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.

In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

This study assesses the implementation of socioformation in Public Higher Education Institutions (HEIs) in Mexico, exploring its impact on the quality of education in the knowledge society. With a sample of 150 educators, gender-balanced (44.7% female, 55.3% male), and an average age of 43.7 years, the research employed a validated socioformative rubric. Significant progress was observed in analytical and creative thinking, while areas related to living conditions and entrepreneurship education showed slower development. The findings highlight the advancements in socioformation but advocate for further research, including classroom observation and student evidence collection. Gender differences, communication, and leadership emerged as critical factors influencing socioformation implementation. Women demonstrated deeper comprehension of the educational model, willingness to adopt innovative strategies, and emphasis on socioformation axes. As educators gain experience, their adaptability to new pedagogical approaches increases. The study underscores the universal relevance of effective communication, leadership, and stakeholder involvement in successful educational model implementation. The research contributes valuable insights, emphasizing the importance of openness to new approaches and collaboration to prepare students for the challenges of the evolving knowledge society.

This article addresses the pressing issue of training and mediation for conflict resolution among employees within a corporate setting. Employing a methodology that includes literature analysis, comparative studies, and surveys, we explore various strategies and their effectiveness in mitigating workplace conflicts. Through a comprehensive comparison with metrics and conclusions from other scholarly works, we provide a nuanced understanding of the current landscape of conflict resolution practices. As a result of our research, we implemented a tailored training program focused on conflict resolution for employees within a mobile company, alongside the development of a competency framework designed to enhance conflict resolution skills. This framework comprises five integral components: emotional, operational, motivational, behavioral, and regulatory. Our findings suggest that training in each of these competencies is essential for fostering a healthy workplace environment and must be integrated into organizational practices. The importance of this initiative cannot be overstated; effective conflict resolution skills are not only vital for individual employee wellbeing but also crucial for the overall efficiency and productivity of the organization. By investing in these competencies, companies can reduce turnover, enhance team cohesion, and create a more positive and collaborative workplace culture.

There is insufficient consideration of Generation Z’s cultural and generational needs in the implementation of biometric attendance systems in Arabic educational settings. This study delves into Generation Z’s discipline, exploring their perspectives on attendance systems and aligning commitment with their interests. The primary aim is to gauge biometric systems’ impact on productivity. Google Form questionnaires collected data from young employees, ages 25 to 35, who belong to Generation Z’s working in the higher education system. Structural equation modeling and descriptive analysis assessed the data. While biometric systems enhance discipline, they may dampen morale. Implementing systems fairly and maintaining flexibility is vital. The study underscores the importance of evaluating employees based on achievements. It sheds light on biometric systems’ role in attendance management and organizational performance, aiding HR practices. The results showed no significant effect of Employee Management Practices (EMP) on organization performance through Biometric Attendance Technology (BAT) (B = 0.049, t = 1.330, p = 0.184). Nor significant effects of Organizational Performance Metrics (OPM) (B = 0.019, t = 0.608, p = 0.543). Technological Infrastructure (TI) (B = 0.019, t = 0.2461, p = 0.645), or Satisfaction and Engagement (ESE) (B = 0.057, t = 1.381, p = 0.167) on organization performance through Biometric Attendance Technology. The mediator impact was also found to be not significant (P > 0.05). Therefore, both direct and specific indirect effects were not significant. Indicating that Biometric Attendance Technology does not mediate the relationship between these variables and organizational performance.