The widespread use of computer technologies in education has reshaped the roles of instructors, who are encouraged to innovate interactive forms of technology‐supported instruction that promote participation and collaboration. This in turn engenders new experiences of teaching that need to be gathered and capitalized as teaching assets to be shared among communities of instructors. Among these experiences, best teaching practices (BTPs) are instructional practices accumulated in teaching that have been proven to work well, give good results, and can therefore be recommended as a model. Identifying and sharing best practices means duplicating successes which help instructors learn from each other and deliver better quality teaching. This paper presents a knowledge management framework for acquiring, coding, sharing, and reusing BTPs. To encourage instructors’ participation, the framework is based on peer scoring of BTPs, which stimulates contribution and interaction. The framework has been implemented as a knowledge portal that allows instructors to create, store, search, and share BTPs and to receive feedback and comments from other users, providing many useful functionalities and services to users as individuals and communities. The paper presents also a real‐life case study, lessons learned from using the system within a community of instructors, and a system evaluation of the effectiveness of reusing BTPs using the reuse effort and impact metrics. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:163–178, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21776

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Prediction of financial and economic markets is very challenging but valuable for economists, business owners, and traders. Forecasting stock market prices depends on many factors, such as other markets' performance, economic state of a country, and others. In behavioral finance, people's emotions and opinions influence their transactional decisions and therefore the financial markets. The focus of this research is to predict the Saudi Stock Market Index by utilizing its previous values and the impact of people's sentiments on their financial decisions. Human emotions and opinions are directly influenced by media and news, which we incorporated by utilizing the Global Data on Events, Location, and Tone (GDELT) dataset by Google. GDELT is a collection of news from all over the world from different types of media such as TV, broad-casts, radio, newspapers, and websites. We extracted two time series from GDELT, filtered for Saudi Arabian news. The two time series rep-resent daily values of tone and social media attention. We studied the characteristics of the generated multivariate time series, then deployed and compared multiple multivariate models to predict the daily index of the Saudi stock market.

The latest COVID-19 pandemic is a specific and unusual event. It forced universities to close their doors and move fully to distance education. The sudden shift from traditional education to full distance education created many challenges and difficulties for universities, faculty members, and students. This study aims to investigate the challenges and obstacles faced by undergraduate women in Saudi Arabia universities while using online-only learning during the COVID-19 pandemic outbreak. Moreover, this study provides some recommendations to address these challenges from undergraduate women’s perspectives. The study used a qualitative research methodology to investigate the challenges and difficulties. The participants were undergraduate women selected using random purposive sampling technique from the population of College of Computer and Information Sciences (CCIS) at Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia. The final sample consisted of 68 undergraduate women who responded to a predesigned open-ended questionnaire that was sent via e-mail to targeted respondents. The data gathered from the questionnaire were analyzed using qualitative content analysis. Results of the research revealed that the most obvious challenges identified by the participants were technical issues, lack of in-person interaction, distractions and time management, lack of a systematic schedule, stress and psychological pressure, missing the traditional university environment, limited availability of digital devices, and lack of access to external learning resources.

Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.