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Engineering students need to practice and carry out experiments in laboratories to complement their learning process. However, instructors and equipment are not always available. Additionally, there may be risk and trouble using some equipment that may hurt students or damage the equipment. A generic architecture based on probabilistic relational models to(More)
To ensure learning, game-based learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner's emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the(More)
Game-based learning offers key advantages for learning through experience in conjunction with offering multi-sensorial and engaging communication. However, ensuring that learning has taken place is the ultimate challenge. Intelligent Tutoring Systems (ITSs) have been incorporated into game-based learning environments to guide learners' exploration. Emotions(More)
We have developed an intelligent tutoring system coupled with a virtual laboratory, which constitute a semi-open learning environment. This environment provides the student with the opportunity to learn through exploration within a virtual laboratory, while achieving the expected learning objectives. The key element of this environment is a novel(More)
Emotions have been identified as important players in motivation, and motivation is very important for learning. When a tutor recognizes the affective state of the student and responds accordingly, the tutor may be able to motivate students and improve the learning process. We propose a general affective behavior model which integrates information from the(More)
We are combining collaborative didactic techniques, virtual laboratories and intelligent tutors to improve the process of learning mobile robotics. The students learn the basic concepts in mobile robotics, first experimenting in a virtual laboratory, and later by building a small mobile robot for a competition. The guiding thread for the course is based on(More)
In this paper we address the problem of explaining the recommendations generated by a Markov decision process (MDP). We propose an automatic explanation generation mechanism that is composed by two main stages. In the first stage, the most relevant variable given the current state is obtained, based on a factored representation of the MDP. The relevant(More)
An important requirement for intelligent assistants is to have an explanation generation mechanism, so that the trainee has a better understanding of the recommended actions and can generalize them to similar situations. In this work we combine different knowledge sources to generate explanations for operator training. The explanations are based on a(More)