There is no denying that the COVID-19 pandemic resulted in significant stress worldwide and impacted practically every aspect of human activity. The impacts of this deadly virus on education are not seen as gaining much-needed focus from the scientific research community. The majority of educational institutions globally switched to online instruction during the COVID-19 pandemic. However, there were considerable differences in the technical readiness of various nations. In this regard, the study’s attempt to provide a way forward for how the educational sector ought to manage the challenges brought on by COVID-19 issues in support of online educational activities. Since some of the consequences that resulted have an impact on the educational sector, the answers presumably also should have included innovations that would improve scientific research to lessen its effects. Particularly, it appears there is still much that has to be done about the impact of the COVID-19 pandemic on the educational sector. Hence, this perspective review study aims to explore the potential relationship between the COVID-19 pandemic and the educational sector while suggesting a way forward.

This study aimed to analyze government policies in education during the Covid-19 pandemic and how teachers exercised discretion in dealing with limitations in policy implementation. This research work used the desk review method to obtain data on government policies in the field of education during the Covid-19 pandemic. In addition, interviews were conducted to determine the discretion taken in implementing the learning-from-home policy. There were three learning models during the pandemic: face-to-face learning in turns (shifts), online learning, and home visits. Online learning policies did not work well at the pandemic’s beginning due to limited infrastructure and human resources. To overcome various limitations, the government provided internet quota assistance and curriculum adjustments and improved online learning infrastructure. The discretion taken by the teachers in implementing the learning-from-home policy was very dependent on the student’s condition and the availability of the internet network. The practical implication of this research is that street-level bureaucrats need to pay attention to discretionary standards when deciding to provide satisfaction to the people they serve.

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.

This study explores the experiences and perceptions of Chinese postgraduate students in the UK regarding online learning, focusing on the Community of Inquiry (CoI) framework. Semi-structured interviews were used to collect qualitative data, which were analyzed thematically. The findings reveal positive perceptions of online learning, challenges related to technology and infrastructure, the significance of social interaction and collaboration, and the limited impact of teaching quality on student satisfaction. The study emphasizes the importance of the CoI framework in designing effective online learning environments. Limitations include a small sample size and potential bias. Future research should involve larger and more diverse samples, investigate different teaching strategies, and enhance student agency and self-regulated learning in online education. Overall, this study contributes to understanding the applicability of the CoI framework and its potential for improving online learning experiences.