Abstract
Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures. © Springer Nature Switzerland AG 2020.

Abstract
The great ambition of using games as the cornerstone of education is hindered by its associated teaching workload. The BEACONING project developed a framework based on an authoring tool for gamified lesson paths, which has been rolled-out in large scale across Europe. It includes stages for planning game-based educational activities, plus their deployment, monitoring, and assessment.

Abstract
One of the great challenges in programming education is to keep students motivated while working on their programming assignments. Of the techniques proposed in the literature to engage students, gamification is arguably the most widely spread and effective method. Nevertheless, gamification is not a panacea and can be harmful to students. Challenges comprising intrinsic motivators of games, such as graphical feedback and game-thinking, are more prone to have longterm positive effects on students, but those are typically complex to create or adapt to slightly distinct contexts. This paper presents Asura, a game-based programming assessment environment providing means to minimize the hurdle of building game challenges. These challenges invite the student to code a Software Agent to solve a certain problem, in a way that can defeat every opponent. Moreover, the experiment conducted to assess the difficulty of authoring Asura challenges is described.

Abstract
Virtual Reality (VR) through head-mounted displays (HMDs) can be delivered via multiple setups such as smartphones, standalone VR or VR Workstations. The VR Workstation setup delivers the best performance of them all; however, as a drawback up until recently, it required cables to power up the VR equipment. The introduction of wireless solutions for VR Workstations came to solve one of the disadvantages of this setup. However, the impact of the wireless solution versus the HMD cables was not yet properly investigated. In this paper, we study the impact of using a wired vs wireless HMD on Presence, Cybersickness, and Game Experience. We conducted a quasi-experimental between-subjects study with 68 participants assigned to the following three groups that were balanced regarding gender and sample size: Cable (participants used a wired HMD), Cable & x002B; Help (a researcher assisted the participants with the HMD cable during the experience to reduce the participants; awareness of it), and Wireless (untethered HMD). Results showed no significant differences in presence and cybersickness. The game experience was significantly different, with Cable & x002B; Help performing better than Cable and Wireless. We conclude that sense of presence using wireless solutions is equated to wired HMD solutions.

Abstract
This article introduces a Special Issue that gathers a collection of effective tools to promote the teaching and learning of writing in school-aged and university students, across varied contexts. The authors present the theoretical rationale and technical specificities of writing tools aimed at enhancing writing processes (e.g., spelling, revising) and/or at providing writers with automated feedback to improve the implementation of those processes. The tools are described in detail, along with empirical data on their effectiveness in improving one or more aspects of writing. All articles conclude by indicating future directions for further developing and evaluating the tools. This Special Issue represents an important contribution to the field of technology-based writing instruction, in a moment in which online teaching and learning tools have shifted from being an instructional asset to a necessity. We hope that in the future the validation of each tool can be expanded by reaching out to different populations and cultural contexts.

Abstract
Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.