Formazione, apprendimento e nuove prospettive e-learning. Lo sviluppo delle nuove tecnologie didattiche e la creazione di sistemi di apprendimento lungo l’intero arco della vita

The thesis of this paper is based on the assumption that the socio-economic system in which we are living is characterised by three great trends: growing attention to the promotion of human capital; extremely rapid technological progress, based above all on the information and communication technologies (ICT); the establishment of new production and organizational set-ups. These transformation processes pose a concrete challenge to the training sector, which is called to satisfy the demand for new skills that need to be developed and disseminated. Hence the growing interest that the various training sub-systems devote to the issues of lifelong learning and distance learning. In such a context, the so-called e-learning acquires a central role. The first chapter proposes a reference theoretical framework for the transformations that are shaping post-industrial society. It analyzes some key issues such as: how work is changing, the evolution of organizational set-ups and the introduction of learning organization, the advent of the knowledge society and of knowledge companies, the innovation of training processes, and the key role of ICT in the new training and learning systems. The second chapter focuses on the topic of e-learning as an effective training model in response to the need for constant learning that is emerging in the knowledge society. This chapter starts with a reflection on the importance of lifelong learning and introduces the key arguments of this thesis, i.e. distance learning (DL) and the didactic methodology called e-learning. It goes on with an analysis of the various theoretic and technical aspects of e-learning. In particular, it delves into the theme of e-learning as an integrated and constant training environment, characterized by customized programmes and collaborative learning, didactic assistance and constant monitoring of the results. Thus, all the aspects of e-learning are taken into exam: the actors and the new professionals, the virtual communities as learning subjects, the organization of contents in learning objects, the conformity to international standards, the integrated platforms and so on. The third chapter, which concludes the theoretic-interpretative part, starts with a short presentation of the state-of-the-art e-learning international market that aims to understand its peculiarities and its current trends. Finally, we focus on some important regulation aspects related to the strong impulse given by the European Commission first, and by the Italian governments secondly, to the development and diffusion of e-learning. The second part of the thesis (chapters 4, 5 and 6) focus on field research, which aims to define the Italian scenario for e-learning. In particular, we have examined some key topics such as: the challenges of training and the instruments to face such challenges; the new didactic methods and technologies for lifelong learning; the level of diffusion of e-learning in Italy; the relation between classroom training and online training; the main factors of success as well as the most critical aspects of the introduction of e-learning in the various learning environments. As far as the methodological aspects are concerned, we have favoured a qualitative and quantitative analysis. A background analysis has been done to collect the statistical data available on this topic, as well as the research previously carried out in this area. The main source of data is constituted by the results of the Observatory on e-learning of Aitech-Assinform, which covers the 2000s and four areas of implementation (firms, public administration, universities, school): the thesis has reviewed the results of the last three available surveys, offering a comparative interpretation of them. We have then carried out an in-depth empirical examination of two case studies, which have been selected by virtue of the excellence they have achieved and can therefore be considered advanced and emblematic experiences (a large firm and a Graduate School).

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

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.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Ambienti innovativi per l'apprendimento: Modelli interpretativi e contributi di esperienze. Uno studio sull'organizzazione di Scuola-Città Pestalozzi a Firenze

La tesi affronta il tema dell'innovazione della scuola, oggetto di costante attenzione da parte delle organizzazioni internazionali e dei sistemi educativi nazionali, per le sue implicazioni economiche, sociali e politiche, e intende portare un contributo allo studio sistematico e analitico dei progetti e delle esperienze di innovazione complessiva dell'ambiente di apprendimento. Il concetto di ambiente di apprendimento viene approfondito nelle diverse prospettive di riferimento, con specifica attenzione al framework del progetto "Innovative Learning Environments" [ILE], dell’Organisation For Economic And Cultural Development [OECD] che, con una prospettiva dichiaratamente olistica, individua nel dispositivo dell’ambiente di apprendimento la chiave per l’innovazione dell’istruzione nella direzione delle competenze per il ventunesimo Secolo. I criteri presenti nel quadro di riferimento del progetto sono stati utilizzati per un’analisi dell’esperienza proposta come caso di studio, Scuola-Città Pestalozzi a Firenze, presa in esame perché nell’anno scolastico 2011/2012 ha messo in pratica appunto un “disegno” di trasformazione dell’ambiente di apprendimento e in particolare dei caratteri del tempo/scuola. La ricerca, condotta con una metodologia qualitativa, è stata orientata a far emergere le interpretazioni dei protagonisti dell’innovazione indagata: dall’analisi del progetto e di tutta la documentazione fornita dalla scuola è scaturita la traccia per un focus-group esplorativo attraverso il quale sono stati selezionati i temi per le interviste semistrutturate rivolte ai docenti (scuola primaria e scuola secondaria di primo grado). Per quanto concerne l’interpretazione dei risultati, le trascrizioni delle interviste sono state analizzate con un approccio fenomenografico, attraverso l’individuazione di unità testuali logicamente connesse a categorie concettuali pertinenti. L’analisi dei materiali empirici ha permesso di enucleare categorie interpretative rispetto alla natura e agli scopi delle esperienze di insegnamento/apprendimento, al processo organizzativo, alla sostenibilità. Tra le implicazioni della ricerca si ritengono particolarmente rilevanti quelle relative alla funzione docente.

L'utilizzo dei casi di studio per favorire l'apprendimento di metodologie e strumenti per lo sviluppo organizzativo o di percorsi di cura nelle aziende sanitarie

La formazione, in ambito sanitario, è considerata una grande leva di orientamento dei comportamenti, ma la metodologia tradizionale di formazione frontale non è la più efficace, in particolare nella formazione continua o “long-life education”. L’obiettivo primario della tesi è verificare se l’utilizzo della metodologia dello “studio di caso”, di norma utilizzata nella ricerca empirica, può favorire, nel personale sanitario, l’apprendimento di metodi e strumenti di tipo organizzativo-gestionale, partendo dalla descrizione di processi, decisioni, risultati conseguiti in contesti reali. Sono stati progettati e realizzati 4 studi di caso con metodologia descrittiva, tre nell’Azienda USL di Piacenza e uno nell’Azienda USL di Bologna, con oggetti di studio differenti: la continuità di cura in una coorte di pazienti con stroke e l’utilizzo di strumenti di monitoraggio delle condizioni di autonomia; l’adozione di un approccio “patient-centred” nella presa in carico domiciliare di una persona con BPCO e il suo caregiver; la percezione che caregiver e Medici di Medicina Generale o altri professionisti hanno della rete aziendale Demenze e Alzheimer; la ricaduta della formazione di Pediatri di Libera Scelta sull’attività clinica. I casi di studio sono stati corredati da note di indirizzo per i docenti e sono stati sottoposti a quattro referee per la valutazione dei contenuti e della metodologia. Il secondo caso è stato somministrato a 130 professionisti sanitari all’interno di percorso di valutazione delle competenze e dei potenziali realizzato nell’AUSL di Bologna. I referee hanno commentato i casi e gli strumenti di lettura organizzativa, sottolineando la fruibilità, approvando la metodologia utilizzata, la coniugazione tra ambiti clinico-assistenziali e organizzativi, e le teaching note. Alla fine di ogni caso è presente la valutazione di ogni referee.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Studio longitudinale sulle capacità cognitive del cane: discriminazione di quantità ed apprendimento per imitazione in relazione all'attaccamento

Il presente studio ha indagato e valutato alcune abilità cognitive del cane: la capacità di discriminare quantità e le capacità di apprendimento mediante imitazione; quest’ultima è poi stata messa in relazione con l’attaccamento nei confronti del proprietario. Per l’esecuzione della prima indagine sono stati messi appunto due test: il primo si è basato esclusivamente sulla presentazione di uno stimolo visivo: diversi quantitativi di cibo, differenti tra loro del 50%, sono stati presentati al cane; la scelta effettuata dai soggetti testati è stata premiata con differenti tipi di rinforzo differenziale o non differenziale. Il secondo test è stato diviso in due parti: sono stati presentati al cane diversi quantitativi di cibo sempre differenti tra loro del 50% ma nella prima parte del test l’input sensoriale per il cane è stato esclusivamente uditivo mentre nella seconda parte è stato sia uditivo che visivo. Ove è stato possibile è stato applicato ai cani un cardiofrequenzimetro al fine di eseguire una valutazione delle variazioni della frequenza cardiaca nel corso del test. Lo scopo è stato quello di valutare se i soggetti testati erano in grado di discriminare la quantità maggiore. La seconda indagine ha analizzato le capacità di apprendimento di 36 soggetti che sono stati suddivisi in cani da lavoro e pet. I soggetti protagonisti dello studio hanno eseguito il Mirror Test per la valutazione dell’apprendimento per imitazione. I soggetti presi in considerazione, sono stati sottoposti a scansione termografica all’inizio ed al termine del test ed è stata rilevata la loro frequenza respiratoria nella fase iniziale e finale del test. In 11 soggetti che hanno eseguito il precedente test è stato possibile eseguire anche il Strange Situation Test per la valutazione dell’attaccamento al proprietario; i test in questione sono stati videoregistrati ed analizzati per mezzo di un software preposto (OBSERVER XT 10).

Efficacia e complicanze durante la curva di apprendimento della Thulep: risultati a breve e medio termine delle prime 48 procedure

MATERIALI E METODI: Tra il 2012 e il 2013, abbiamo analizzato in uno studio prospettico i dati di 48 pazienti sottoposti a ThuLEP con approccio autodidatta. I pazienti sono stati rivalutati a 3, 6, 12 e 24 mesi con la valutazione del PSA, il residuo post-minzionale (RPM), l'uroflussometria (Qmax), l'ecografia transrettale e questionari validati (IPSS: international prostate symptom score e QoL: quality of life) RISULTATI: Il volume medio della prostata è di 63 ± 5,3 ml. Il tempo operatorio medio è stato di 127,58 ± 28.50 minuti. Il peso medio del tessuto asportato è stato di 30,40 ± 13,90 gr. A 6 mesi dopo l'intervento l'RPM medio è diminuito da 165,13 ± 80,15 ml a 7,78 ± 29.19 ml, mentre il Qmax medio è aumentato da 5.75 ± 1.67ml / s a 18.1 ± 5.27 ml / s. I valori medi dei IPSS e QoL hanno dimostrato un progressivo miglioramento: da 19.15 (IQR: 2-31) e 4 (IQR: 1-6) nel preoperatorio a 6.04 (IQR: 1-20) e 1.13 (IQR: 1-4), rispettivamente. Durante la curva di apprendimento si è assistito ad un progressivo aumento del peso del tessuto enucleato e ad una progressiva riduzione del tempo di ospedalizzazione e di cateterismo. Tra le principali complicanze ricordiamo un tasso di incontinenza transitoria del 12,5% a 3 mesi e del 2.1% a 12 mesi. CONCLUSIONI: ThuLEP rappresenta una tecnica chirurgica efficace, sicura e riproducibile indipendentemente dalle dimensioni della prostata. I nostri dati suggeriscono che la ThuLEP offre un miglioramento significativo dei parametri funzionali comparabili con le tecniche tradizionali, nonostante una lunga curva di apprendimento.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.