Abstract
This work presents a model for student knowledge diagnosis that can be used in ITSs for student model update. The diagnosis is accomplished through Computerized Adaptive Testing (CAT). CATs are assessment tools with theoretical background. They use an underlying psychometric theory, the Item Response Theory (IRT), for question selection, student knowledge estimation and test finalization. In principle, CATs are only able to assess one topic for each test. IRT models used in CATs are dichotomous, that is, questions are only scored as correct or incorrect. However, our model can be used to simultaneously assess multiple topics through content-balanced tests. In addition, we have included a polytomous IRT model, where answers can be given partial credit. Therefore, this polytomous model is able to obtain more information from student answers than the dichotomous ones. Our model has been evaluated through a study carried out with simulated students, showing that it provides accurate estimations with a reduced number of questions.
This work has been partially financed by LEActiveMath project, funded under FP6 (Contr. No507826). The author is solely responsible for its content, it does not represent the opinion of the EC, and the EC is not responsible for any use that might be made of data appearing therein.
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References
Birnbaum, A.: Some Latent Trait Models and Their Use in Inferring an Examinee’s Mental Ability. In: Lord, F.M., Novick, M.R. (eds.) Statistical Theories of Mental Test Scores, Addison-Wesley, Reading (1968)
Conejo, R., Guzmán, E., Millán, E., Pérez-de-la-Cruz, J.L., Trella, M.: SIETTE: A web-based tool for adaptive testing. International Journal of Artificial Intelligence in Education (forthcoming)
Dodd, B.G., DeAyala, R.J., Koch, W.R.: Computerized Adaptive Testing with Polytomous Items. Applied Psychological Measurement 9(1), 5–22 (1995)
Guzmán, E., Conejo, R.: Simultaneous evaluation of multiple topics in SIETTE. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 739–748. Springer, Heidelberg (2002)
Guzmán, E., Conejo, R.: A library of templates for exercise construction in an adaptive assessment system. Technology, Instruction, Cognition and Learning (TICL) (forthcoming)
Huang, S.X.: A Content-Balanced Adaptive Testing Algorithm for Computer-Based Training Systems. In: Lesgold, A.M., Frasson, C., Gauthier, G. (eds.) ITS 1996. LNCS, vol. 1086, pp. 306–314. Springer, Heidelberg (1996)
Lord, F.M.: Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates, Hillsdale (1980)
McCalla, G.I., Greer, J.E.: Granularity-Based Reasoning and Belief Revision in Student Models. In: Greer, J.E., McCalla, G. (eds.) Student Modeling: The Key to Individualized Knowledge-Based Instruction, vol. 125, pp. 39–62. Springer, Heidelberg (1994)
Owen, R.J.: A Bayesian Sequential Procedure for Quantal Response in the Context of Adaptive Mental Testing. Journal of the American Statistical Association 70(350), 351–371 (1975)
Thissen, D., Steinberg, L.: A Response Model for Multiple Choice Items. In: Van der Linden, W.J., Hambleton, R.K. (eds.) Handbook of Modern Item Response Theory, pp. 51–65. Springer, New York (1997)
van der Linden, W.J., Glas, C.A.W.: Computerized Adaptive Testing: Theory and Practice. Kluwer Academic Publishers, Netherlands (2000)
VanLehn, K., Ohlsson, S., Nason, R.: Applications of Simulated Students: An Exploration. Journal of Artificial Intelligence and Education 5(2), 135–175 (1995)
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Guzmán, E., Conejo, R. (2004). A Model for Student Knowledge Diagnosis Through Adaptive Testing. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_2
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DOI: https://doi.org/10.1007/978-3-540-30139-4_2
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