Introduction: Academic failure, as one of the key challenges universities face, has numerous causes. To mitigate it, the impact of these factors must be acknowledged.
Objective: This study aims to propose a predictive model for students’ academic achievement, focusing on variables such as addiction to virtual etworks, procrastination in work, feelings of loneliness, and academic stress.
Materials and Methods: This research is applied and correlational. The statistical population comprised all students from a chosen military university in Tehran in 2023. By employing convenience sampling and G*power software, 386 individuals participated in the study. Data collection involved utilizing questionnaires on internet addiction, loneliness, work procrastination, and student stress, while academic performance was determined using students’ averages. The study variables’ relationships were explored through a suggested conceptual model. Amos24 software was utilized for examining the causal links among research variables using structural equation modeling (SEM).
Results: After modifying the initial measurement model, Composite Reliability index (CR) were obtained greater than the cut-off point of 0.7, and the overall reliability of the model was confirmed. The average variance extracted (AVE) being greater than the cut point of 0.5 and CR being greater than AVE confirmed the convergent validity of the constructs. Divergent validity was also confirmed by the AVE value being higher than the values of the maximum shared squared variance (MSV) and average shared squared variance (ASV). The normed chi-square (CMIN/D˂3), comparative fit index (CFI>0.9), root mean square residual (SRMR˂0.08) and root mean square error of approximate (RMSEA˂0.08) confirmed the fit of the modified model.
Conclusion: Virtual network addiction and procrastination, as independent variables, along with loneliness as a mediating variable, significantly influence academic performance. The findings suggest that the model fits well, with the variables successfully predicting students’ academic performance at an above-average level Keywords: Academic performance, Addiction