Article: Simulating academic stress formation via causal discovery and temporal sequence analysis Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.55 pp.78 - 96 Abstract: Academic pressure has a significant impact on students' physical and mental health and academic performance. Understanding its dynamic formation mechanism is crucial for effective intervention. This paper proposes an innovative framework that combines causal discovery with time series modelling to simulate the development path of academic pressure. The framework first uses the systematic causal discovery algorithm to learn the directional acyclic diagram to present the causal correlation between key factors; then builds a causal constraint time series model to simulate the dynamic evolution process of student pressure. Based on the comprehensive longitudinal data evaluation of 300 students, compared with the traditional benchmark model, this model notably increases the average accuracy of pressure precursor recognition by 7.4% and effectively reduces the trajectory simulation error by 28.4%. This research finding provides operational insights for early warning of stress and personalised intervention strategies. Inderscience Publishers - linking academia, business and industry through research

Title: Simulating academic stress formation via causal discovery and temporal sequence analysis

Authors: Fu Yao; Mengting Yan

Addresses: Jingchu University of Technology, Jingmen, 448000, China ' Jingchu University of Technology, Jingmen, 448000, China

Abstract: Academic pressure has a significant impact on students' physical and mental health and academic performance. Understanding its dynamic formation mechanism is crucial for effective intervention. This paper proposes an innovative framework that combines causal discovery with time series modelling to simulate the development path of academic pressure. The framework first uses the systematic causal discovery algorithm to learn the directional acyclic diagram to present the causal correlation between key factors; then builds a causal constraint time series model to simulate the dynamic evolution process of student pressure. Based on the comprehensive longitudinal data evaluation of 300 students, compared with the traditional benchmark model, this model notably increases the average accuracy of pressure precursor recognition by 7.4% and effectively reduces the trajectory simulation error by 28.4%. This research finding provides operational insights for early warning of stress and personalised intervention strategies.

Keywords: academic pressure; simulation; causal discovery; time series modelling; oriented acyclic diagram.

DOI: 10.1504/IJICT.2026.153795

International Journal of Information and Communication Technology, 2026 Vol.27 No.55, pp.78 - 96

Received: 22 Jan 2026
Accepted: 01 Mar 2026
Published online: 26 May 2026 *

Title: Simulating academic stress formation via causal discovery and temporal sequence analysis

Keep up-to-date