ABSTRACT
User-experience (UX) evaluation is one approach to design a high-quality user experience for a product. UX evaluation aims to examine the effectiveness of a product and what end-users expect to do with the product to complete intended tasks. In education, adopting a learning management system (LMS) is critical for institutions. Thus, it is important to understand the users' needs and requirements in order to design a comprehensive LMS that provides the desired features in ways that are easy to use. The purpose of this study was to describe the process of a UX evaluation of a K-12 educational organization's LMS that was developed in-house in response to COVID-19. Data for this study was collected using a triangulation of methods: interviews, survey questionnaires, thinking-aloud, and focus-group discussions. The result of UX evaluation lists 19 problems that users had with their current LMS and 17 areas of the LMS where users wanted improvement.
ABSTRACT
Quality control Charts were used to monitor the number of infections with the emerging corona virus (Covid-19) for the purpose of predicting the extent of the disease's control, knowing the extent of its spread, and determining the injuries if they were within or outside the limits of the control charts. The research aims to use each of the control chart of the (Kernel Principal Component Analysis Control Chart) and (K- Nearest Neighbor Control Chart). As (18) variables representing the governorates of Iraq were used, depending on the daily epidemiological position of the Public Health Department of the Iraqi Ministry of Health. To compare the performance of the charts, a measure of average length of run was adopted, as the results showed that the number of infection with the new Corona virus is out of control, and that the (KNN) chart had better performance in the short term with a relative equality in the performance of the two charts in the medium and long rang
ABSTRACT
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices' security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.