Studio di ausili tecnologici compensativi di supporto all'iter di apprendimento matematico secondario e universitario degli studenti con dislessia

Data coming out from various researches carried out over the last years in Italy on the problem of school dispersion in secondary school show that difficulty in studying mathematics is one of the most frequent reasons of discomfort reported by students. Nevertheless, it is definitely unrealistic to think we can do without such knowledge in today society: mathematics is largely taught in secondary school and it is not confined within technical-scientific courses only. It is reasonable to say that, although students may choose academic courses that are, apparently, far away from mathematics, all students will have to come to terms, sooner or later in their life, with this subject. Among the reasons of discomfort given by the study of mathematics, some mention the very nature of this subject and in particular the complex symbolic language through which it is expressed. In fact, mathematics is a multimodal system composed by oral and written verbal texts, symbol expressions, such as formulae and equations, figures and graphs. For this, the study of mathematics represents a real challenge to those who suffer from dyslexia: this is a constitutional condition limiting people performances in relation to the activities of reading and writing and, in particular, to the study of mathematical contents. Here the difficulties in working with verbal and symbolic codes entail, in turn, difficulties in the comprehension of texts from which to deduce operations that, once combined together, would lead to the problem final solution. Information technologies may support this learning disorder effectively. However, these tools have some implementation limits, restricting their use in the study of scientific subjects. Vocal synthesis word processors are currently used to compensate difficulties in reading within the area of classical studies, but they are not used within the area of mathematics. This is because the vocal synthesis (or we should say the screen reader supporting it) is not able to interpret all that is not textual, such as symbols, images and graphs. The DISMATH software, which is the subject of this project, would allow dyslexic users to read technical-scientific documents with the help of a vocal synthesis, to understand the spatial structure of formulae and matrixes, to write documents with a technical-scientific content in a format that is compatible with main scientific editors. The system uses LaTex, a text mathematic language, as mediation system. It is set up as LaTex editor, whose graphic interface, in line with main commercial products, offers some additional specific functions with the capability to support the needs of users who are not able to manage verbal and symbolic codes on their own. LaTex is translated in real time into a standard symbolic language and it is read by vocal synthesis in natural language, in order to increase, through the bimodal representation, the ability to process information. The understanding of the mathematic formula through its reading is made possible by the deconstruction of the formula itself and its “tree” representation, so allowing to identify the logical elements composing it. Users, even without knowing LaTex language, are able to write whatever scientific document they need: in fact the symbolic elements are recalled by proper menus and automatically translated by the software managing the correct syntax. The final aim of the project, therefore, is to implement an editor enabling dyslexic people (but not only them) to manage mathematic formulae effectively, through the integration of different software tools, so allowing a better teacher/learner interaction too.

Per una semiotica evolutiva dell'apprendimento. Produzione segnica, abduzione ed exaptation nell'accesso alla literacy

Con questa ricerca si intende costruire una semiotica specifica che sia in grado di fare luce sui processi di apprendimento della lettoscrittura durante l’età evolutiva. È un campo di ricerca a cui hanno contribuito numerose discipline: la psicologia e le neuroscienze trattano la lettoscrittura come uno stato cognitivo a cui l’essere umano accede nel corso dello sviluppo individuale, mentre l’archeologia cognitiva e la linguistica considerano lo stesso fenomeno dal punto di vista della filogenesi culturale. Queste stesse discipline possono essere distinte in due categorie a seconda dell’adozione di una prospettiva internalista, in cui lettura e scrittura sono rappresentate come attività compiute dal cervello e dai neuroni, o di una prospettiva distribuita, in cui si tratta di studiare l’evoluzione e la presa in carico delle forme materiali della lingua scritta. Gli strumenti di una semiotica interpretativa e cognitiva consentono di mediare e tradurre tra queste prospettive differenti e rendere ragione del modo in cui l’apprendimento di una pratica culturale socialmente regolata e costruita a partire da forme materiali disponibili, produce profonde modificazioni a livello neurofisiologico, nei vincoli di un’architettura cerebrale che - per quanto plastica - pone divieti e passaggi obbligati. Questa ricerca propone un ruolo centrale della produzione segnica e dell’inferenza abduttiva nei processi di apprendimento, nel processo di acquisizione delle competenze fondamentali dell’emergent literacy (la scoperta del fonema e la phonemic awareness) e, conseguentemente, nei processi di riciclaggio ed exaptation che si danno a livello neurofisiologico.

Challenges and Opportunities of Machine Learning for Clinical and Omics Data

Clinical and omics data are a promising field of application for machine learning techniques even though these methods are not yet systematically adopted in healthcare institutions. Despite artificial intelligence has proved successful in terms of prediction of pathologies or identification of their causes, the systematic adoption of these techniques still presents challenging issues due to the peculiarities of the analysed data. The aim of this thesis is to apply machine learning algorithms to both clinical and omics data sets in order to predict a patient's state of health and get better insights on the possible causes of the analysed diseases. In doing so, many of the arising issues when working with medical data will be discussed while possible solutions will be proposed to make machine learning provide feasible results and possibly become an effective and reliable support tool for healthcare systems.