The COVID-19 pandemic has led to an abrupt transition from face-to-face learning to online learning, which has also affected the mental health of college students. In this study, we examined the relationship between students adjustment to online learning and their mental health by using the Dual-Continua Model. The model assumes that mental disorder and mental well-being are related yet distinct factors of mental health. For this purpose, 2,933 college students completed an online survey around the beginning of the Fall semester of 2020 (N = 1,724) and the Spring semester of 2021 (N = 1,209). We assessed participants mental well-being, mental disorders, and academic distress by means of the online survey. In addition, we incorporated grades and log data accumulated in the Learning Management System (LMS) as objective learning indicators of academic achievement and engagement in online learning. Results revealed that two dimensions of mental health (i.e., mental well-being and mental disorder) were independently associated with all objective and subjective online learning indicators. Specifically, languishing (i.e., low levels of mental well-being) was negatively associated with student engagement derived from LMS log data and academic achievement and was positively associated with self-reported academic distress even after we controlled for the effects of mental disorder. In addition, mental disorder was negatively related to student engagement and academic achievement and was positively related to academic distress even after we controlled for the effects of mental well-being. These results remained notable even when we controlled for the effects of sociodemographic variables. Our findings imply that applying the Dual-Continua Model contributes to a better understanding of the relationship between college students mental health and their adaptation to online learning. We suggest that it is imperative to implement university-wide interventions that promote mental well-being and alleviate psychological symptoms for students successful adjustment to online learning.

Photovoltaic devices based on hybrid perovskite materials have exceeded 22% efficiency due to high charge-carrier mobilities and lifetimes. Properties such as photocurrent generation and open-circuit voltage are influenced by the microscopic structure and orientation of the perovskite crystals, but are difficult to quantify on the intra-grain length scale and are often treated as homogeneous within the active layer. Here, we map the local short-circuit photocurrent, open-circuit photovoltage, and dark drift current in state-of-the-art methylammonium lead iodide solar cells using photoconductive atomic force microscopy. We find, within individual grains, spatially correlated heterogeneity in short-circuit current and open-circuit voltage up to 0.6 V. These variations are related to different crystal facets and have a direct impact on the macroscopic power conversion efficiency. We attribute this heterogeneity to a facet-dependent density of trap states. These results imply that controlling crystal grain and facet orientation will enable a systematic optimization of polycrystalline and single-crystal devices for photovoltaic and lighting applications.