Online addiction analysis and identification of students by applying gd-LSTM algorithm to educational behaviour data

Abstract

Internet has become the primary source of extracurricular entertainment for college students in today’s information age of Internet entertainment. However, excessive Internet addiction (IA) can negatively impact a student’s daily life and academic performance. This study used Stochastic models to gather data on campus education behaviour, extract the temporal characteristics of university students’ behaviour, and build a Stochastic dropout long short-term memory (LSTM) network by fusing Dropout and LSTM algorithms in order to identify and analyse the degree of IA among university students. The model is then used to locate and forecast the multidimensional vectors gathered, and finally to locate and evaluate the extent of university students’ Internet addiction. According to the experiment’s findings, there were 4.23% Internet-dependent students among the overall (5,861 university students), and 95.66% of those students were male. The study examined the model using four dimensions, and the experimental findings revealed that the predictive model suggested in the study had much superior predictive performance than other models, scoring 0.73, 0.72, 0.74, and 0.74 on each dimension, respectively. The prediction model outperformed other algorithms overall and in the evaluation of the four dimensions, performing more evenly than other algorithms in the performance comparison test with other similar models. This demonstrated the superiority of the research model.

Keywords

• lstm
• dropout algorithm
• internet addiction identification
• stochastic models
• time-series features