Abstract
Recent developments in content processing technology and the widespread diffusion of wired and wireless Internet mean that users can now learn by means of a computer, anytime and anywhere. English learning that involves multimedia content can increase the interest of learners and lead to the development of their communication ability. Although using computers to teach English in a conventional educational environment provides motivation and effective learning on the part of the students, the method still has problems, which include the provision of learning materials without consideration of teaching methods, and evaluation without provision for differences in individual student levels. This paper introduces the Intelligent Tutoring System (ITS) for English learning, using web-based technologies. Using this system, the above problems are solved at the same time as the benefits of computer-based learning are retained. Its design and implementation are based on an intelligent tutoring system that provides content suitable for specific levels of ability. We used the contents of the Korean Elementary school 300-certification program for English conversation and an estimation of students’ abilities using Item Response Theory (IRT) to evaluate the proposed system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Davidovic, A., Warren, J., Trichina, E.: Learning Benefits of Structural Example-Based Adaptive Tutoring Systems. IEEE Trans. on Education 46, 241–251 (2003)
Lee, Y., et al.: An Intelligent Tutoring System Based on a Multi-modal Environment for the 300-Certification Program of English Conversation. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 778–780. Springer, Heidelberg (2006)
Lee, Y., et al.: A Personalized Assessment System Based on Item Response Theory. In: Luo, X., Spaniol, M., Wang, L., Li, Q., Nejdl, W., Zhang, W. (eds.) ICWL 2010. LNCS, vol. 6483, pp. 381–386. Springer, Heidelberg (2010)
Murase, T., Isomoto, Y.: e-Learnig System to Provide Optimum Questions Based on Item Response Theory. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 695–697. Springer, Heidelberg (2006)
Okamoto, T.: The Current Situation and Future Directions of Intelligent. IEICE Trans. on Information & System E77-D, 143–161 (1994)
Gin-Fon, N.J., Alfred, B.: The Implementation of an Adaptive Test on the Computer. In: Proceedings of the Fifth IEEE International Confernce on Advanced Learning Technologies (ICALT 2005), pp. 822–823 (2005)
Feng-Hsu, W.: Application of Componential IRT Model for Diagnostic Test in Standard-conformant eLearning System. In: Proceedings of the Sixth International Confernce on Advanced Learning Technologies (ICALT 2006), pp. 237–241 (2006)
Johns, J., Mahadevan, S., Park Woolf, B.: Estimating Student Proficiency Using an Item Response Theory Model. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 473–480. Springer, Heidelberg (2006)
Crocker, L., Algina, J.: Introduction to Classical and Modern Test Theory. Rinehart and Winston, Inc., New York (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, Y., Cho, J., Choi, BU. (2011). The Method of Generating Assessment for Intelligent Tutoring System. In: Kim, Th., et al. Grid and Distributed Computing. GDC 2011. Communications in Computer and Information Science, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27180-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-27180-9_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27179-3
Online ISBN: 978-3-642-27180-9
eBook Packages: Computer ScienceComputer Science (R0)