Automated Ranking of Hints
Authors: Sylvia Encheva
The importance of hints in an intelligent tutoring system is well understood. The problems however related to their delivering are quite a few. In this paper we propose delivering of hints to be based on considering their usefulness. By this we mean that a hint is regarded as useful to a student if the student has succeeded to solve a problem after the hint was suggested to her/him. Methods from the theory of partial orderings are further applied facilitating an automated process of offering individualized advises on how to proceed in order to solve a particular problem.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056939Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1173
 V. Aleven, V., and K. R. Koedinger, Limitations of Student Control: Do Student Know when they need help?, In G. Gauthier, C. Frasson, and K. VanLehn (Eds.), Proceedings of the 5th International Conference on Intelligent Tutoring Systems, ITS 2000 Berlin: Springer Verlag, 2000, 292-303
 R. S. Baker, A. T. Corbett, and K. R. Koedinger, Detecting student misuse of intelligent tutoring systems, Lecture Notes in Computer Science, 3220, Springer-Verlag, Berlin Heidelberg New Jork, 2004, 531-540
 R. S. Baker, A. T. Corbett, I. Roll, I., and K. R. Koedinger, Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 18, 3, 2008, 287-314.
 S. Berg, Condorcet's Jury Theorem and the Reliability of Majority Voting, Group Decision and Negotiation, 5(3), 1996, 229-238
 K. P. Bogart, Some social sciences applications of ordered sets, In: I. Rival, Editor, Ordered Sets, Reidel, Dordrecht 1982, 759 - 787
 J. Breuker, J., Components of Problem Solving and Types of Problems, In 8th European Knowledge Acquisition Workshop, EKAW '94, 1994, 118-136
 A. Brunstein, and J. Krems, Helps and Hints for Learning with Web Based Learning Systems: The Role of Instructions, Intelligent Tutoring Systems, Lecture Notes in Computer Science 3220, 2004, 794-796.
 J. Bull, and C. McKenna, Blueprint for Computer-assisted Assessment, RoutledgeFalmer, 2003.
 J. Bull, and S. Zakrzewski, The mass implementation and evaluation of computer-based assessments. Assessment and Evaluation in Higher Education, 23(2), 1998, 141-152.
 J. Bull, and D. Stephens, The use of question mark software for formative and summat assessment in two universities. Innovations in Education and Training International, 36(2), 1999, 128-136.
 R. Harper, Correcting computer-based assessments for guessing. Journal of Computer Assisted Learning, 19, 2003, 2-8.
 K. R. Koedinger, B. M. McLaren and I. Roll, A help-seeking tutor agent. Proceedings of the Seventh International Conference on Intelligent Tutoring Systems, ITS 2004, Berlin, Springer-Verlag, 2004, 227-239.
 M. Mayo, and A. Mitrovic, Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelli¬gence in Education, 12, 2001, 124-153.
 M. Oliver, J. MacBean, G. Conole, and J. Harvey, Using a Toolkit to Support the Evaluation of Learning. Journal of Computer Assisted Learning, 18(2), 2002, 199-208.
 E. Pecheanu, C. Segal and D. Stefanescu, Content modeling in Intel¬ligent Instructional Environment. Lecture Notes in Artificial Intelligence, 3190, Springer-Verlag, Berlin Heidelberg New Jork, 2003, 1229-1234.
 A. Renld, Learning from worked-out examples: Instructional explana-tions supplement self- explanations. Learning and Instruction, 12, 2002, 529-556.
 S. Schworm, and A. Renkl, Learning by solved example problems: Instructional explanations reduce self-explanation activity. In W. D. Gray and C. D. Schunn (Eds.), Proceeding of the 24th Annual Conference of the Cognitive Science Society, Mahwah, NJ: Erlbaum, 2002, 816-821.
 B. Shih, K. R. Koedinger, R. Scheines, A response time model for bottom-out hints as worked examples. In Baker, R.S.J.d., Barnes, T., and Beck, J.E. (Eds.) Proceedings of the First International Conference on Educational Data Mining, 2008, 117-126.
 D. Tsovaltzi, A. Fiedler, and H. Horacek, A Multi-dimensional Taxonomy for Automating Hinting, Intelligent Tutoring Systems, Lecture Notes in Computer Science 3220, 2004, 772-781.
 J. A. Walonoski, N. T. Heffernan, Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. Intelligent Tutoring Systems, 2006, 722-724.
 D. Wood, Scaffolding, contingent tutoring, and computer-supported learning. International Journal of Artificial Intelligence in Education, 12, 2001, 280-292.
 Y. Zhou, R. Freedman, M. Glass, J. A. Michael, A. Allen, A. A. Rovick, and M. W. Evens, Delivering hints in a dialogue-based intelli¬gent tutoring system, Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, 1999, 128-134.
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