Abstract

Intelligent Tutoring Systems have made great advances in providing assessment and useful feedback in domains with well-structured problems, where start state, rules, or goals of a problem are well formalized and used to reach an unambiguously correct or incorrect solution. The problems of ill-defined domain often possess multiple solutions. Plausible student solutions of ill-defined problems are deemed wrong by tutoring system if they do not match the known solution accepted by the system. This paper describes a mechanism and the results of a tutoring system in an ill-defined domain such as the English language, for accepting plausible student solutions for ill-defined problems. The WordNet is deployed as a knowledge base, which is a lexical resource of English language database. Semantic similarity measure technique uses WordNet ontology hierarchy to accept the student plausible solutions. The student solutions of cloze passages were evaluated by a group of English experts and compared against a semantic similarity measure. The experts agreed among themselves with a correlation of 0. 7 with p<0. 05. The correlation between semantic similarity and experts is 0. 58 with p<0. 05 to indicate valid hypothesis. The area under the curve of ROC is 0. 76.

References

• Lynch, C. F, Ashley, K. D. , and Aleven, V. , &amp; Pinkwart, N. (2006) Defining ill-defined domains; a literature survey. In V. Aleven, K. Ashley, C. Lynch, &amp; N. Pinkwart (Eds. ), Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at the 8th International Conference on Intelligent Tutoring Systems, pp. 1-10.
• Aleven, V. , Ashley, K. , Lynch, C. and Pinkwart, N. (2008), Preface, Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains, the 9th Conference on ITS.
• Matsuda, N. , and VanLehn, K. (2005) Advanced Geometry Tutor: An intelligent tutor that teaches proof-writing with construction. In C. -K. Looi, G. McCalla, B. Bredeweg &amp; J. Breuker (Eds. ), Proceedings of the 12th International Conference on Artificial Intelligence in Education, pp. 443-450.
• Melis, E. , and Siekmann, J. (2004) ActiveMath: An Intelligent Tutoring System for Mathematics. Seventh International Conference Artificial Intelligence and Soft Computing (ICAISC). In L. Rutkowski, J. Siekmann, R. Tadeusiewicz, L. A. Zadeh (Eds. ), Lecture Notes in Artificial Intelligence, Springer-Verlag, Vol. 3070, pp. 91-101.
• Corbett,A. T. , Koedinger. K. R and Anderson, J. R (1997) Intelligent Tutoring Systems, Handbook of Human-Computer Interaction, Second Completely Revised Edition in M. Helander, T. K. Landauer, P. Prabhu (Eds), Elsevier Science B. V. , Chapter 37.
• Mitrovic, A. (1997) SQL-Tutor: A preliminary report. Technical report TRCOSC 08/97, Computer science department, University of Canterbury.
• Kazi, H. , Haddawy, P. , Suebnukarn, S. Expanding the Space of Plausible Solutions in a Medical Tutoring System for Problem Based Learning. International Journal of Artificial Intelligence in Education 19, 3 (2009), 309-334.
• Fum, D. , Giangrandi, O. and Tasso, C. (1992) The Use of Explanation-Based Learning for Modeling Student Behavior in Foreign Language Tutoring. In M. L. Swartz, M. Yazdani (Eds. ) Intelligent Tutoring Systems for Foreign Language Learning, Berlin: Springer Verlag, pp. 151-170.
• Schuster, E. (1986) The role of native grammars in correcting errors in second language learning, Computational Intelligence, 2, 9398.
• Bos, E. and van de Plassche, J. (1994) A Knowledge-Based, English Verb-Form Tutor. Journal of Artificial Intelligence in Education, Spengels, Vol. 5, No. 1, pp. 107-129.
• Boucher, P. and Danna, F. et Pascale Sebillot (1993) Compounds: an intelligent tutoring system for Learning to Use Compounds in English. Computer Assisted Language Learning (CALL), ISSN 1166-8687, Vol. 6, No. 3, pp. 249-272.
• Mayo, M. , Mitrovic, A. and McKenzie, J. (2000) CAPIT: An intelligent tutoring system for Capitalisation and Punctuation. Proceedings of the International Workshop on Advanced Learning Technologies, pp. 151-154.
• Virvou, M. , Maras, D. , and Tsiriga, V. (2000) Student Modelling in an Intelligent Tutoring System for the Passive Voice of English Language. In EDUCATIONAL TECHNOLOGY &amp; SOCIETY, Journal of International Forum of Educational Technology &amp; Society and IEEE Learning Technology Task Force. , Vol. 4, No, 3, pp. 139-150.
• Collins-Thompson, K. and Callan, J. (2004) Information retrieval for language tutoring: An overview of the REAP project. In Proceedings of the Twenty Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Sheffield, UK.
• Brown, J. and Eskenazi, M. (2004) Retrieval of authentic documents for reader-specific lexical practice. In Proceedings of InSTIL/ICALL Symposium, Venice, Italy.
• Miller, G. A. (1995) WordNet: A Lexical Database for English. Communication of ACM, Vol. 38, No. 11, pp. 39-41.
• Troy Simpson and Thanh Dao, (2005, October 01), "WordNet-based semantic similarity measurement", (The Code Project) Available: http://www. codeproject. com/KB/string/semanticsimilaritywordnet. aspx
• Wu, Z and Palmer, M. (1994) Verb Semantics and Lexical Selection. In Proceedings of the 32nd Annual Meeting of the Associations for computational Linguistics (ACL'94), pp. 133-138.
• "READING #1", (2003, October 07), (INTERLINK Language Centers), Available: http://eslus. com/LESSONS/READING/CLOZE/R1. HTM