Content and English Language Integrated Learning: The use of Moodle in Aircraft Maintenance Training Programs

The aim of this work is to develop a prototype of an e-learning environment that can foster Content and Language Integrated Learning (CLIL) for students enrolled in an aircraft maintenance training program, which allows them to obtain a license valid in all EU member states. Background research is conducted to retrace the evolution of the field of educational technology, analyzing different learning theories – behaviorism, cognitivism, and (socio-)constructivism – and reflecting on how technology and its use in educational contexts has changed over time. Particular attention is given to technologies that have been used and proved effective in Computer Assisted Language Learning (CALL). Based on the background research and on students’ learning objectives, i.e. learning highly specialized contents and aeronautical technical English, a bilingual approach is chosen, three main tools are identified – a hypertextbook, an exercise creation activity, and a discussion forum – and the learning management system Moodle is chosen as delivery medium. The hypertextbook is based on the technical textbook written in English students already use. In order to foster text comprehension, the hypertextbook is enriched by hyperlinks and tooltips. Hyperlinks redirect students to webpages containing additional information both in English and in Italian, while tooltips show Italian equivalents of English technical terms. The exercise creation activity and the discussion forum foster interaction and collaboration among students, according to socio-constructivist principles. In the exercise creation activity, students collaboratively create a workbook, which allow them to deeply analyze and master the contents of the hypertextbook and at the same time create a learning tool that can help them, as well as future students, to enhance learning. In the discussion forum students can discuss their individual issues, content-related, English-related or e-learning environment-related, helping one other and offering instructors suggestions on how to improve both the hypertextbook and the workbook based on their needs.

Portare la tecnologia in cabina: le nuove tecnologie a servizio dell'interprete e il caso della simultanea con testo

The digital revolution has affected all aspects of human life, and interpreting is no exception. This study will provide an overview of the technology tools available to the interpreter, but it will focus more on simultaneous interpretation, particularly on the “simultaneous interpretation with text” method. The decision to analyse this particular method arose after a two-day experience at the Court of Justice of the European Union (CJEU), during research for my previous Master’s dissertation. During those days, I noticed that interpreters were using "simultaneous interpretation with text" on a daily basis. Owing to the efforts and processes this method entails, this dissertation will aim at discovering whether technology can help interpreters, and if so, how. The first part of the study will describe the “simultaneous with text” approach, and how it is used at the CJEU; the data provided by a survey for professional interpreters will describe its use in other interpreting situations. The study will then describe Computer-Assisted Language Learning technologies (CALL) and technologies for interpreters. The second part of the study will focus on the interpreting booth, which represents the first application of the technology in the interpreting field, as well as on the technologies that can be used inside the booth: programs, tablets and apps. The dissertation will then analyse the programs which might best help the interpreter in "simultaneous with text" mode, before providing some proposals for further software upgrades. In order to give a practical description of the possible upgrades, the domain of “judicial cooperation in criminal matters” will be taken as an example. Finally, after a brief overview of other applications of technology in the interpreting field (i.e. videoconferencing, remote interpreting), the conclusions will summarize the results provided by the study and offer some final reflections on the teaching of interpreting.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.