This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Even in the late stages of the COVID-19, the physical and psychological trauma caused by the epidemic continues to affect people, particularly university students, whose physical and psychological health is vulnerable to environmental influences. The purpose of this article is to investigate the relationship between learning adaptability and “state” anxiety among university students enrolled during the COVID-19(2020-2022), as well as the role of self-management in mediating this process. The findings reveal a negative association between college students' academic adjustment and their state anxiety, a process that also includes a mediation role for self-management, with subjects in this research being college students enrolled during COVID-19. This study offers a theoretical foundation for investigating the factors influencing anxiety from an operationalized viewpoint, as well as for further effective regulation of university students' mental health and anxiety reduction.

Leadership and personality traits of leaders always remained a hot debate among researchers and practitioners. However, there is still limited literature in the context of higher education. Thus, this research aimed to identify the most important personality traits in the workplace from the perspectives of higher education system leaders in four countries. The data were gathered by interviewing six participants from different nations, and those participants identified six personality traits that they considered positive at work. These traits include integrity, passion, adaptability, positivity, creativity, and compassion. Moreover, the findings revealed how program leaders can modify their recruitment and placement strategies to promote positive workplace practices and what methods can be used to reduce bad practices and their elimination, leading to higher business prospects. The results of this study can serve as guidelines for managers, program administrators, or intermediaries who want to improve their organizational performance. Moreover, the propositions developed by the findings can be investigated empirically.