Thanks to the emerging technologies of e-Learning, the uses that make the students of digital resources can be registered in data repositories. By analyzing these data, it can be determined how students acquire knowledge and at what pace. Therefore, those responsible for the educational process can have the opportunity to proactively identify the learning needs of their students and, if any difficulty is detected, it can be corrected in time.


The client has asked us to develop a predictive model that allows to extract patterns of student network usage data in order to predict their future behavior and make personalized recommendations. In addition, using this tool, the teaching team of the school, can have some risk indicators, very valuable when it comes to accompanying the students in their specific needs, keeping them motivated and helping them. They will improve their academic performance.


The use of Big Data in the e-Learning space, is becoming increasingly common. Thanks to the benefits that data analysis offers teachers, it allows to revolutionize how to analyze and evaluate the learning experience. Teachers can visualize more comfortably how students are acquiring new concepts, and which topics they attract more. This visualization can also help them identify the issues in which they may need further reinforcement. In addition, the agility in which these data can be accessed allows the teaching team to introduce possible changes as soon as possible. The sum of these advantages makes the teaching team able to develop content and strategies so that students can obtain the best possible results.

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Development of a leather classification system by means of artificial vision

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DataScience@UB designs and prototype a machine that includes an intelligent vision system based on semantic segmentation through convolutional neural networks to automate the classification of skins and leather.

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We have won a new competition with the Cartographic and Geological Institute of Catalonia, to apply deep learning techniques in the detection of changes in the network of infrastructures and constructions in the territory with the aim of automating the process of elaboration of the Topographic map of Catalonia.