The rise of online learning has brought about a close connection between micro-credentials and lifelong learning, employability, and new models of digital education. Micro-credentials are considered instrumental in transforming higher education today. This study aims to examine the extent to which micro-credentials have been adopted in Malaysia, focussing on the viewpoint of Higher Education Providers (HEPs). It seeks to identify the challenges faced by HEPs when offering micro-credentials, encompassing technological, organisational, and people-related obstacles. By analysing empirical data, this research intends to propose a conceptual framework that can guide the successful adoption and implementation of micro-credentials within educational institutions. By addressing these recommendations, HEPs in Malaysia can successfully adopt and implement micro-credentials within their institutions. This will not only enhance the learning experience for students but also contribute to the overall transformation of higher education, keeping pace with the demands of the digital age and fostering a culture of continuous learning and skill development.

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.

Sustainable development goals bring the environmental performance (EP) of firms into the global policy limelight. EP has been largely compromised due to the emission of hazardous waste by manufacturing firms. These issues are not only contributing to natural resource depletion and global warming but also affecting the well-being of society. This study examines the role of organizational resilience capacity (ORC) and green innovation (GNI) in achieving EP in underdeveloped economies. The data were collected from 173 managers of manufacturing organizations operating in Pakistan. Further, researchers employed partial least-squares structural equation modelling to analyse quantitative data. The findings show that ORC enhances GNI, leading to improved EP. This study provides nuanced insights for developing green and resilient initiatives to improve EP in resource-constrained economies. Likewise, the management of manufacturing organizations should develop resilience capabilities that help organizations quickly adapt to disturbances and assure their capacity to successfully accomplish environmental goals.

The rapid expansion of online education has heightened concerns about cyberbullying in virtual classrooms. This paper presents a comprehensive approach for detecting cyberbullying by analyzing students’ engagement and emotional responses in online classrooms. Due to the influence of camera resolution and surrounding light in online classrooms, students’ facial expressions are often blurry, and the changes in facial features may not be significant. Moreover, most current recognition algorithms utilize larger models, which may not be suitable for real-time detection in online environments. To address these challenges, this study introduces a student facial expression recognition (SFER) method based on an enhanced YOLOv5 (you only look once version 5) model, termed SFER-YOLOv5. Firstly, the improved soft-non-maximum suppression (NMS) is employed to replace the original NMS, effectively enhancing training efficiency. Then, the coordinate attention module is incorporated into the backbone network to improve detection accuracy, particularly in classroom settings with multiple students or when students are at a considerable distance from the camera. Next, the efficient intersection over union (EIoU) loss function is utilized. EIoU calculates width and height losses separately based on complete-IoU, replacing the aspect ratio. Finally, focal loss is introduced to address sample imbalance issues. The comparative results show that SFER-YOLOv5 achieves an mAP@0.5 of 78.4% on the facial expression recognition-2013 dataset, 98.1% on the CK+ dataset, and 88.9% on our self-constructed dataset student facial expressions in classroom (SFEC). These results underscore the effectiveness of SFER-YOLOv5 in enhancing the accuracy of SFER. The proposed method detects reduced engagement, offering a preventive strategy for mitigating cyberbullying in virtual learning environments.

Because of COVID-19, people have felt the social distance and have resorted to the internet for information needs. Hence, fake news has become prevalent as people rely on information explored online. This research aims to examine the social-cultural impacts of fake news adaptation behavior from the social psychological perspective by investigating the relationship between collectivism, social support, sense of belonging, social endorsement, fear of missing out, perceived credibility, issue involvement, and adaptation on fake news among young adults in Malaysia. A quantitative research approach with an online self-administered survey was conducted, and 451 responses were obtained through snowball sampling. In the data analysis, measurement and structural equation modeling were adopted. Findings showed that the relationships among adaptation behaviors on fake news were significantly supported. This research consummates the understanding of the influences of social-cultural (collectivism) on the judgment formation of adaptation among internet users on fake news.