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.

Augmented reality (AR) is a well-known technology that can be exploited to provide mass-market users an effective and customizable support in a large spectrum of personal applications, by overlapping computer-generated hints to the real world. Mobile devices, such as smartphones and tablets, are playing a key role in the exponential growth of this kind of solutions. Nonetheless, there exists some application domains that just started to take advantage from the AR systems. Maintenance, repair, and assembly have been considered as strategic fields for the application of the AR technology from the 1990s, but often only specialists using ad hoc hardware were involved in limited experimental tests. Nowadays, AR-based maintenance and repair procedures are available also for end-users on consumer electronics devices. This paper aims to explore new challenges and opportunities of this technology, by also presenting the software framework that is being developed in the EASE-R ³ project by exploiting reconfigurable AR procedures and tele-assistance to overcome some of the limitations of current solutions.

Since the origins of Augmented Reality (AR), industry has always been one of its prominent application domains. The recent advances in both portable and wearable AR devices and the new challenges introduced by the fourth industrial revolution (renowned as industry 4.0) further enlarge the applicability of AR to improve the productiveness and to enhance the user experience. This paper provides an overview on the most important applications of AR regarding the industry domain. Key among the issues raised in this paper are the various applications of AR that enhance the user's ability to understand the movement of mobile robot, the movements of a robot arm and the forces applied by a robot. It is recommended that, in view of the rising need for both users and data privacy, technologies which compose basis for Industry 4.0 will need to change their own way of working to embrace data privacy.

The digitalization is transforming the very nature of factories, from automated systems to intelligent ones. In this process, industrial robots play a key role. Even if repeatability, precision and velocity of the industrial manipulators enable reaching considerable production levels, factories are required to face an increasingly competitive market, which requires being able to dynamically adapt to different situations and conditions. Hence, facilities are moving toward systems that rely on the collaboration between humans and machines. Human workers should understand the behavior of the robots, placing trust in them to properly collaborate. If a fault occurs on a manipulator, its movements are suddenly stopped for security reasons, thus workers may not be able to understand what happened to the robot. Therefore, the operators’ stress and anxiety may increase, compromising the human-robot collaborative scenario. This work fits in this context and it proposes an adaptive Augmented Reality system to display industrial robot faults by means of the Microsoft HoloLens device. Starting from the methodology employed to identify which virtual metaphors best evoke robot faults, an adaptive modality is presented to dynamically display the metaphors in positions close to the fault location, always visible from the user and not occluded by the manipulator. A comparison with a non adaptive modality is proposed to assess the effectiveness of the adaptive solution. Results show that the adaptive modality allows users to recognize faults faster and with fewer movements than the non adaptive one, thus overcoming the limitation of the narrow field-of-view of the HoloLens device.