Due to the Covid-19 pandemic outbreak, academic institutions around the globe have been forced to shut their gates and switch to online learning environments (OLEs). Current online academic environments may not be customized equally for different fields of instruction. This study examined the influence of Covid-19 on OLEs in Software Engineering (SE) and Computer Sciences (CS) fields, where there is allegedly great potential for creating customized OLEs. For this purpose, students and lecturers from Israeli institutions of higher education participated in an online survey that examined the opportunities and constraints involved in learning solely in online environment. Results show that generally, the degree of interactivity in the OLE and adjustment of OLE's for CS and SE fields affect the learning experience and outcome for students. The study points to the need to customize the OLE to the CS and SE fields and emphasize “hands on” experiences rather than theoretical skills. Additionally, it can be is expanding the knowledge and skills of the students to operate at an OLE by adding mandatory provisions to the Software Engineer Book of Knowledge.

Installing security applications is a common way to protect against malicious apps, phishing emails, and other threats in mobile operating systems. While these applications can provide essential security protections, they also tend to access large amounts of people's sensitive information. Therefore, individuals need to evaluate the trade-off between the security features and the privacy invasion when deciding on which protection mechanisms to use. In this paper, we examine factors affecting the willingness to install mobile security applications by taking into account the invasion levels and security features of cyber-security applications. To this end, we propose a visual language that depicts the coverage of different security features as well as privacy intrusiveness levels. Our user study (n=300) shows that users assessing security applications find their trade-off balance in highly secure apps with a medium level of privacy invasion. The results indicate that a low privacy invasion might signal that the security application provides less security. We discuss these findings in the context of understanding the trade-off between privacy and security.

Contemporary mobile devices continuously interrupt people with notifications in various and changing physical environments. As different places can have different social setting, understanding how disturbing an interruption might be to people around the user is not a straightforward task. To understand how users perceive disturbance in their social environment, we analyze the results of a 3-week user study with 50 participants using the experience sampling method and log analysis. We show that perceptions of disturbance are strongly related to the social norms surrounding the place, such as whether the place is considered private or public, even when controlling for the number of people around the user. Furthermore, users' perceptions of disturbance are also related to the activity carried out on the phone, and the subjective perceptions of isolation from other people in the space. We conclude the paper by discussing how our findings can be used to design new mobile devices that are aware of the social norms and their users' environmental context.

The way users manage access to their information and computers has a tremendous effect on the overall security and privacy of individuals and organizations. Usually, access management is conducted using a choice architecture , a behavioral economics concept that describes the way decisions are framed to users. Studies have consistently shown that the design of choice architectures, mainly the selection of default options, has a strong effect on the final decisions users make by nudging them toward certain behaviors. In this article, we propose a method for optimizing access control choice architectures in online social networks. We empirically evaluate the methodology on Facebook, the world's largest online social network, by measuring how well the default options cover the existing user choices and preferences and toward which outcome the choice architecture nudges users. The evaluation includes two parts: (a) collecting access control decisions made by 266 users of Facebook for a period of 3 months; and (b) surveying 533 participants who were asked to express their preferences regarding default options. We demonstrate how optimal defaults can be algorithmically identified from users’ decisions and preferences, and we measure how existing defaults address users’ preferences compared with the optimal ones. We analyze how access control defaults can better serve existing users, and we discuss how our method can be used to establish a common measuring tool when examining the effects of default options.