Development of a Neural Network for modeling the behavior of the Educational Process

Abstract

Identifying high-risk students as early as possible plays an important role in improving the quality of education. To do this, most of the existing research used traditional machine learning algorithms to predict student achievement based on their behavioral data, from which behavioral features were manually extracted using the experience and knowledge of experts. However, due to the increase in diversity and the overall volume of behavioral data, it is becoming increasingly difficult to identify high-quality handcrafted items. In this article, the authors propose an end-to-end deep learning model that automatically extracts features from heterogeneous student behavior data from multiple sources to predict academic achievement. The key innovation of this model is that it uses long-short-term memory networks to capture the inherent characteristics of the time series for each behavior, and it also uses 2D convolutional networks to extract correlation features between different behaviors. The authors carried out experiments with four types of data on the daily behavior of RTU MIREA students. The experimental results demonstrated that the proposed deep model method outperforms several machine learning algorithms (by about 5 times).