The COVID-19 pandemic has shifted education from traditional in-person classes to remote, online-dependent learning, often resulting in reduced learning effectiveness and satisfaction due to limited face-to-face interaction. To address these challenges, interactive teaching strategies, such as the flipped classroom approach, have gained attention. The flipped classroom model emphasizes individual preparation outside class and collaborative learning during class time, relying heavily on in-person interactions. To adapt this method to remote learning, the Remote Flipped Classroom (RFC) integrates the flipped classroom approach with online learning, allowing flexibility while maintaining interactive opportunities. RFC has incorporated short films as teaching tools, leveraging their ability to contextualize knowledge and cater to the preferences of visually-driven younger learners. However, research on the effectiveness of RFC with films remains limited, particularly in fields like nursing education, where practical engagement is crucial. This article shares the practical experience of applying RFC with films in a nursing education context. Positive feedback was observed, though many students still expressed a preference for in-person classes. These insights suggest that strategies like RFC with films could be valuable in maintaining engagement and learning efficiency in remote classrooms.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.