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After the COVID-19 pandemic, no one refutes the importance of smart online learning systems in the educational process. Measuring student engagement is a crucial step towards smart online learning systems. A smart online learning system can automatically adapt to learners' emotions and provide feedback about their motivations. In the last few decades, online learning environments have generated tremendous interest among researchers in computer-based education. The challenge that researchers face is how to measure student engagement based on their emotions. There has been an increasing interest towards computer vision and camera-based solutions as technology that overcomes the limits of both human observations and expensive equipment used to measure student engagement. Several solutions have been proposed to measure student engagement, but few are behavior-based approaches. In response to these issues, in this paper, we propose a new automatic multimodal approach to measure student engagement levels in real time. Thus, to offer robust and accurate student engagement measures, we combine and analyze three modalities representing students' behaviors: emotions from facial expressions, keyboard keystrokes, and mouse movements. Such a solution operates in real time while providing the exact level of engagement and using the least expensive equipment possible. We validate the proposed multimodal approach through three main experiments, namely single, dual, and multimodal research modalities in novel engagement datasets. In fact, we build new and realistic student engagement datasets to validate our contributions. We record the highest accuracy value (95.23%) for the multimodal approach and the lowest value of "0.04" for mean square error (MSE).

One of the objective of the Computer Science (CS) educational community, when teaching Object Oriented Programming (OOP), is to improve programmer productivity, and improve modelling of the real-world using objects. Some of the new techniques that allow the students to make the connection between steps in developing an algorithm for problem solving are centered on the visualization of objects and their behaviors using 3D animation environments, such as Alice. It is the purpose of this research to assess the performance of novice programmers in KAU's female CS department in Saudi Arabia, and the effectiveness of the visualization environments-Alice-in teaching inheritance as a concept of OOP.

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.