Abstract
In this paper, we propose Computerized Adaptive Testing (CAT) method based on deep learning, which is improved in some aspects. First, training samples used for Model-GRU is generated by monte carlo simulation, as a data-driven method. Second, comparing with time consuming conventional methods, the proposed deep learning based methods can greatly reduce necessary time to evaluate ability after finishing training. Third, our model can notice the embedded relationships among the performances for neighboring items, thus human’s memory effect during testing can be considered. In the implementation of CAT, the recently developed Gated Recurrent Unit (GRU) is applied. Testing results have shown that the proposed model can response more quickly and accurately compared with the conventional CAT. The findings offer a new way to study the student’s ability through testing.
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Acknowledgment
We would like to acknowledge support in part from the National Natural Science Foundation of China under Grants 61233001, 71232006, 61533019 and Science and Technology Innovation Program of Chinese Academy of Sciences (CAS).
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Wang, X., Zhang, Y., Yu, S., Liu, X., Wang, FY. (2018). Computerized Adaptive English Ability Assessment Based on Deep Learning. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_13
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