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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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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