Abstract
The use of digital games as support tools for education has proven to be effective. To explore their potential, it is crucial to design them carefully. This chapter considers the design of games for education, where players cultivate their knowledge and practice their skills by multiplying numerous hindrances during gaming. Educational elements are integrated into the gameplay, which players acquire while playing. The game's effectiveness depends on the players' ability to form a cheerful and encouraging environment to continue playing while increasing their interest in gameplay and improving academic performance. Following a design-first development approach, an innovative proposal for this design is presented, adding a new dimension to the game's tetrad: learning dynamics. Benefiting from years of professional practice, this game pentad design framework fulfills the learning and user experience requirements while overcoming the design limitations of more conventional approaches not based on an educational purpose.

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Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

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We present a literature review that aims to understand the role of the Educational Escape Room (EER) in improving the teaching, learning, and assessment processes through an EER design framework. The main subject is to identify the recent interventions in this field in the last five years. Our study focuses on understanding how it is possible to create an EER available to all students, namely visually challenged users. As a result of the implementation of new learning strategies that promote autonomous learning, a concern arose in adapting educational activities to each student's individual needs. To study the adaptability of each EER, we found the EER design framework essential to increase the student experience by promoting the consolidation of knowledge through narrative and level design. The results of our study show evidence of progress in students' performance while playing an EER, revealing that students' learning can be effective. Research on Procedural Content Generation (PCG) highlighted how important it is to implement adaptability in future studies of EERs. However, we found some limitations regarding the process of evaluating learning through the EERs, showing how important it is to study and implement learning analytics in future studies in this field. © 2022 Dechema e.V.. All rights reserved.

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.