Predicting Academic Adjustment Based on Digital Emotion Regulation and Cognitive Fatigue Caused by Online Learning
Keywords:
digital emotion regulation, cognitive fatigue, online learning, academic adjustment, cognitive reappraisalAbstract
Objective: This study aimed to predict students’ academic adjustment based on digital emotion regulation and cognitive fatigue resulting from online learning.
Methods and Materials: This applied study employed a descriptive–correlational and predictive design. The statistical population consisted of female upper secondary school students in Ashkhaneh during the 2023–2024 academic year, from whom 294 participants were selected using multistage cluster sampling. Research instruments included the Academic Adjustment Scale, the Digital Emotion Regulation Questionnaire (assessing cognitive reappraisal and expressive suppression), and the Zoom Exhaustion and Fatigue Scale. Data were analyzed using Pearson correlation coefficients and simultaneous multiple regression analysis with SPSS version 26.
Findings: The results indicated that online cognitive fatigue was the strongest negative predictor of academic adjustment (β = −0.58, p < 0.001), whereas digital cognitive reappraisal emerged as the strongest positive predictor (β = 0.38, p < 0.001). Expressive suppression also negatively predicted academic adjustment, although with a smaller effect size (β = −0.13, p < 0.05). Overall, the regression model explained 55% of the variance in academic adjustment (R² = 0.55).
Conclusion: The findings suggest that in online learning contexts, cognitive fatigue substantially undermines students’ academic adjustment, while effective digital emotion regulation—particularly cognitive reappraisal—serves a protective and facilitative role; thus, implementing interventions focused on digital emotion regulation skills and cognitive fatigue management may enhance students’ academic adjustment in virtual learning environments.
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