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BackgroundMany countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting. ObjectiveThe goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review. MethodsThis research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed. ResultsThis study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps. ConclusionsThis article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.

Monitoring students' attention and engagement levels in classrooms are critical in promoting an interactive teaching process. Therefore, in addition to teaching, instructors are constantly making an effort to monitor and maintain the students' attention and engagement levels throughout the lecture session. Recent advancements in computer vision techniques have resulted in tools that empower real-time head-pose estimation for multiple people with reasonable accuracy. This study aims to harness such technology to help instructors determine and evaluate faster and more accurately the level of students' attention, thus supporting them in managing the session appropriately and more beneficial to the students. Our system employs an ordinary webcam, a standard computer, and simple computer vision algorithms. The system determines the students' faces, estimates their level of attention, and displays them through easy color-coded charts to the instructor to take the necessary action during the lecture. To avoid complicating the system, the metric given is a percentage of attention and associated color-coded level of concern so that the instructor can act. Our results show that the proposed system can numerically evaluate students' attention in the classroom, individually and in-group, in real-time. Our scheme was tested a classroom with 8 students, and the instructors' feedbacks were positive. From the results, for instance, a student shows 89% instant attention, and the overall classroom attention was 40% after 11 minutes of starting the lecture. Similar measurements can be obtained from each student and the overall students, at any time during the lecture, which is novel compared to other similar studies that only target either the student or the overall class in mostly offline mode or complicated and unpractical setup ((more detailed comparisons are in the introduction). We assume that this work has potential in significantly enhancing instructors' teaching abilities and students' academic performance.

Aim/Purpose: This study sought to understand the views of both teachers and students on the usage of humanoid robots as teaching assistants in a specifically Arab context. Background: Social robots have in recent times penetrated the educational space. Although prevalent in Asia and some Western regions, the uptake, perception and acceptance of educational robots in the Arab or Emirati region is not known. Methodology: A total of 20 children and 5 teachers were randomly selected to comprise the sample for this study, which was a qualitative exploration executed using focus groups after an NAO robot (pronounced now) was deployed in their school for a day of revision sessions. Contribution: Where other papers on this topic have largely been based in other countries, this paper, to our knowledge, is the first to examine the potential for the integration of educational robots in the Arab context. Findings: The students were generally appreciative of the incorporation of humanoid robots as co-teachers, whereas the teachers were more circumspect, expressing some concerns and noting a desire to better streamline the process of bringing robots to the classroom. Recommendations for Practitioners: We found that the malleability of the robot's voice played a pivotal role in the acceptability of the robot, and that generally students did well in smaller groups with the robot; teachers expressed concern that the children would become easily distracted should too many children be privy to one robot. Recommendations for Researchers: Our results provide valuable recommendations for researchers in the area. We believe, there needs to be continued efforts in devising suitable methodological assessment tools to evaluate student and teacher attitudes in the classroom particularly in the Arab world. We also advise researchers to focus on providing adaptive behavior in the context of educational robots. There are different distinct areas that need further clarifications and study based on our review. Impact on Society: On a wider scale, the findings of this paper have a huge implication for the educational technology as the integration of robotics in education is one of the emerging trends in the area, particularly in the UAE. This study allows to answer questions related to attitudes and perceptions of both teachers and students toward educational robots in the UAE. Future Research: Possible avenues of research in the area include focusing on the adaptive and natural behavior of robots in disciplines other than Mathematics as a means of successfully integrating robots in the classroom.

Healthy aging requires the maintenance of good physical and cognitive activity. However, as they age, older adults often experience a decline in physical and cognitive activity, leading to a more sedentary lifestyle. Some older adults may not have a choice but to become increasingly sedentary as they age due to injury or deteriorated physicality. As such, they require assistive technologies to aid in their daily lives and activities to maintain healthy cognitive function. Social Robots are a newer form of assistive technology, specifically designed for social interactions and gameplay. As with other assistive technologies, compliance barriers to their acceptance and use for meaningful, seated activities among older adults are expected. To better explore this phenomenon, improve quality of life and understand what drives older adults to accept and use newer forms of technology like social robots, this conceptual paper conjoins two theoretical frameworks: The Activity Theory of Aging (ATA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). As social robots hold great promise for improving the quality of life for older adults, exploring what driving factors could enable their greater acceptance and use is essential to furthering this field of study within Australia. [ABSTRACT FROM AUTHOR]

This study aims to classify research impact indicators based on their characteristics and scope. A concept of evidence-based nomenclature of research impact (RI) indicator has been introduced for generalization and transformation of scope.