Abstract: The COVID-19 epidemic has caused great impact on the entire society, and the spread of novel coronavirus has brought a lot of inconvenience to the education industry. To ensure the sustainability of education, distance education plays a significant role. During the process of distance education, it is necessary to examine the learning situation of students. This study proposes an academic early warning model based on long- and short-term memory (LSTM), which firstly extracts and classifies students’ behavior data, and then uses the optimized LSTM to establish an academic early warning model. The precision rate of the optimized LSTM algorithm is 0.929, the recall rate is 0.917 and the F value is 0.923, showing a higher degree of convergence than the basic LSTM algorithm. In the actual case analysis, the accuracy rate of the academic early warning system is 92.5%. The LSTM neural network shows high performance after parameter optimization, and the academic early warning model based on LSTM also has high accuracy in the actual case analysis, which proves the feasibility of the established academic early warning model.

Yi Qu, Zhiyuan Sun and Libin Liu, “Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131074

@article{Qu2022,
title = {Research on the Academic Early Warning Model of Distance Education based on Student Behavior Data in the Context of COVID-19},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131074},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131074},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Yi Qu and Zhiyuan Sun and Libin Liu}
}