USE OF RECURRENT NEURAL NETWORK ARCHITECTURES FOR DATA VERIFICATION IN THE SYSTEM OF DISTANCE EDUCATION

There are very few examples of the use of various architectures for recurrent neural networks to predict student learning outcomes. In fact, the only architecture used to solve this problem is the LSTM architecture. In the works devoted to the use of LSTM to predict educational outcomes, the results of a detailed theoretical substantiation of the preference of this particular architecture of the RNN are not presented. In this regard, it seems advisable to provide such justification in the framework of this study. The main property of input data for prediction of educational outcomes is its temporary nature. Some sequence of user actions unfolds in time and is evaluated (classified) by an external observer as evidence of the presence or absence of an educational result (objective or metaobjective). In this regard, the RNN used to classify user actions should perform a procedure for adjusting the weights of neurons for a certain set of states in the past. At the same time, the length of the sequence of these states is not predetermined: it can be both short (for example, for objective results), and quite long.