Modern technologies have intensified innovations and necessitated changes in public service processes and operations. Continuous employee learning development (CELD) is one means of the molecule-atom that keep employees motivated and sustain competitiveness. The study explored the efficacy of CELD in relation to modern technology in the South African (SA) public service departments between 2014 to 2023 era. Departments are faced with challenge of equipping their employees with adequate professional and technical skills for both the present and the future in order to deliver specific government priorities. Data for the study were gathered utilizing a qualitative semi-structured e-questionnaire. The study sample consisted of 677 human capital development practitioners from national and provincial government departments in SA. The inefficacy CELD and the inadequacy of technological infrastructure and service delivery can be attributed to the failure by executive management and senior managers to invest in CELD to prepare employees for digital world. It is recommended that departments should use Ruggles’s knowledge management, Kirkpatrick’s training, and Becker and Schultz’s human capital models as sound measurement tools in order to gain a true return on investment. The study adds pragmatic insight into the value of CELD in the new technological environment in public service departments.

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.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

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.

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 process of teaching and learning at any stage, the important role of interest guidance cannot be ignored. Especially in college mathematics teaching, mathematical knowledge is very complex and abstract, and most students are unable to effectively understand and master it during the learning process. So it is even more important to fully stimulate students' interest in learning. This article analyzes the significance and current situation of stimulating students' learning interest in university mathematics teaching, and conducts effective strategy analysis. In order to effectively awaken students' desire for knowledge, guide students to change from passive learning to active learning, so that students can continue to grow and progress in this process.