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.

Online community facilitates firm-consumer and consumer-consumer interactions for value co-creation. This study explores the relationship between social capital of online community users and community value co-creation in the context of the Xiaomi community. In the study, the forms of value co-creation are differentiated into two forms: initiated value co-creation and participatory value co-creation, and the effects of different types of online community users’ social capital on the forms of value co-creation in which they participate are empirically examined, and the results find that: structural capital has a significant positive effect on initiated value co-creation, while the effect on participatory value co-creation is insignificant; cognitive capital has a significant positive effect on both initiated value co-creation and participatory value co-creation; and cognitive capital has a significant positive effect on both initiated value co-creation and participatory value co-creation. In this context, the present study contributes to a deeper comprehension of the interplay between social capital and models of value co-creation.

This article examines migration as a complex social phenomenon using innovative pedagogical tools such as Story Maps and virtual ethnography. the study focuses on how these tools enhance the learning process by integrating Paulo Freire’s critical pedagogy. Original empirical data was collected from student feedback and reflective exercises, demonstrating enhanced critical thinking and engagement. The study also highlights the challenges posed by technological access inequalities, emphasizing the need for equitable solutions.

There is a growing trend among elderly people to live alone and this trend is expected to increase in the future. Social isolation and limited support can have a negative impact on the physical and mental well-being of older adults. The increasing life expectancy and expanding geriatric population necessitate the development of innovative solutions to support their health, independence, and autonomy. This article addresses the key challenges and issues confronting the elderly and analyzes various IoT technologies and solutions proposed to enhance their lives. Smart home technologies improve the quality of life and enable older adults to live independently in their own homes while their adult children are at work. This article presents a smart home model for the elderly in Kazakhstan, based on their needs, concerns, and financial capabilities. The proposed prototype will be developed using an accessible, open-source intelligent system that includes health monitoring, medication adherence monitoring, alerting family members in case of falls or deteriorating health indicators, and video surveillance. Another advantage of this system is the automation of processes such as automatic lighting control, voice command functionality, home security, and climate control. Preliminary testing of the hardware model shows promising results, with plans for continuous improvement and evaluation as it is deployed. Key criteria for its implementation include affordability, accessibility, and feasibility. Based on Kazakhstan’s unique socio-cultural and economic context, this paper proposes a sophisticated smart home model tailored to the specific needs and financial capabilities of elderly Kazakhs.