Abstract: The paper examines the issues of the use of adaptive testing systems in terms of their incorporation in artificial neural network modules designed to solve the problem of choosing the next question, thereby forming an individual testing trajectory. The study presents an analysis of data affecting the quality of problem-solving, proposes a general modular structure of a system, and describes the main data flows at the input of an artificial neural network. The solution proposed for the problem of choosing the difficulty of the question is to use feedforward neural networks. Different architectures and parameters of training artificial neural networks (weight update mechanisms, loss functions, the number of training epochs, batch sizes) are compared. As an alternative, the option of using recurrent long-short term memory networks is considered.

Ekaterina Vitalevna Chumakova, Tatiana Alexandrovna Chernova, Yulia Aleksandrovna Belyaeva, Dmitry Gennadievich Korneev and Mikhail Samuilovich Gasparian, “Use of Neural Networks in the Adaptive Testing System” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130504

@article{Chumakova2022,
title = {Use of Neural Networks in the Adaptive Testing System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130504},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130504},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Ekaterina Vitalevna Chumakova and Tatiana Alexandrovna Chernova and Yulia Aleksandrovna Belyaeva and Dmitry Gennadievich Korneev and Mikhail Samuilovich Gasparian}
}