Abstract
The academic performance of a higher education student can be affected by several factors and in most cases Higher Education Institutions (HEI) have programs to intervene, prevent failure or students dropping out. These include student tutoring, mentoring, recovery classes, summer school, etc. Being able to identify the borderline cases is extremely important for planning and intervening in time. This position paper reports on an ongoing project, being developed at the University of Trás-os-Montes e Alto Douro (UTAD), which uses the students’ data and artificial intelligence algorithms to create models and predict the performance of students and classes. The main objective of the IA.EDU project is to research the usage of data, artificial intelligence and data science to create artificial intelligence solutions, including models and applications, to provide predictive information that can contribute to the increase in students’ academic success and a reduction in the dropout rate, by making it possible to act proactively with the students at risk, course directors and course designers. © 2020, Springer Nature Switzerland AG.

Abstract
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human-drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

Abstract
The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution’s reputation and funding and rankings. For this reason, it is essential to identify which students are at risk. In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies, we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data. © 2021, Springer Nature Switzerland AG.

Abstract
Visualization can be defined as a technique that allows us to obtain the perception of an object/event in a clear and consistent way. The use of visualization in education is a key factor to explain complex information in a clear way. Therefore, it is essential to have tools capable of visualizing various types of data. An example of a data type is the weather forecast data, which includes various atmospheric data for a given place, and allows the simulation of the atmospheric evolution. It is used for decision making in many areas, such as, agriculture, fishing, tourism, etc. Thus, it is beneficial to demonstrate the usefulness of this type of visualization to better understand the meteorological phenomena, as well as to teach scientific visualization techniques in order to enable access to information that otherwise can only be interpreted by qualified people. In this article it will be discussed the scientific visualization and its benefits to the area of meteorology, and it will be presented a case study of data visualization using the ParaView tools for meteorological data visualization and analysis. ParaView is a multiplatform tool based on the Visualization Toolkit (VTK) that provides features to process, analyze, and visualize various types of data. This study aims to present a tool for scientific visualization and to demonstrate its applications and usefulness for education. © 2021, Springer Nature Switzerland AG.

Abstract
The fields of artificial intelligence, knowledge inference, data science, etc. have been deeply studied over time and many theoretical approaches have been developed, including its application to health and diseases inference. The creation of prototype and consumer systems has been restrained by the technology limitations on data acquisition and processing, which has been greatly overcome with the new sensors and mobile devices technologies. So, in this work we go through a literature review of the current state of the art on record to the usage of mobile technologies for diseases inference. The review methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The criteria were based on journal articles, prior to 2008, and using the defined keywords. A total of 14 selected articles were analyzed. A general conclusion was attained regarding the current state of maturity of the field, leading to fully functional consumer and professional market products.

Abstract
Currently, solutions based on the Internet of Things (IoT) concept are increasingly being adopted in several fields, namely, industry, agriculture, and home automation. The costs associated with this type of equipment is reasonably small, as IoT devices usually do not have output peripherals to display information about their status (e.g., a screen or a printer), although they may have informative LEDs, which is sometimes insufficient. For most IoT devices, the price of a minimalist display, to output and display the device's running status (i.e., what the device is doing), might cost much more than the actual IoT device. Occasionally, it might become necessary to visualize the IoT device output, making it necessary to find solutions to show the hardware output information in real time, without requiring extra equipment, only what the administrator usually has with them. In order to solve the above, a technological solution that allows for the visualization of IoT device information in actual time, using augmented reality and a simple smartphone, was developed and analyzed. In addition, the system created integrates a security layer, at the level of AR, to secure the shown data from unwanted eyes. The results of the tests carried out allowed us to validate the operation of the solution when accessing the information of the IoT devices, verify the operation of the security layer in AR, analyze the interaction between smartphones, the platform, and the devices, and check which AR markers are most optimized for this use case. This work results in a secure augmented reality solution, which can be used with a simple smartphone, to monitor/manage IoT devices in industrial, laboratory or research environments.