The use of mobile technologies is reshaping how to teach and learn. In this paper the authors describe their research on the use of these technologies to teach physics. On the one hand they develop mobile applications to complement the traditional learning and to help students learn anytime and anywhere. The use of this applications has proved to have very positive influence on the students' engagement. On the other hand, they use smartphones as measurement devices in physics experiments. This opens the possibility of designing and developing low cost laboratories where expensive material can be substituted by smartphones. The smartphones' sensors are reliable and accurate enough to permit good measurements. However, as it is shown with some examples, special care must be taken here if one does not know how these apps used to access the sensors' data are programmed.

Context has long been considered very useful to help the user assess the actual relevance of a document. In web searching, context can help assess the relevance of a web page by showing how the page is related to other pages in the same web site, for example. Such information is very difficult to convey and visualize in a user friendly way. In this paper we present the design, implementation and evaluation of a graphical visualization tool aimed at helping users to determine the relevance of a web page by displaying the structure of the web site the page belongs to. The results of an initial evaluation suggest that this visualization technique helps the user navigate large web sites and find useful information in an effective way, without increasing the cognitive load of the user.

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).