This study explores the impact of online assessments on students’ academic performance and learning outcomes at the University of Technology in South Africa. The research problem addresses the effectiveness and challenges of digital assessment platforms in higher education (HE), particularly their influence on student engagement, feedback quality, and academic integrity. A qualitative case study approach was employed, involving semi-structured interviews with ten undergraduate and postgraduate students from diverse academic backgrounds. The findings reveal that while online assessments provide flexibility and immediate feedback, they also pose challenges related to technical issues, feedback delays, and concerns about long-term knowledge retention. The study highlights the necessity of aligning assessment strategies with constructivist learning principles to enhance critical thinking and student-centered learning. Implications for theory include strengthening the application of constructivist learning in digital environments, while practical recommendations focus on improving assessment design, institutional support, and feedback mechanisms. Policy adjustments should consider inclusive and equitable access to online assessments. Future research should further investigate the long-term impact of digital assessments on professional readiness. This study contributes to ongoing discussions on online education by offering a nuanced understanding of digital assessment challenges and opportunities in higher education.

This study investigates the application and effectiveness of modern teaching techniques in improving reading literacy among elementary school students in Kazakhstan. In the rapidly evolving educational landscape, the integration of innovative pedagogical strategies is essential to foster student reading skills and general literacy. This study aims to explore how these modern teaching techniques can be applied to improve reading literacy among elementary school students in Kazakhstan. The study sample includes 64 respondents to the research. The key modern teaching techniques explored in this study include the use of digital learning tools, interactive reading sessions, differentiated instruction, and collaborative learning activities. The findings reveal significant improvements in reading literacy among students exposed to these techniques, highlighting the potential of modern pedagogy to bridge literacy gaps and promote educational equity. Furthermore, the study discusses the challenges and opportunities to implement these techniques within the Kazakhstani educational system. The results provide valuable information for educators, policymakers, and stakeholders aiming to improve reading literacy through innovative teaching practices.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

The tunable conduction of coumarin-based composites has attracted considerable attention in a wide range of applications due to their unique chemical structures and fascinating properties. The incorporation of graphene oxide (GO) further enhances coumarin properties, including strong fluorescence, reversible photodimerization, and good thermal stability, expanding their potential use in advanced technological applications. This review describes the developmental evolution from GO, GO-polymer, and coumarin-based polymer to the coumarin-GO composite, concerning their synthesis, characterization, unique properties, and wide applications. We especially highlight the outstanding progress in the synthesis and structural characteristics along with their physical and chemical properties. Therefore, understanding their structure-property relations is very important to acquire scientific and technological information for developing the advanced materials with interesting performance in optoelectronic and energy applications as well as in the biomedical field. Given the expertise of influenced factors (e.g., dispersion quality, functionalization, and loading level) on the overall extent of enhancement, future research directions include optimizing coumarin-GO composites by varying the nanofiller types and coumarin compositions, which could significantly promote the development of next-generation polymer composites for specific applications.

Generational differences shape technological preferences and fundamentally influence workplace motivation and interactions. Our research aims to examine in detail how different generations assess the importance of workplace communication and leadership styles and how these diverse preferences impact workplace motivation and commitment. In our analysis, we studied the behavioral patterns of four generations—Baby Boomers, Generations X, Y, and Z—through anonymous online questionnaires supplemented by in-depth interviews conducted with a leader and a Generation Z employee. To verify our hypotheses, we employed statistical methods, including the Chi-Square test, Spearman’s rank correlation, and cross-tabulation analysis. Our results clearly demonstrated that different generations evaluate the importance of applied leadership and communication styles differently. While Generations Y and Z highly value flexible, supportive leadership styles, older generations, such as the Baby Boomers prefer more traditional, structured approaches. The study confirmed that aligning leadership and communication styles is crucial, as it significantly impacts the workplace atmosphere and employee performance. Our research findings hold both theoretical and practical significance. This research highlights how understanding generational preferences in leadership and communication styles can enhance workplace cohesion and efficiency. The results provide specific guidance for leaders and HR professionals to create a supportive and adaptable environment that effectively meets the needs of diverse generations.