
Investment, Programming profession, Artificial intelligence, Educational technology, Problem-solving, Cities and towns,adaptive educational technology, classification, Ill-defined domains, ill-defined problems, ITS, CSCL
Nguyen-Thinh Le, F. Loll, N. Pinkwart, "Operationalizing the Continuum between Well-Defined and Ill-Defined Problems for Educational Technology", IEEE Transactions on Learning Technologies, vol.6, no. 3, pp. 258-270, July-Sept. 2013, doi:10.1109/TLT.2013.16
[1] 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, 1997.
[2] K. VanLehn, C. Lynch, K. Schulze, J.A. Shapiro, R. Shelby, L. Taylor, D. Treacy, A. Weinstein, and M. Wintersgill, "The Andes Physics Tutoring System: Lessons Learned," Int'l J. Artificial Intelligence in Education, vol. 15, no. 3, pp. 147-204, 2005.
[3] C. Lynch, K. Ashley, A. Mitrovic, V. Dimitrova, N. Pinkwart, and V. Aleven, Proc. 10th Int'l Workshop on Intelligent Tutoring Systems and Ill-Defined Domains Held at the 10th Int'l Conf. Intelligent Tutoring Systems (ITS '10), 2010.
[4] A. Mitrovic and A. Weerasinghe, "Revisiting Ill-Definedness and the Consequences for ITSs," Proc. 14th Int'l Conf. Artificial Intelligence in Education (AIED '09), pp. 375-382, 2009.
[5] H.A. Simon, "The Structure of Ill Structured Problems," Artificial Intelligence, vol. 4, no. 3, pp. 181-201, 1973.
[6] P. Fournier-Viger, R. Nkambou, and E. Nguifo, "Building Intelligent Tutoring Systems for Ill-Defined Domains," Proc. Advances in Intelligent Tutoring Systems Conf., pp. 81-101, 2010.
[7] C. Lynch, K.D. Ashley, N. Pinkwart, and V. Aleven, "Concepts, Structures, and Goals: Redefining Ill-Definedness," Int'l J. Artificial Intelligence in Education, vol. 19, no. 3, pp. 253-266, 2009.
[8] 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.
[9] M. Chi and K. VanLehn, "Meta-Cognitive Strategy Instruction in Intelligent Tutoring Systems: How, When, and Why," Educational Technology and Soc., vol. 13, pp. 25-39, 2010.
[10] J.-M. Hoc, Cognitive Psychology of Planning. Academic Press, 1988.
[11] M.L. Gick, "Problem-Solving Strategies," Education Psychologist, vol. 21, pp. 99-120, 1986.
[12] S. Ohlsson and N. Bee, "Radical Strategy Variability: A Challenge to Models of Procedural Learning," Proc. Int'l Conf. Learning Science, pp. 351-356, 1991.
[13] J. McCarthy, "The Inversion of Functions Defined by Turing Machines," Automata Studies, 1956.
[14] J. Conklin, "Wicked Problems & Social Complexity," Dialogue Mapping: Building Shared Understanding of Wicked Problems, Wiley, 2005.
[15] P. Suppes, M. Jerman, and D. Brian, Computer Assisted Instruction: Stanford's 1965-66 Arithmetic Program. Academic Press, 1968.
[16] B.P. Woolf, Building Intelligent Interactive Tutors. Morgan Kaufman, 2009.
[17] V. Aleven and K.R. Koedinger, "An Effective Metacognitive Strategy: Learning by Doing and Explaining with a Computer-Based Cognitive Tutor," Cognitive Science, vol. 26, no. 2, pp. 147-179, 2002.
[18] W. Menzel, "Diagnosing Grammatical Faults—A Deep-Modelled Approach," Proc. Third Int'l Conf. AI: Methodology, Systems, Applications, pp. 319-326, 1988.
[19] B. Martin, "Intelligent Tutoring Systems: The Practical Implementation of Constraint-Based Modelling," PhD dissertation, Univ. of Canterbury, 2001.
[20] S. Ohlsson and E. Rees, "The Function of Conceptual Understanding in the Learning of Arithmetic Procedures," J. Cognition and Instruction, vol. 8, no. 2, pp. 103-179, 1991.
[21] S. Ohlsson and A. Mitrovic, "Constraint-Based Knowledge Representation for Individualized Instruction," Computer Science and Information Systems, vol. 3, no. 1, pp. 1-22, 2006.
[22] 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.
[23] N. Matsuda, W.W. Cohen, J. Sewall, G. Lacerda, and K.R. Koedinger, "Evaluating a Simulated Student Using Real Students Data for Training and Testing," Proc. 11th Int'l Conf. User Modeling, pp. 107-116, 2007.
[24] J. Jeuring, A. Gerdes, and B. Heeren, "A Programming Tutor for Haskell," Proc. Central European School on Functional Programming Conf., pp. 1-45, 2011.
[25] N.-T. Le and W. Menzel, "Using Weighted Constraints to Diagnose Errors in Logic Programming—The Case of an Ill-Defined Domain," Int'l J. Artificial Intelligence in Education, vol. 19, no. 4, pp. 381-400, 2009.
[26] H. Fargier and J. Lang, "Uncertainty in Constraint Satisfaction Problems: A Probabilistic Approach," Symbolic and Quantitative Approaches to Reasoning and Uncertainty, vol. 747, pp. 97-104, Springer, 1993.
[27] N.T. Le and N. Pinkwart, "Adding Weights to Constraints in Intelligent Tutoring Systems: Does It Improve the Error Diagnosis?" Proc. Sixth European Conf. Technology Enhanced Learning, pp. 233-247, 2011.
[28] N.-T. Le and N. Pinkwart, "Can Soft Computing Techniques Enhance the Error Diagnosis Accuracy for Intelligent Tutors?" Proc. 11th Int'l Conf. Intelligent Tutoring Systems, pp. 320-329, 2012.
[29] J. Soler, I. Boada, F. Prados, J. Poch, and R. Fabregat, "A Web-Based E-Learning Tool for UML Class Diagrams," Proc. IEEE Education Eng. Conf., pp. 973-979, 2010.
[30] P. Fournier-Viger, R. Nkambou, and E.M. Nguifo, "Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains," Proc. Conf. Artificial Intelligence in Education, pp. 383-390, 2009.
[31] R. Nkambou, P. Fournier-Viger, and E.M. Nguifo, "Learning Task Models in Ill-Defined Domain Using an Hybrid Knowledge Discovery Framework," Knowledge-Based Systems, vol. 24, no. 1, pp. 176-185, 2010.
[32] T. Barnes and J.C. Stamper, "Automatic Hint Generation for Logic Proof Tutoring Using Historical Data," Educational Technology and Soc., vol. 13, no. 1, pp. 3-12, 2010.
[33] S. Gross, X. Zhu, B. Hammer, and N. Pinkwart, "Cluster Based Feedback Provision Strategies in Intelligent Tutoring Systems," Proc. 11th Int'l Conf. Intelligent Tutoring Systems, pp. 699-700, 2012.
[34] F. Loll and N. Pinkwart, "Using Collaborative Filtering Algorithms as Elearning Tools," Proc. 42nd Hawaii Int'l Conf. System Sciences, pp. 1-10, 2009.
[35] K. Cho and C.D. Schunn, "Scaffolded Writing and Rewriting in the Discipline: A Web-Based Reciprocal Peer Review System," Computers and Education, vol. 48, no. 3, pp. 409-426, 2007.
[36] E. Gehringer, "Electronic Peer Review and Peer Grading in Computer-Science Courses," Proc. 32nd Technical Symp. Computer Science Education, pp. 139-143, 2001.
[37] A. Ogan, V. Aleven, and C. Jones, "Advancing Development of Intercultural Competence through Supporting Predictions in Narrative Video," Int'l J. Artificial Intelligence in Education, vol. 19, pp. 267-288, 2009.
[38] O. Scheuer, F. Loll, B.M. McLaren, and N. Pinkwart, "Computer-Supported Argumentation: A Review of the State-of-the-Art," Int'l J. Computer-Supported Collaborative Learning, vol. 5, no. 1, pp. 43-102, 2010.
[39] D.D. Suthers, "Representational Guidance for Collaborative Inquiry," Arguing to Learn, Computer-Support Collaborative Learning Series, J. Andriessen, M. Baker, and D.D. Suthers, eds. vol. 1, pp. 27-46, Springer, 2003.
[40] R. de Groot, R. Drachman, R. Hever, B.B. Schwarz, U. Hoppe, A. Harrer, M. de Laat, R. Wegerif, B.M. McLaren, and B. Baurens, "Computer Supported Moderation of E-Discussions: The ARGUNAUT Approach," Proc. Conf. Computer-Supported Collaborative Learning, pp. 168-170, 2007.
[41] N. Pinkwart, K. Ashley, C. Lynch, and V. Aleven, "Graph Grammars: An ITS Technology for Diagram Representations," Proc. 21st Int'l FLAIRS Conf., pp. 433-438, 2008.
[42] P. Schank and M. Ranney, "Improved Reasoning with Convince Me," Proc. Conf. Companion on Human Factors in Computing Systems (CHI '95), pp. 276-277, 1995.
[43] V. Aleven, K.D. Ashley, and C. Lynch, "Helping Law Students to Understand US Supreme Court Oral Arguments: A Planned Experiment," Proc. 10th Int'l Conf. AI and Law, pp. 55-59, 2005.
[44] I. Bittencourt, E. Costa, B. Fonseca, G. Maia, and I. Calado, "Themis, a Legal Agent-Based ITS," Proc. 13th Int'l Conf. Artificial Intelligence in Education Workshop, pp. 11-20, 2007.
[45] G. Span, "LITES, an Intelligent Tutoring System for Legal Problem Solving in the Domain of Dutch Civil Law," Proc. Fourth Int'l Conf. AI and Law, pp. 76-81, 1993.
[46] G. Vossos, J. Zeleznikow, T. Dillon, and V. Vossos, "An Example of Integrating Legal Case Based Reasoning with Object-Oriented Rule-Based Systems: IKBALS II," Proc. Third Int'l Conf. AI and Law, pp. 31-41, 1991.
[47] F.P. Deek and J. McHugh, "A Survey and Critical Review of Tools for Learning Programming," J. Computer Science Education, vol. 8, no. 2, pp. 130-178, 1999.
[48] A. Corbett and J.R. Anderson, "Student Modeling in an Intelligent Programming Tutor," Cognitive Models and Intelligent Environments for Learning Programming, E. Lemut, B. du Boulay, and G. Dettori, eds., pp. 1-10, Springer, 1993.
[49] M. Gomez-Albarran, "The Teaching and Learning of Programming: A Survey of Supporting Software Tools," Computer J., vol. 48, no. 2, pp. 130-144, 2005.
[50] E.R. Sykes, "Qualitative Evaluation of the Java Intelligent Tutoring System," J. Systemics, Cybernetics and Informatics, vol. 3, no. 5, pp. 49-60, 2005.
[51] N. Baghaei, A. Mitrovic, and W. Irwin, "Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams," Int'l J. Computer-Supported Collaborative Learning, vol. 2, no. 2, pp. 159-190, 2007.
[52] S. Moritz and G. Blank, "Generating and Evaluating Object-Oriented Designs for Instructors and Novice Students," Proc. Nineth Int'l Conf. Intelligent Tutoring Systems Workshop Intelligent Tutoring Systems for Ill-Defined Domains, pp. 35-45, 2008.
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