As a result of several governments closing educational facilities in reaction to the COVID-19 pandemic in 2020, almost 80% of the world’s students were not in school for several weeks. Schools and universities are thus increasing their efforts to leverage educational resources and provide possibilities for remote learning. A variety of educational programs, platforms, and technologies are now accessible to support student learning; while these tools are important for society, they are primarily concerned with the dissemination of theoretical material. There is a lack of support for hands-on laboratory work and practical experience. This is particularly important for all disciplines related to science, technology, engineering, and mathematics (STEM), where labs and pedagogical assets must be continuously enhanced in order to provide effective study programs. In this study, we describe a unique perspective to achieving multi-sensory learning through the integration of virtual and augmented reality (VR/AR) with haptic wearables in STEM education. We address the implications of a novel viewpoint on established pedagogical notions. We want to encourage worldwide efforts to make fully immersive, open, and remote laboratory learning a reality.

The heterogeneity of the leading mobile platforms in terms of user interfaces, user experience, programming language, and ecosystem have made cross-platform development frameworks popular. These aid the creation of mobile applications – apps – that can be executed across the target platforms (typically Android and iOS) with minimal to no platform-specific code. Due to the cost- and time-saving possibilities introduced through adopting such a framework, researchers and practitioners alike have taken an interest in the underlying technologies. Examining the body of knowledge, we, nonetheless, frequently encounter discussions on the drawbacks of these frameworks, especially with regard to the performance of the apps they generate. Motivated by the ongoing discourse and a lack of empirical evidence, we scrutinised the essential piece of the cross-platform frameworks: the bridge enabling cross-platform code to communicate with the underlying operating system and device hardware APIs. The study we present in the article benchmarks and measures the performance of this bridge to reveal its associated overhead in Android apps. The development of the artifacts for this experiment was conducted using five cross-platform development frameworks to generate Android apps, in addition to a baseline native Android app implementation. Our results indicate that – for Android apps – the use of cross-platform frameworks for the development of mobile apps may lead to decreased performance compared to the native development approach. Nevertheless, certain cross-platform frameworks can perform equally well or even better than native on certain metrics which highlights the importance of well-defined technical requirements and specifications for deliberate selection of a cross-platform framework or overall development approach.

Mobile apps are increasingly realized by using a crossplatform development framework. Using such frameworks, code is written once but the app can be deployed to multiple platforms. Despite progress in research on cross-platform techniques, results (i.e. apps) are not always satisfactory. They are subject to tedious tailoring and the development effort tends to be notable. In these cases, either pure web apps (realized through web browsers) or native apps (realized for each platform separately) are chosen. Recent activities have led to new approaches. In this paper, we have a closer look at three of these, namely React Native, the Ionic Framework, and Fuse. We present a comprehensive analysis of the three approaches. Our work is based on a real-world use case, which allows us to provide generalizable advice. Our findings suggest that there is no clear winner; the frameworks incorporate notable ideas and general progress in the field can be asserted.

During crisis situations, emergency operators require fast information access to achieve situation awareness and make the best possible decisions. Augmented reality could be used to visualize the wealth of user-generated content available on social media and enable context-adaptive functions for emergency operators. Although emergency operators agree that social media analytics will be important for their future work, it poses a challenge to filter and visualize large amounts of social media data. We conducted a goal-directed task analysis to identify the situation awareness requirements of emergency operators. By collecting tweets during two storms in Germany we evaluated the usefulness of Twitter data for achieving situation awareness and conducted interviews with emergency operators to derive filter strategies for social media data. We synthesized the results by discussing how the unique interface of augmented reality can be used to integrate social media data into emergency control rooms for situation awareness support.

The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.