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.

The power of language and the language of power in M. T. Anderson’s Feed and Gary Stheyngart’s Super Sad True Love Story. A comparative analysis

The aim of my dissertation is to analyze how selected elements of language are addressed in two contemporary dystopias, Feed by M. T. Anderson (2002) and Super Sad True Love Story by Gary Shteyngart (2010). I chose these two novels because language plays a key role in both of them: both are primarily focused on the pervasiveness of technology, and on how the use/abuse of technology affects language in all its forms. In particular, I examine four key aspects of language: books, literacy, diary writing, as well as oral language. In order to analyze how the aforementioned elements of language are dealt with in Feed and Super Sad True Love Story, I consider how the same aspects of language are presented in a sample of classical dystopias selected as benchmarks: We by Yevgeny Zamyatin (1921), Brave New World by Aldous Huxley (1932), Animal Farm (1945) and Nineteen Eighty-Four (1949) by George Orwell, Fahrenheit 451 by Ray Bradbury (1952), and The Handmaid's Tale by Margaret Atwood (1986). In this way, I look at how language, books, literacy, and diaries are dealt with in Anderson’s Feed and in Shteyngart’s Super Sad True Love Story, both in comparison with the classical dystopias as well as with one another. This allows for an analysis of the similarities, as well as the differences, between the two novels. The comparative analysis carried out also takes into account the fact that the two contemporary dystopias have different target audiences: one is for young adults (Feed), whereas the other is for adults (Super Sad True Love Story). Consequently, I also consider whether further differences related to target readers affect differences in how language is dealt with. Preliminary findings indicate that, despite their different target audiences, the linguistic elements considered are addressed in the two novels in similar ways.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Audiovisual materials and second-language acquisition: a comparison between native language subtitles and target language captions.

Over the last few years, the massive popularity of video streaming platforms has managed to impact our daily habits by making the watching of movies and TV shows one of the main activities of our free time. By providing a wide range of foreign language audiovisual content, these entertainment services may represent a powerful resource for language learners, as they provide them with the possibility to be exposed to authentic input. Moreover, research has shown the beneficial role of audiovisual textual aids such as native language subtitles and target language captions in enhancing language skills such as vocabulary and listening comprehension. The aim of this thesis is to analyze the existing literature on the subject of subtitled and captioned audiovisual materials used as a pedagogical tool for informal language learning.

Il child language brokering a Ravenna e provincia: la parola agli studenti e ai professori

The main aim of this study is to provide a description of the phenomenon defined as Child Language Brokering (CLB), a common practice among language minority communities but which has received less attention in the academic literature. As the children of immigrants often learn the host language much more quickly than their parents, they contribute to family life by acting as language and cultural mediators between a family members and different language speakers. Many immigrant families prefer a language broker from within their own family to an external mediator or interpreter, even though there is a well-found resistance to the use of these young interpreters by professionals. In this study I report some findings from surveys of teachers in schools in Ravenna where there has been some use of students as CLBs and of students who have acted or are still acting as mediators for their families in different contexts, not only while at school. This dissertation is divided into five chapters. Chapter one aims at providing an overview of recent migration to Italy and of the differences between first-generation immigrants and second-generation immigrants. The chapter also discusses the available professional interpreting facilities provided by the municipality of Ravenna. Chapter two presents an overview of the literature on child language brokering. Chapter three provides a description of the methodology used in order to analyze the data collected. Chapter four contains a detailed analysis of the questionnaires administered to the students and the interviews submitted to the teachers in four schools in Ravenna. Chapter five focuses on the studies carried out by the researchers of the Thomas Coram Research Unit and University College London and draws a general comparison between their findings from on-line surveys of teachers in schools and my own findings on teachers’ points of view. The results of this study demonstrate that CLB is a common practice among immigrant children living in Ravenna and, although almost all students reported positive appreciation, further work is still needed to assess the impact of this phenomenon.

Aviation English: Structure and Importance of an International Airborne Language

In today’s globalized world, air travel is one of the fastest growing markets. Millions of aircrafts take off and then touch down all around the world each day. This well-synchronized symphony, however, is much more complex than it seems, and communication – language - plays a crucial role during a plane’s journey. Misunderstandings and miscommunications can have disastrous effects, so the adoption of a standard phraseology to be used during flight is a means to overcome language barriers, avoid ambiguous expressions and guarantee a safe and effective operation of an aircraft. Little is known about the interaction that goes on between pilots and air traffic controllers (ATCOs), and even though the language of aviation is English, cockpit communication can be hard to understand for people who are not familiar with this specific language. The scope of this thesis is to examine the origins of this uncommon language, the characteristics and peculiarities of air communication and to shed a little light on this mystery called Aviation English.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

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.

Google Translate e Microsoft Translator - Valutazione di due applicazioni per la traduzione automatica del parlato e analisi di una tecnologia in evoluzione.

This work focuses on Machine Translation (MT) and Speech-to-Speech Translation, two emerging technologies that allow users to automatically translate written and spoken texts. The first part of this work provides a theoretical framework for the evaluation of Google Translate and Microsoft Translator, which is at the core of this study. Chapter one focuses on Machine Translation, providing a definition of this technology and glimpses of its history. In this chapter we will also learn how MT works, who uses it, for what purpose, what its pros and cons are, and how machine translation quality can be defined and assessed. Chapter two deals with Speech-to-Speech Translation by focusing on its history, characteristics and operation, potential uses and limits deriving from the intrinsic difficulty of translating spoken language. After describing the future prospects for SST, the final part of this chapter focuses on the quality assessment of Speech-to-Speech Translation applications. The last part of this dissertation describes the evaluation test carried out on Google Translate and Microsoft Translator, two mobile translation apps also providing a Speech-to-Speech Translation service. Chapter three illustrates the objectives, the research questions, the participants, the methodology and the elaboration of the questionnaires used to collect data. The collected data and the results of the evaluation of the automatic speech recognition subsystem and the language translation subsystem are presented in chapter four and finally analysed and compared in chapter five, which provides a general description of the performance of the evaluated apps and possible explanations for each set of results. In the final part of this work suggestions are made for future research and reflections on the usability and usefulness of the evaluated translation apps are provided.