Ideological and political education is not only a basic course to cultivate students’ moral quality, but also an important part of modern education outline. The current intelligent electronic technology course should strengthen the gradual integration of curriculum and ideological education. Under the background of the new era, the state pays more attention to education, aiming to integrate the concept of ideological and political education into the classroom to effectively improve the effectiveness of comprehensive education for students. The course of intelligent electronic technology should integrate ideological and political education resources and innovate educational means from teachers to classrooms. This paper analyzes the principle of Integrating ideological and political education into intelligent electronic technology curriculum, and hopes to put forward constructive suggestions on the research and innovation path.

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.

This article explores the transformative journey of universities in Kazakhstan, focusing on the results of recent research on the quality of higher education. The study delves into the significant reforms and innovations implemented in the Kazakhstani higher education system, assessing their impact on academic standards, student performance, and institutional efficiency. Through comprehensive data analysis and expert interviews, the research highlights the strides made in improving educational quality, fostering international collaborations, and integrating modern technologies in teaching and learning. The findings underscore the critical role of government policies, industry partnerships, and community participation in driving these transformations. This article provides valuable information on the challenges and successes experienced by Kazakhstani universities, providing a blueprint for further advances in the sector of higher education. The key factors contributing to the success of these reforms include strong government support, international collaboration, robust quality assurance mechanisms, a focus on research and innovation, and professional development for educators. While challenges remain, the future of higher education in Kazakhstan looks promising, provided that these efforts continue and are further refined to address existing gaps.

To address gaps in practical skills among Public Health and Preventive Medicine graduates, an ‘open collaborative practice teaching model’ integrating medicine, teaching, and research was introduced. A cross-sectional study surveyed 312 Preventive Medicine undergraduates at a Yunnan medical university from 2020 to 2023, utilizing satisfaction scores and analyses (cluster, factor, SWOT) to assess the impact of the reform. Satisfaction scores from baseline, mid-term, and end-term assessments showed minor variations (4.30, 4.29, 4.36), with dissatisfaction primarily related to teaching content and methods. Key influences on satisfaction included teaching content, methods, and effectiveness. The SWOT analysis highlighted the importance of continuously updating teaching strategies to meet changing student expectations. This study suggests that the model has the potential for wider use in enhancing public health education, particularly in regions facing similar challenges.

This paper conducts a bibliometric visual analysis of the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) in education, using CiteSpace software. Drawing on data from the Web of Science, the study explores research trends and influential works related to UTAUT from 2008 to 2023. It highlights the growing use of educational technologies such as mobile learning and virtual reality tools. The analysis reveals the most cited articles, journals, and key institutions involved in UTAUT research. Furthermore, keyword analysis identifies research hot spots, such as artificial intelligence and behavioral intentions. This study contributes to the understanding of how UTAUT has been used to predict technology adoption in education and provides recommendations for future research directions based on emerging trends in the digital learning environment.