Fallbasiertes E-Learning durch dynamische Verknüpfung von Fallstudien und Fachinhalten. Neue Diskussionsansätze zu einem vernachlässigten Konzept

Der Einsatz von Fallstudien kann als wichtiges Bindeglied zur Verknüpfung von Theorie und Praxis betrachtet werden. Fallstudien ermöglichen die Anwendung theoretischen Grundlagenwissens und die Entwicklung überfachlicher Kompetenzen. Damit können sie einen wichtigen Beitrag zur beruflichen Handlungskompetenz genau dort leisten, wo praktische Erfahrungen im Rahmen der Aus-und Weiterbildung nicht möglich sind. Der Einsatz von Fallstudien sollte aus diesem Grund nicht nur den „klassischen“ Anwendungsdisziplinen wie den Rechtswissenschaften, der Betriebswirtschaftslehre oder der Psychologie vorbehalten sein. Auch im Bereich der Informatik können sie eine wichtige Ergänzung zu den bisher eingesetzten Methoden darstellen. Das im Kontext des Projekts New Economy1 entwickelte und hier vorgestellte Konzept zur didaktischen und technischen Aufbereitung von Fallstudien am Beispiel der IT-Aus- und Weiterbildung soll diese Diskussion anregen. Mit Hilfe des vorgestellten Ansatzes ist es möglich, unterschiedliche methodische Zugänge zu einer Fallstudie für eine computerbasierte Präsentation automatisch zu generieren und mit fachlichen Inhalten zu verknüpfen. Damit ist ein entscheidender Mehrwert gegenüber den bisherigen statischen und in sich geschlossenen Darstellungen gegeben. Der damit zu erreichende Qualitätssprung im Einsatz von Fallstudien in der universitären und betrieblichen Aus- und Weiterbildung stellt einen wichtigen Beitrag zur praxisorientierten Gestaltung von Blended Learning-Ansätzen dar.(DIPF/Orig.)

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Computerized Medical Diagnosis of Melanocytic Lesions based on the ABCD approach

Melanoma is a type of skin cancer and is caused by the uncontrolled growth of atypical melanocytes. In recent decades, computer aided diagnosis is used to support medical professionals; however, there is still no globally accepted tool. In this context, similar to state-of-the-art we propose a system that receives a dermatoscopy image and provides a diagnostic if the lesion is benign or malignant. This tool is composed with next modules: Preprocessing, Segmentation, Feature Extraction, and Classification. Preprocessing involves the removal of hairs. Segmentation is to isolate the lesion. Feature extraction is considering the ABCD dermoscopy rule. The classification is performed by the Support Vector Machine. Experimental evidence indicates that the proposal has 90.63 % accuracy, 95 % sensitivity, and 83.33 % specificity on a data-set of 104 dermatoscopy images. These results are favorable considering the performance of diagnosis by traditional progress in the area of dermatology

Effect of haptic guidance and error amplification robotic training interventions on the immediate improvement of timing among individuals that had a stroke

Abstract : Many individuals that had a stroke have motor impairments such as timing deficits that hinder their ability to complete daily activities like getting dressed. Robotic rehabilitation is an increasingly popular therapeutic avenue in order to improve motor recovery among this population. Yet, most studies have focused on improving the spatial aspect of movement (e.g. reaching), and not the temporal one (e.g. timing). Hence, the main aim of this study was to compare two types of robotic rehabilitation on the immediate improvement of timing accuracy: haptic guidance (HG), which consists of guiding the person to make the correct movement, and thus decreasing his or her movement errors, and error amplification (EA), which consists of increasing the person’s movement errors. The secondary objective consisted of exploring whether the side of the stroke lesion had an effect on timing accuracy following HG and EA training. Thirty-four persons that had a stroke (average age 67 ± 7 years) participated in a single training session of a timing-based task (simulated pinball-like task), where they had to activate a robot at the correct moment to successfully hit targets that were presented a random on a computer screen. Participants were randomly divided into two groups, receiving either HG or EA. During the same session, a baseline phase and a retention phase were given before and after each training, and these phases were compared in order to evaluate and compare the immediate impact of HG and EA on movement timing accuracy. The results showed that HG helped improve the immediate timing accuracy (p=0.03), but not EA (p=0.45). After comparing both trainings, HG was revealed to be superior to EA at improving timing (p=0.04). Furthermore, a significant correlation was found between the side of stroke lesion and the change in timing accuracy following EA (r[subscript pb]=0.7, p=0.001), but not HG (r[subscript pb]=0.18, p=0.24). In other words, a deterioration in timing accuracy was found for participants with a lesion in the left hemisphere that had trained with EA. On the other hand, for the participants having a right-sided stroke lesion, an improvement in timing accuracy was noted following EA. In sum, it seems that HG helps improve the immediate timing accuracy for individuals that had a stroke. Still, the side of the stroke lesion seems to play a part in the participants’ response to training. This remains to be further explored, in addition to the impact of providing more training sessions in order to assess any long-term benefits of HG or EA.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

On the brink of madness: Leituras do tema da loucura na obra pré-heteronímica de Fernando Pessoa

O tema da loucura na obra pessoana tem sido objecto de várias aproximações interpretativas, decorrentes da multiplicidade de textos que fazem eco do fenómeno e da sua persistência enquanto topos ao longo de praticamente toda a sua produção. Na sua acepção literal, manteve constantes interferências com a dimensão empírica do autor sobretudo quando sustentadas na contingência de certos textos basilares, os quais têm vindo a ser questionados quanto ao seu valor estritamente testemunhal. Como fenómeno desde cedo relacionado com a criação artística, a loucura surge neste estudo não apenas na representatividade da condição distintiva do sujeito criador mas ainda como figuração da contingência que envolve qualquer acto criativo que tome a linguagem na sua dimensão essencialmente metafórica e ambígua. Neste sentido, o presente trabalho pretende a abertura de várias vias de leitura do fenómeno através da análise crítica dos vocabulários relativos a diferentes âmbitos e concepções de loucura e a sua relação com o génio (capítulos I e II), focando o seu interesse no que diz respeito a alguma produção pessoana pré-heteronímica, nomeadamente Charles Robert Anon, Alexander Search (capítulo III) e Jean Seul de Méluret (capítulo IV), a que acrescentaremos o Primeiro Fausto (capítulo IV), de modo a conseguirmos estabelecer uma possível relação entre as primeiras experiências de alterização e a descoberta simultânea da irredutibilidade do discurso literário face a tentativas de literalização e de racionalização da linguagem, defendidas por outros modelos (paradigma biologista). A identificação da ambiguidade e da ironia nas suas mais variadas acepções como componentes essenciais da aprendizagem do valor contingente do processo de criação contribuirá tanto para a definição da autonomização da literatura como para a redescrição moderna do sujeito criador, paradoxalmente investido do pathos criativo anunciado no Romantismo e confrontando-se com os seus limites, que corresponderão aos da própria linguagem, situação de crise de que a figuração do louco lúcido será um dos tópicos mais produtivos. ABSTRACT: The theme of madness in the Pessoa work has been the subject of several interpretive approaches, from the multiplicity of texts that echo the phenomenon and its persistence as a topos priority over virtually all its production. ln its physiological sense, remained constant interference with the empirical dimension of the author when sustained in the contingency of certain basic texts that have been questioned as to their strictly testimonial value. As early phenomenon associated with artistic creation, the madness in this study is not only representative in the distinctive condition of the subject creator but also as the contingency figuration involving any creative act to take the language mainly in its essential metaphorical and ambiguous dimension. Accordingly, this work intends to open several analysis manners of the phenomenon through critical analysis of vocabularies for different areas and concepts of madness and its special relationship with the genius (Chapters I and II), focusing its interest in respect to some of the previous-heteronomy Pessoa production, including Charles Robert Anon, Alexander Search (Chapter III) and Jean Seul de Méluret (Chapter IV), and the Primeiro Fausto (Chapter IV), among others, to establish a possible relationship between changing early experiences and the discovery simultaneous literary discourse of irreducibility in the face of the attempts of literacy and the rationalization of language defended by other models (biologist paradigm). The identification of the ambiguity and irony in its many different meanings as essential components of learning the contingent value of the creation process which will contribute to the definition of empowerment in the literature as to the redescription of the modem subject creator that paradoxically had the creative pathos announced in Romanticism and from the confrontation with its limits, which will correspond to the language itself, the crisis situation from which the mad-lucid figuration will be one of the most productive topics.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

The murky waters of neoliberal marketization and commodification on the education of adults in the United States

We approach marketization and commodification of adult education from multiple lenses including our personal narratives and neoliberalism juxtaposed against the educational philosophy of the Progressive Period. We argue that adult education occurs in many arenas including the public spaces found in social movements, community-based organizations, and government sponsored programs designed to engage and give voice to all citizens toward building a stronger civil society. We conclude that only when adult education is viewed from the university lens, where it focuses on the individual and not the public good, does it succumb to neoliberal forces. (DIPF/Orig.)

Evaluation of the quality of the teaching-learning process in undergraduate courses in Nursing

Objective: to identify aspects of improvement of the quality of the teaching-learning process through the analysis of tools that evaluated the acquisition of skills by undergraduate students of Nursing. Method: prospective longitudinal study conducted in a population of 60 second-year Nursing students based on registration data, from which quality indicators that evaluate the acquisition of skills were obtained, with descriptive and inferential analysis. Results: nine items were identified and nine learning activities included in the assessment tools that did not reach the established quality indicators (p<0.05). There are statistically significant differences depending on the hospital and clinical practices unit (p<0.05). Conclusion: the analysis of the evaluation tools used in the article "Nursing Care in Welfare Processes" of the analyzed university undergraduate course enabled the detection of the areas for improvement in the teaching-learning process. The challenge of education in nursing is to reach the best clinical research and educational results, in order to provide improvements to the quality of education and health care.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

Research on the BEIC Curriculum: Unveiling What Lies Under the Tip of the Iceberg

Es un detenido análisis de la investigación The Value of Curricular lntrospection, en que se incorpora la realimentación dada por diversos participantes nacionales y extranjeros en el CILAP 2007, a quienes se les expuso los resultados iniciales de esta investigación. El estudio surgió de la disparidad de criterios entre diversos actores del BEIC, de la ELCL, respecto a la pertinencia que para la enseñanza del inglés a niños en Costa Rica tienen los principios comunicativos denominados interacción, inmersión parcial y aprendizaje por experiencia. Así, mientras diseñadores de currículo y profesores del BEIC consideraban estos principios altamente eficaces, buena parte del estudiantado que realizaba la práctica docente pensaba lo contrario.A detailed analysis is provided here of the research project titled The Value of Curricular Introspection. It also includes the feedback given by the national and foreign participants in CILAP 2007, to whom this study was presented. The investigation emerged from the diverse opinions existing among BEIC-ELCL actors regarding the pertinence of interaction, partial immersion and experiential learning communicative principies for the teaching of English to children in Costa Rica. Thus, whereas BEIC curriculum designers and professors considered these principies to be highly effective, many student-teachers in that program believe just the opposite.

A Comparison of the Effects of Classroom and Multi-User Virtual Environments on the Perceived Speaking Anxiety of Adult Post-Secondary English Language Learners

The population of English Language Learners (ELLs) globally has been increasing substantially every year. In the United States alone, adult ELLs are the fastest growing portion of learners in adult education programs (Yang, 2005). There is a significant need to improve the teaching of English to ELLs in the United States and other English-speaking dominant countries. However, for many ELLs, speaking, especially to Native English Speakers (NESs), causes considerable language anxiety, which in turn plays a vital role in hindering their language development and academic progress (Pichette, 2009; Woodrow, 2006). Task-based Language Teaching (TBLT), such as simulation activities, has long been viewed as an effective approach for second-language development. The current advances in technology and rapid emergence of Multi-User Virtual Environments (MUVEs) have provided an opportunity for educators to consider conducting simulations online for ELLs to practice speaking English to NESs. Yet to date, empirical research on the effects of MUVEs on ELLs’ language development and speaking is limited (Garcia-Ruiz, Edwards, & Aquino-Santos, 2007). This study used a true experimental treatment control group repeated measures design to compare the perceived speaking anxiety levels (as measured by an anxiety scale administered per simulation activity) of 11 ELLs (5 in the control group, 6 in the experimental group) when speaking to Native English Speakers (NESs) during 10 simulation activities. Simulations in the control group were done face-to-face, while those in the experimental group were done in the MUVE of Second Life. The results of the repeated measures ANOVA revealed after the Huynh-Feldt epsilon correction, demonstrated for both groups a significant decrease in anxiety levels over time from the first simulation to the tenth and final simulation. When comparing the two groups, the results revealed a statistically significant difference, with the experimental group demonstrating a greater anxiety reduction. These results suggests that language instructors should consider including face-to-face and MUVE simulations with ELLs paired with NESs as part of their language instruction. Future investigations should investigate the use of other multi-user virtual environments and/or measure other dimensions of the ELL/NES interactions.

Student’s Perception on the Personal Impact of a Social Education Degree Internship

The functional profile of the social educator is based on the development of theoretical, technical and personal/relational skills, which should guide training courses organization. Assuming the shortcomings of a merely theoretical approach, besides a consistent preparation in theoretical and essential technical contents for socio-educational intervention, practice in context should be favoured as an opportunity to develop professional skills, together with a critical reflection on the functional profile. This study emerges from the need to reflect and rethink the internship, as well as how the respective supervision is developed, of the degree in social education at the School of Education of the Polytechnic Institute of Viseu, and it is based on the students’ perceptions about the impact of the internship on personal development. This is a qualitative and exploratory study, using the documentary analysis of 50 final internship reports. From the content analysis, four categories emerged referring to gains in terms of acquiring and managing knowledge, development of technical skills, personal and relational development and reinforcement of professional identity. The importance given to personal and relational development should be noted (41.4% of mentions) taking into account its relevance in constructing a professional identity. Findings on the technical skills and on the increase of profession knowledge, also by mobilising theoretical training, positively reinforce the internship model that is based on a proximity supervision approach and on a dialogical perspective of the professional learning.

Motivation and relationship of the student with the school as factors involved in the perceived learning

Motivation should be seen as a very important factor in the learning process. The motivated student has the inner strength to learn, to discover and capitalize on capabilities, to improve academic performance and to adapt to the demands of the school context. Contextual factors like the psychological sense of school membership may be also especially important to students’ classroom engagement, their motivation and learning success. So with this study we intend to examine how the sense of school belonging and intrinsic motivation influences perceived learning.A structural model reveals that the negative sense of school belonging has a negative impact on intrinsic motivation and on perceived learning. In turn, intrinsic motivation positively and significantly influences perceived learning in the course.

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.