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Smartphone has been widely used by the younger generation. However, research exploring the technostress triggered by smartphone use lacks. Based upon the stressor-strain-outcome model, this study examined how smartphone use, especially compulsive use, life invasion, and information overload, cause university students' technostress and, furthermore, how technostress impact their sleep quality and academic performance. Data were collected from 540 undergraduates studying at a Chinese public university and analyzed using structural equation modeling. Results revealed that compulsive smartphone use and information overload are both positively associated with technostress, which in turn have a positive effect on poor sleep quality and academic selfperception. Furthermore, compulsive smartphone use indirectly predicts sleep and academic problems through the mediating effect of technostress. The findings contribute to extend the existing technostress literature and provide valuable practical implications for smartphonerelated designers, university teachers, and students.

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Colleges and universities worldwide have adopted online teaching for short or long periods in response to the COVID-19 outbreak. This study examines the appraisal results of specific techno-stressors related to online instruction and how these appraisal results impact teachers’ continuance intention towards online instruction. The investigation is important because it enables university administrators to manage different techno-stressors distinctively and adopt appropriate coping strategies to support online teaching. A research model is developed based on the transactional theory of stress (TTS) and tested empirically with a sample of 201 university teachers in China. The results reveal that (a) work overload is positively associated with university teachers’ challenge appraisal but negatively associated with their threat appraisal ; (b) invasion of privacy is not significantly associated with challenge or threat appraisal ; (c) work–home conflict is positively associated with threat appraisal but negatively associated with challenge appraisal ; and (d) challenge appraisal is positively associated with continuance intention , while threat appraisal is negatively associated with continuance intention . Implications for online learning during pandemics are discussed.

During the epidemic, online teaching became the mainstream. Online teaching evaluation aims to systematically test teachers' teaching process according to certain teaching objectives and standards, and evaluate its value, advantages and disadvantages, so as to improve the quality of teaching. It is not only an important part of the teaching process, but also the basis of all effective and successful teaching. In this paper, we propose an online teaching evaluation method based on Epistemic Neural Network (ENN), which is an evolutionary intelligence method. In terms of uncertainty modeling, ENN's design innovation provides the improvement effect of geometric progression in terms of statistical quality and calculation cost. Therefore, it is very suitable for teaching evaluation, which is an evaluation process guided by a variety of uncertain factors. Specifically, this paper considers the content and grade standards of online teaching evaluation from five aspects. (1) Teachers' syllabus, teaching progress, teaching plan, courseware and other teaching documents and teaching materials; (2) Abide by teaching discipline, the implementation of teaching plan and the completion of teaching tasks; (3) Teaching attitude, teaching investment, teaching and educating people, and the comprehensive quality of teachers; (4) Whether the concepts taught in the course are accurate, the expression is clear, whether the key points are prominent and whether the difficulties are clearly explained; (5) The depth, breadth and frontier of teaching content, and the amount of classroom information. According to the above five evaluation indexes which involves the big data analysis, we train ENN to get an evaluation score that can evaluate the teacher's online teaching process. In addition, we also test the average evaluation time to verify the effectiveness.