Gaining Insight into Teaching: A Phenomenological Exploration of the Lived Experience of the Teachers of the Year

What qualities, skills, and knowledge produce quality teachers? Many stake-holders in education argue that teacher quality should be measured by student achievement. This qualitative study shows that good teachers are multi-dimensional; their effectiveness cannot be represented by students’ test scores alone. The purpose of this phenomenological study was to gain a deeper understanding of quality in teaching by examining the lived experiences of 10 winners or finalists of the Teacher of the Year (ToY) Award. Phenomenology describes individuals’ daily experiences of phenomena, examines how these experiences are structured, and focuses analysis on the perspectives of the persons having the experience (Moustakas, 1994). This inquiry asked two questions: (a) How is teaching experienced by recognized as outstanding Teachers of the Year? and (b) How do ToYs feelings and perceptions about being good teachers provide insight, if any, about concepts such as pedagogical tact, teacher selfhood, and professional dispositions? Ten participants formed the purposive sample; the major data collection tool was semi-structured interviews (Patton, 1990; Seidman, 2006). Sixty to 90-minute interviews were conducted with each participant. Data also included the participants’ ToY application essays. Data analysis included a three-phase process: description, reduction, interpretation. Findings revealed that the ToYs are dedicated, hard-working individuals. They exhibit behaviors, such as working beyond the school day, engaging in lifelong learning, and assisting colleagues to improve their practice. Working as teachers is their life’s compass, guiding and wrapping them into meaningful and purposeful lives. Pedagogical tact, teacher selfhood, and professional dispositions were shown to be relevant, offering important insights into good teaching. Results indicate that for these ToYs, good teaching is experienced by getting through to students using effective and moral means; they are emotionally open, have a sense of the sacred, and they operate from a sense of intentionality. The essence of the ToYs teaching experience was their being properly engaged in their craft, embodying logical, psychological, and moral realms. Findings challenge current teacher effectiveness process-product orthodoxy which makes a causal connection between effective teaching and student test scores, and which assumes that effective teaching arises solely from and because of the actions of the teacher.

Nurses’ clinical communication skills

Abstract Background: Providing nursing care involves an interpersonal relationship between the nurse and the patient which is created through communication. The importance of clinical communication skills is a current priority when it comes to health care workers’ education and training and has been attracting more and more attention. As a consequence clinical communication skills are now present in more and more academic programmes. Objectives: To assess nurses’ clinical communication skills; to identify the variables that might inluence the clinical communication skills; to analyse nurses’ perspective regarding the training in the clinical communication ield. Material and methods: Quantitative, non-experimental, descriptive and correlational and crosssectional study. We used the questionnaire to collect socio-demographic and professional data, and the Clinical Communication Skills Scale based on the Kalamazoo Consensus Statement (KCS)1,2 and which had already been used in Portugal.3 The sample was formed by 275 practitioner nurses who have been working in health care institutions located in the center of Portugal. Results: The Scale we used presents 5 factors that explain 64.33% of the total variation: To in‑ volve the patient; To facilitate dialogue; To understand concerns; To communicate in an asser‑ tive way; To carry out the interview. The majority of the nurses consider that the training they had in the communication skills ield during their nursing course was good or very good, however we could see that 23.3% think it was mediocre. Almost all of them (98.9%) agree that there should be a better and more speciic training in the ield of clinical communication skills as far, as nurses as concerned. Nurses who had training in this area, older nurses, those who work directly with patients and those who have been working for a longer period of time show better communication skills. Conclusion: Although they think that the training they has was good, we could conirm that there was a deicit in nurses’ clinical communication skills and that nurses themselves refer they need more training in this area. Data point out to a more signiicant investment in clinical communication as far as nurses’ training is concerned and they suggest the promotion of lifelong learning opportunities in this area.

Técnicas e sistemas de desobturação canalar no retratamento endodôntico não cirúrgico

Na prática clínica, a diversidade de instrumentos manuais, rotatórios ou reciprocantes, dificulta a seleção do sistema a aplicar no retratamento dentário não cirúrgico. O presente trabalho teve como objetivo comparar diferentes instrumentos quanto a diferentes parâmetros: capacidade de remoção de Gutta-Percha (GP), extrusão apical de detritos, fratura de instrumentos, e ocorrência de iatrogenias. Neste trabalho foram utilizadas 111 publicações posteriores a 2011, obtidas via PubMed e Science Direct. A análise da bibliografia indica que, independentemente do sistema, não é possível remover todo o material obturador das paredes radiculares, sendo esta tarefa dificultada em canais curvos e na área apical. Verifica-se que a remoção de GP melhora no sentido: limas H, ProTaper, e Mtwo. O sistema Reciproc foi associado a melhores desempenhos e a menores tempo de trabalho, do que os sistemas de rotação contínua. Nenhum dos instrumentos analisados é capaz de evitar a extrusão apical de detritos na totalidade. Apesar de resultados dispares, a maioria dos estudos assume que o sistema Reciproc provoca menor extrusão apical de detritos. Em Endodontia, as duas principais causas da fratura de instrumentos são a fadiga cíclica e a torsão. A maioria dos estudos concordam que o movimento reciprocante, como o do Reciproc, aumenta a resistência à fractura e a resistência à torsão, mantendo a anatomia original do canal. Relativamente à produção de perfurações e fracturas radiculares, a superioridade dos instrumentos NiTi relativamente às limas manuais não foi clara. De acordo com a literatura, o sistema Reciproc, constituído por liga de NiTi M-Wire, está associado a menos eventos iatrogénicos. Finalmente, conclui-se que futuros estudos seriam benéficos para esclarecer o potencial dos diferentes sistemas estudados.

Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

New perspectives of genetic disorders in cattle

In the last decades a negative trend in inbreeding has accompanied the evident improvement in productivity and performance of bovine domestic population, predisposing to the occurrence of recessively inherited disorders. The objectives of this thesis were: a) the study of genetic diseases applying a “forward genetic approach” (FGA); b) the estimation of the prevalence of deleterious alleles responsible for eight recessive disorders in different breeds; c) the collection of well-characterized materials in a Biobank for Bovine Genetic Disorders. The FGA allowed the identification of seven new recessive deleterious variants (Paunch calf syndrome - KDM2B; Congenital cholesterol deficiency - APOB; Ichthyosis congenita - FA2H; Hypotrichosis - KRT71; Hypotrichosis - HEPHL1; Achromatopsia - CNGB3; Hemifacial microsomia – LAMB1) and of seven new de novo dominant deleterious variants (Achondrogenesis type II - two variants in COL2A1; Osteogenesis imperfecta - COL1A1; Skeletal-cardio-enteric dysplasia - MAP2K2; Congenital neuromuscular channelopathy - KGNG1; Epidermolysis bullosa simplex - KRT5; Classical Ehlers-Danlos syndrome - COL5A2) in different breeds, associated with a large spectrum of phenotypes affecting different systems. The FGA was based on the sequence of a clinical, genealogical, gross- and/or histopathological and genomic study. In particular, a WGS trio-approach (patient, dam and sire) was applied. The prevalence of deleterious alleles was calculated for the Pseudomyotonia congenita, Paunch calf syndrome, Hemifacial microsomia, Congenital bilateral cataract, Ichthyosis congenita, Ichthyosis fetalis, Achromatopsia and Hypotrichosis. A particular concern resulted the allelic frequency of 12% for the Paunch calf syndrome in Romagnola cattle. In respect to the Biobank for Bovine Genetic Diseases, biological materials of clinical cases and their available relatives as well as controls used for the allelic frequency estimations were stored at -20 °C. Altogether, around 16.000 samples were added to the biobank.

Innovazione e inclusione scolastica: didattiche e spazi di apprendimento innovativi in una scuola aperta e in dialogo con il territorio possono favorire l'inclusione di tutti gli alunni? Le percezioni di Dirigenti scolastici, insegnanti e studenti

La ricerca, di carattere esplorativo, prende spunto dal dibattito internazionale, sviluppatosi sul finire dello scorso secolo, sulla necessità di innovare il sistema educativo, in ottica di lifelong-learning, e favorire l’acquisizione delle competenze richieste nel XXI secolo. Le diverse indicazioni sollecitano una scuola intesa come Civic-center in grado di riconoscere gli apprendimenti extra-scolastici, con spazi di apprendimento innovativi funzionali a didattiche learner-centred. A circa trent’anni dalla Dichiarazione di Salamanca riteniamo necessario interrogarsi se queste innovazioni garantiscano l’inclusione e il successo formativo di tutti. La ricerca si articola in quattro studi di caso relativi a due scuole secondarie di secondo grado innovative italiane e due finlandesi. Si propone di comprendere sulla base delle percezioni di studenti, insegnanti, dirigenti se tale modello di scuola favorisca anche l’inclusione e il benessere di tutti gli studenti. Dall’analisi dei risultati sembra che, secondo le percezioni di coloro che hanno partecipato alla ricerca, le scuole siano riuscite a far coesistere innovazione e inclusione. In particolare, l’utilizzo di spazi di apprendimento innovativi e didattiche learner-centred all’interno di una scuola aperta al territorio in grado di riconoscere le competenze extra-scolastiche, sembrano favorire effettivamente l’inclusione di tutti gli studenti. Nonostante gli aspetti innovativi, restano tuttavia presenti all’interno delle scuole analizzate ancora diverse criticità che non consentono una piena inclusione for all

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Systems thinking: learning or driving?

The ability to foresee how behaviour of a system arises from the interaction of its components over time - i.e. its dynamic complexity – is seen an important ability to take effective decisions in our turbulent world. Dynamic complexity emerges frequently from interrelated simple structures, such as stocks and flows, feedbacks and delays (Forrester, 1961). Common sense assumes an intuitive understanding of their dynamic behaviour. However, recent researches have pointed to a persistent and systematic error in people understanding of those building blocks of complex systems. This paper describes an empirical study concerning the native ability to understand systems thinking concepts. Two different groups - one, academic, the other, professional – submitted to four tasks, proposed by Sweeney and Sterman (2000) and Sterman (2002). The results confirm a poor intuitive understanding of the basic systems concepts, even when subjects have background in mathematics and sciences.

Systems thinking: learning or driving?

The ability to foresee how behaviour of a system arises from the interaction of its components over time - i.e. its dynamic complexity – is seen an important ability to take effective decisions in our turbulent world. Dynamic complexity emerges frequently from interrelated simple structures, such as stocks and flows, feedbacks and delays (Forrester, 1961). Common sense assumes an intuitive understanding of their dynamic behaviour. However, recent researches have pointed to a persistent and systematic error in people understanding of those building blocks of complex systems. This paper describes an empirical study concerning the native ability to understand systems thinking concepts. Two different groups - one, academic, the other, professional – submitted to four tasks, proposed by Sweeney and Sterman (2000) and Sterman (2002). The results confirm a poor intuitive understanding of the basic systems concepts, even when subjects have background in mathematics and sciences.

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Intersubband excitations at finite temperatures and their roles in different quasi-one-dimensional systems

We theoretically study many-body excitations in three different quasi-one-dimensional (Q1D) electron systems: (i) those formed on the surface of liquid Helium; (ii) in two coupled semiconductor quantum wires; and (iii) Q1D electrons embedded in polar semiconductor-based quantum wires. Our results show intersubband coupling between higher subbands and the two lowest subbands affecting even the lower energy intersubband plasmons on the liquid Helium surface. Concerning the second system, we show a pronounced extra peak appearing in the intersubband impurity spectral function for temperatures as high as 20 K. We finally show coupled intersubband plasmon-phonon modes surviving for temperatures up to 300 K.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.