[2R1-OS-10c] AI諸技術の発展に基づく学びのモデルの高度化と展望

[2R1-OS-10c-04] Linking Method for Writing Tests using Item Response Theory and Neural Automated Scoring Technology

〇Kota Aramaki¹, Masaki Uto¹ (1. The University of Electro-Communications)

Keywords:Item Response Theory, Deep Learning, Writing Tests

A difficulty of writing tests is that given scores depend on rater characteristics, such as severity and consistency. To resolve this problem, the generalized many-facet Rasch model (GMFRM), an item response theory model that considers such rater characteristics, has been proposed. When applying such an IRT model to datasets comprising results of multiple writing tests administered to different examinees, test linking is needed to unify the scale for model parameters estimated from individual test results. In test linking, test administrators generally need to design multiple tests such that examinees or raters partially overlap. However, preparing common examinees and raters is often difficult in actual testing environments. Therefore, in this study, we propose a novel method to link the results of the writing tests, which are obtained by applying GMFRM, using recent neural automatic scoring technology. Experimental results show that our method realizes the test linking without common examinees and raters.