Blockchain is receiving increasing attention from academy and industry, since it is considered a breakthrough technology that could bring huge benefits to many different sectors. In 2017, Gartner positioned blockchain close to the peak of inflated expectations, acknowledging the enthusiasm for this technology that is now largely discussed by media. In this scenario, the risk to adopt it in the wake of enthusiasm, without objectively judging its actual added value is rather high. Insurance is one the sectors that, among others, started to carefully investigate the possibilities of blockchain. For this specific sector, however, the hype cycle shows that the technology is still in the innovation trigger phase, meaning that the spectrum of possible applications has not been fully explored yet. Insurers, as with many other companies not necessarily active only in the financial sector, are currently requested to make a hard decision, that is, whether to adopt blockchain or not, and they will only know if they were right in 3–5 years. The objective of this paper is to support actors involved in this decision process by illustrating what a blockchain is, analyzing its advantages and disadvantages, as well as discussing several use cases taken from the insurance sector, which could easily be extended to other domains.

Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.

Schools, universities, and other educational entities are increasingly aware of the untapped potential of digital transformation, an essential process for increasing efficiency and collaboration, and reducing costs and errors in the management of at-scale training systems. In this context, the “Riconnessioni” project was promoted by the Compagnia di San Paolo in agreement with the Ministry of Education but planned, started and developed by the Foundation for the School. The digital transformation started with a defined strategy that leveraged opportunities presented by new technology while meeting the objectives of system stakeholders. Through several steps, that strategy was developed for education connecting everything to support tomorrow’s digital world and creating strong strategic partnerships able to build an ecosystem connecting people, processes, and things into a powerful, secure, and smart communications network. This paper reports on the three-year Riconnessioni project, which is combining the energies of teachers, managers, administrative staff, students, among others, and experimenting with new learning models, taking advantage of opportunities that emerged from perceptions stemming from concerns and systemic issues. To date, more than 150 schools in Italy have been included in the project, together with 550 teachers selected to scale up the instructional process. Using a methodology called “cascade training”, the 550 selected teachers were able to spread the knowledge to more than 2,600 colleagues. The monitoring and evaluation activity performed in Riconnessioni aims at processing information on implementation and results, following three lines. First, it regularly evaluates project activities from a reporting standpoint. Second, it verifies the plan consistency against implementation achievements. Third, it identifies changes produced and focuses on teachers’ and students’ skills to evaluate the effects of the project. The assessment framework is also discussed in this work, reporting on results regarding feedback, follow-up, and effects gathered from the field. The evaluation highlighted that labs were indeed able to improve teachers' competence and underlined the added value of cascade training which spread digital domain knowledge and awareness into the group of involved schools.

Reliability, power consumption, and latency are the three main performance indicators of wireless sensor networks. Time slotted channel hopping (TSCH) is a promising technique introduced in the IEEE 802.15.4 standard that performs some steps ahead in the direction of the final dream to meet all the previous requirements at the same time. In this paper, a simple and effective mathematical model is presented for TSCH that, starting from measurements performed on a real testbed, permits to characterize both the network and the surrounding environment. To better characterize power consumption, an experimental measurement campaign was purposely performed on OpenMote B devices. The model, which was checked against a real 6TiSCH implementation, can be employed to predict network behaviour when configuration parameters are varied, in such a way to satisfy different application contexts. Results show that, when one of the three above indices is privileged, unavoidably there is a worsening of the others.

Virtual Reality (VR) has increasingly attracted the attention of the computer animation community in search of more intuitive and effective alternatives to the current sophisticated user interfaces. Previous works in the literature already demonstrated the higher affordances offered by VR interaction, as well as the enhanced spatial understanding that arises thanks to the strong sense of immersion guaranteed by virtual environments. These factors have the potential to improve the animators’ job, which is tremendously skill-intensive and time-consuming. The present paper explores the opportunities provided by VR-based interfaces for the generation of 3D animations via armature deformation. To the best of the authors’ knowledge, for the first time a tool is presented which allows users to manage a complete pipeline supporting the above animation method, by letting them execute key tasks such as rigging, skinning and posing within a well-known animation suite using a customizable interface. Moreover, it is the first work to validate, in both objective and subjective terms, character animation performance in the above tasks and under realistic work conditions involving different user categories. In our experiments, task completion time was reduced by 26%, on average, while maintaining almost the same levels of accuracy and precision for both novice and experienced users.

Computer-based assessment of exams provides teachers and students with two main benefits: fairness and effectiveness in the evaluation process. This paper proposes a fully automatic evaluation tool for the Graphic and Virtual Design (GVD) curriculum at the First School of Architecture of the Politecnico di Torino, Italy. In particular, the tool is designed for the 3D modeling course, taught during the second year, where students have to prove their ability to model static scenes using the open source modeler Blender. During the final exam, students are required to create a 3D model as similar as possible to a reference object proposed by the teacher and shown through a set of 2D views; the similarity of the images is judged according to both model shape and materials. The traditional assessment process is particularly slow and strongly based on teachers subjective evaluation; the proposed solution efficiently implements an objective assessment mechanism that exploits computer vision and image analysis algorithms to automatically extract similarity indices. These indices are related to partial evaluation grades, which are then combined to obtain the final mark. A comparison with the traditional assessment process shows robustness and trustworthiness of the designed approach.