Evaluating the immediate and delayed effects of psychological need thwarting of online teaching on Chinese primary and middle school teachers' psychological well-being.

Recent studies on the effects of mandatory online teaching, resulting from the COVID-19 pandemic, have widely reported low levels of satisfaction, unwillingness to continue online teaching, and negative impacts on the psychological well-being of teachers. Emerging research has highlighted the potential role of psychological need thwarting (PNT), in terms of autonomy, competence, and relatedness thwarting, resulting from online teaching. The aim of this study was to evaluate the immediate and delayed (longitudinal) effects of PNT of online teaching on teachers' well-being (including distress and burnout), intention to continue online teaching, and job satisfaction. Moreover, data collected from both cross-sectional and longitudinal surveys allowed for a systematic validation of an important instrument in the field of teacher psychology, the Psychological Need Thwarting Scale of Online Teaching (PNTSOT), in terms of longitudinal reliability and validity. The data reveal the usefulness of the construct of PNT in terms predicting and explaining teachers' willingness to continue using online teaching as well as the degree of burnout after a period of 2 months, such that PNT is positively associated with burnout and negatively associated with willingness to continue online teaching. As such, the PNTSOT is recommended for future research evaluating the long-term psychological, affective, and intentional outcomes stemming from teachers' PNT. Moreover, based on our findings that the impact from PNT of online teaching is persistent and long-term, we suggest that school leaders provide flexible and sustained professional development, model respectful and adaptive leadership, and create opportunities for mastery for the development of community of practice that can mitigate the thwarting of teachers' autonomy, competence, and relatedness during times of uncertainty. Additionally, in terms of the psychometric properties of the PNTSOT instrument, our empirical findings demonstrate internal reliability, test-retest reliability, measurement invariance, and criterion validity (concurrent and predictive) based on cross-sectional and longitudinal data.

The Effect of Primary and Middle School Teachers' Problematic Internet Use and Fear of COVID-19 on Psychological Need Thwarting of Online Teaching and Psychological Distress.

Problematic Internet use (PIU) is a risk factor for psychological distress during COVID-19, as teachers are a psychologically vulnerable population. We explored the role of PIU in terms of primary and middle school teachers' fear of COVID-19 and psychological need thwarting (PNT) of online teaching. We empirically evaluated the relationships among these research variables in explaining teachers' psychological distress during COVID-19. Online survey data were collected from 9030 teachers. A high proportion of participants demonstrated psychological distress: depression (20.4%), anxiety (26.4%), and stress (10.2%). Structural equation modeling was used to test our proposed conceptual model, wherein PIU behaviors served as predictors, mediated by fear of COVID-19 and PNT of online teaching, for teachers' psychological distress. With ideal model fit, the results of the path coefficients indicated that PIU behaviors were associated with fear of COVID-19 (p < 0.001); fear of COVID-19 and PNT of online teaching were associated with psychological distress (p < 0.001); and fear of COVID-19 was also positively associated with PNT of online teaching (p < 0.001). PSU and PSMU had an indirect positive effect on psychological distress through the mediator of fear of COVID-19 and PNT of online teaching. As such, we suggest that school administrators pay greater attention to teachers' psychological needs through efforts to enhance teachers' autonomy and relatedness from interpersonal relationships, alleviating PNT of online teaching. Our PNT of online teaching scale may also serve as a contribution for further research and practice.

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.