Abstract
Computerized adaptive testing (CAT) is a method of administering tests that adapts to the examinee’s ability level. Previous research has focused on estimating the examinee’s ability accurately and on providing adequate feedback upon analyzing the examinee’s ability. However, in order for students to use the feedback, they must find courses or learning materials themselves. It is difficult to make customized learning available continuously. Therefore, we used adaptive testing to estimate a student’s ability and to identify a number of student characteristics. This paper recommends content that can reinforce areas in which the student needs improvement. We applied our system at an actual education site. The group that used our recommendation module learned more effectively than the control group. By using this system, teachers will be able to monitor students closely. This enables customized learning which allows students to study effectively without necessitating the effort to search for learning materials. Customized learning will increase interest in.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
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)
Vasileva, T., Trajkovic, V., Davcev, D.: Experimental Data about Knowledge Evaluation in a Distance Learning System. In: IEEE International Conference on IFSA World Congress and 20th NAFIPS, pp. 25–28 (2001)
Okamoto, T.: The Current Situation and Future Directions of Intelligent. IEICE Trans. on Information & System E77-D, 143–161 (1994)
Chang, H.-H., Ying, Z.: Nonlinear sequential designs for logistic item response models with applications to computerized adaptive tests. The Annals of Statistics 37, 1466–1488 (2009)
Gin-Fon, N.J., Alfred, B.: The Implementation of an Adaptive Test on the Computer. In: Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies (ICALT 2005), pp. 822–823 (2005)
Chih-Ming, C., Chao-Yu, L., Mei-Hui, C.: Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Systems with Applications 30, 378–396 (2006)
Feng-Hsu, W.: Application of Componential IRT Model for Diagnostic Test in Standard-conformant eLearning System. In: Proceedings of the Sixth International Conference on Advanced Learning Technologies (ICALT 2006), pp. 237–241 (2006)
Jeff, J., Sridhar, M., Beverly, W.: 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)
Kyparisia, A.P., Maria, G.: An Instructional Framework Supporting Personalized Learning on the Web. In: Proceedings of the The 3rd IEEE International Conference on Advanced Learning Thchnologies (ICALT 2003), pp. 120–124 (2003)
Takahiro, M., Yukuo, I.: 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)
Crocker, L., Algina, J.: Introduction to Classical and Modern Test Theory. Holt, Rinehart and Winston, Inc., New York (1996)
Russell, S., Norvig, P.: Artificial Intelligence-A Modern Approach, 2nd edn. Pearson Education, Inc., Upper Saddle River (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, Y., Cho, J., Han, S., Choi, BU. (2010). A Personalized Assessment System Based on Item Response Theory. In: Luo, X., Spaniol, M., Wang, L., Li, Q., Nejdl, W., Zhang, W. (eds) Advances in Web-Based Learning – ICWL 2010. ICWL 2010. Lecture Notes in Computer Science, vol 6483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17407-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-17407-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17406-3
Online ISBN: 978-3-642-17407-0
eBook Packages: Computer ScienceComputer Science (R0)