In this study, we aimed to explore the factors associated with higher education students' learning outcomes in emergency remote teaching environments (ERTEs) during the COVID-19 pandemic at both the population and individual levels, given the limited understanding in previous research. 9418 students from 41 countries were selected for analysis from a survey-based dataset that was collected with the aim of understanding the self-perceived impacts of the first-wave COVID-19 pandemic on higher education students. We conducted structural equation modeling to explore associated factors and latent profile analysis to identify student profiles based on these factors. Utilizing the identified profiles, we developed a random forest-based classifier to identify the membership of students' profiles. The results showed that six environmental and individual factors--partially mediated by academic emotions--were significantly associated with learning outcomes. The positively associated factors, ranked by path coefficient, were satisfaction with synchronous course organization (SCO), computer skills (CS), satisfaction with asynchronous course organization (ACO), satisfaction with administration support (AS), and perceived teacher support (TS). The negatively associated factor was worry about life. Based on these factors, eight profiles were identified with varying learning outcomes and student characteristics. The classifier achieved a testing accuracy of 0.904. By integrating variable-centered and person-centered approaches, this study bridges the gap in understandings of general patterns and individual differences regarding key factors associated with higher education students' learning outcomes. The findings have implications for designing individualized interventions and support strategies to enhance student learning outcomes and mitigate educational disparities in ERTEs during crisis situations.