## 5 Conclusions
The scale's structure is well-suited to evaluating teaching practices in virtual contexts, providing a robust framework for future research and assessments in this domain. 2. What are the psychometric properties (construct validity and internal reliability) of the Student Academic Stress Scale in Virtual Education (SASSEV), and how suitable is its structure for evaluating academic stress in virtual education? The SASSEV also exhibited high internal reliability (\(\alpha\) = 0.879) and significant construct validity. This instrument effectively captured essential dimensions of academic stress, such as overload, environmental distractions, and adequacy of digital resources. The scale is validated as a comprehensive tool for assessing the factors contributing to academic stress in virtual environments, facilitating the identification of critical areas for intervention. 3. What is the relationship between digital teaching practices and academic stress among university students in virtual learning environments, and what are the key factors influencing this dynamic according to a theoretical model validated through structural equation modeling? The validated structural equation model (SEM) revealed a significant inverse relationship between digital teaching practices and academic stress. Factors such as high-quality teacher interaction, technology-mediated communication, and teacher support have a positively impact in reducing academic stress. However, this relationship is moderated by mediators such as academic overload, environmental distractions, and insufficient digital resources. The findings emphasize that well-designed teaching practices aimed at personalizing learning and minimizing technological barriers are essential for mitigating academic stress and improving student well-being. The findings provide a solid theoretical framework to guide future research. Further studies should investigate contextual and sociodemographic differences that may influence the relationship between digital teaching practices and academic stress, as well as evaluate specific interventions, such as emotional support programs and personalized pedagogical strategies. From a practical perspective, educational institutions can design inclusive policies that not only optimize virtual environments but also strengthen the resilience, self-regulated learning, and the overall well-being of students. Building on the established framework, future research could explore innovative directions to deepen the understanding of this relationship. One promising avenue is the integration of advanced learning analytics to identify patterns of academic stress in real-time and provide adaptive interventions. Additionally, longitudinal studies could investigate the long-term effects of digital teaching practices on academic stress and student well-being across diverse educational contexts. Exploring the role of emerging technologies, such as virtual reality (VR) and artificial intelligence (AI), in creating immersive and supportive learning environments is another potential area of study. Finally, interdisciplinary research that combines educational psychology, technology design, and policy development could yield holistic strategies to further enhance the effectiveness of virtual education while minimizing its adverse impacts on students. R.P.N.-F.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, supervision, project administration. A.D.-A. : conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, supervision. H.M.R.-O. : conceptualization, methodology, investigation, writing--original draft preparation. E.D.N.-F.: conceptualization, methodology, investigation, writing--original draft preparation.