Abstract
Model-based learning is a teaching methodology that facilitates the learning process through the construction of models, which represent the conceptual models taught in geosciences lessons, promoting the construction of students’ scientific knowledge and the development of a meaningful learning. It is crucial that teachers know how to apply it in schools in order to support students’ learning process, but also because models are important tools for dissemination of science concepts. Having this in mind, it becomes relevant, beyond the analysis of its importance for both teaching and disseminating geosciences in Portuguese high schools, to provide some guidelines and recommendations about the use of models in geosciences teaching, based on the literature, seeking to prepare teachers to apply the methodology in science lessons and for making them more informed about the importance of dissemination of science. To achieve this purpose, the attitudes of Portuguese high school students towards the importance of model-based learning in teaching and disseminating the dependence of earthquakes effects on soils and buildings were analysed. The data were collected through a scale for model evaluation named Seismological Models’ Evaluation Scale (SMES), applied to 126 students who participated in Faculty of Sciences’ Open Days to Schools. This instrument was validated by two experts in geosciences teaching, and its fidelity was also determined. © Springer International Publishing Switzerland 2016.

Abstract
In this paper we introduce three main features extracted from Moodle logs in order to be uses a possible means to predict future student grades. We discuss the statistical analysis on these features and show how they cannot be applied isolatedly to model our data. We then apply them as a whole and use principal component analysis to derive a decision tree based on the features. With derived tree we are able to predict grades in three intervals, namely to predict failures. Our proposed analysis methodology can be incorporated in an LMS and be used during a course. As the course unfolds, the system can to trigger alarms regarding possible failure situations.

Abstract
The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale. © Springer International Publishing Switzerland 2016.

Abstract
Assistive technology enables people to achieve independence when performing daily tasks and it enhances their overall quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of sufficient information about their surrounding environment. With recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities in terms of their mobility. In this context we present and describe a wearable system (Blavigator project), whose global objective is to assist visually impaired people in their navigation on indoor and outdoor environments. This paper is focused mainly on the Computer Vision module of the Blavigator prototype. We propose an object collision detection algorithm based on stereo vision. The proposed algorithm uses Peano-Hilbert Ensemble Empirical Mode Decomposition (PHEEMD) for disparity image processing and a two layer disparity image segmentation to detect nearby objects. Using the adaptive ensemble empirical mode decomposition (EEMD) image analysis real time is not achieved, with PH-EEMD results on a fast implementation suitable for real time applications.

Abstract
We introduce in this paper visibility maps for robots of any shape, representing the reachability limit of the robot's motion and sensing in a 2D gridmap with obstacles. The brute-force approach to determine the optimal visibility map is computationally expensive, and prohibitive with dynamic obstacles. We contribute the Robot-Dependent Visibility Map (RDVM) as a close approximation to the optimal, and an effective algorithm to compute it. The RDVM is a function of the robot's shape, initial position, and sensor model. We first overview the computation of RDVM for the circular robot case in terms of the partial morphological closing operation and the optimal choice for the critical points position. We then present how the RDVM for any-shape robots is computed. In order to handle any robot shape, we introduce in the first step multiple layers that discretize the robot orientation. In the second step, our algorithm determines the frontiers of actuation, similarly to the case of the the circular robot case. We then derive the concept of critical points to the any-shape robot, as the points that maximize expected visibility inside unreachable regions. We compare our method with the ground-truth in a simulated map compiled to capture a variety of challenges of obstacle distribution and type, and discuss the accuracy of our approximation to the optimal visibility map.

Abstract
There is a trend towards the use of Virtual Reality (VR) environments and its evolution has promoted new interaction approaches so there is a need for studying a number of factors that can have impact on its usability. This paper studies the impact of the body position on the usability of VR environments. For the effect, a case study was undertaken based on a bicycle ride that considers two body positions: riding the bicycle seated with the feet on the pedals and hands in the handlebar; and standing with the feet on the ground and the hands on the handlebar. On both cases they had control over the bicycle (steer and brakes). These two body positions were considered as they will allow studying in detail the impact of the different body positions: the first condition mimics the real body position of the depicted scenario while the second condition tests an alternate body position. Results regarding the system's effectiveness have shown an 100% success rate as all participants concluded the task successfully and there were no dropouts. The efficiency results have revealed that the more the participants used the VE the less the number of errors they made and that the completion time differences between the tested conditions were insignificant (> 0.5 seconds). As for satisfaction, participants reported a preference towards the standing position. Furthermore, results reveal that body position has impact on the users' performance but it does not necessarily affect their satisfaction over the virtual experience. © 2016 ACM.