Mind the gap! An exploration of the role of lifelong learning in promoting co-production and citizenship within social care for older people

Contemporary themes in public policy have emphasised co-productive approaches within both the access and provision of support services to older people. This paper provides a cross disciplinary exploration from its respective authors perspectives on social work and educational gerontology to examine the potential for lifelong learning and learning interventions from which co-production with those using social care services in later life might be better facilitated. Using an example from the UK, we specifically elicit how co-produced care can enhance the horizon of learning and learning research. The synthesis of ideas across these two disciplines could enrich understanding and provide essential levers for moving towards empowerment and emancipation by engaging with a more co-productive approach in social care for older people. (DIPF/Orig.)

The Impact of Motor Learning on Motor Behavior and Cortical Dynamics in a Complex Stressful Social Environment

An economy of effort is a core characteristic of highly skilled motor performance often described as being effortless or automatic. Electroencephalographic (EEG) evaluation of cortical activity in elite performers has consistently revealed a reduction in extraneous associative cortical activity and an enhancement of task-relevant cortical processes. However, this has only been demonstrated under what are essentially practice-like conditions. Recently it has been shown that cerebral cortical activity becomes less efficient when performance occurs in a stressful, complex social environment. This dissertation examines the impact of motor skill training or practice on the EEG cortical dynamics that underlie performance in a stressful, complex social environment. Sixteen ROTC cadets participated in head-to-head pistol shooting competitions before and after completing nine sessions of skill training over three weeks. Spectral power increased in the theta frequency band and decreased in the low alpha frequency band after skill training. EEG Coherence increased in the left frontal region and decreased in the left temporal region after the practice intervention. These suggest a refinement of cerebral cortical dynamics with a reduction of task extraneous processing in the left frontal region and an enhancement of task related processing in the left temporal region consistent with the skill level reached by participants. Partitioning performance into ‘best’ and ‘worst’ based on shot score revealed that deliberate practice appears to optimize cerebral cortical activity of ‘best’ performances which are accompanied by a reduction in task-specific processes reflected by increased high-alpha power, while ‘worst’ performances are characterized by an inappropriate reduction in task-specific processing resulting in a loss of focus reflected by higher high-alpha power after training when compared to ‘best’ performances. Together, these studies demonstrate the power of experience afforded by practice, as a controllable factor, to promote resilience of cerebral cortical efficiency in complex environments.

Parental views of antibiotic use in children with upper respiratory tract infections in Jordan

Purpose: To assess the knowledge, attitudes and practices of parents towards antibiotics use for upper respiratory tract infections (URTIs) in Jordan. Methods: A cross-sectional study was carried out at 10 private outpatients’ pediatric clinics across Amman-Jordan from September to December 2013. During the study period, 1329 parents of young children who fulfilled the inclusion criteria and agreed to participate were interviewed, and completed a validated structured questionnaire. Results: A large proportion of parents (903, 68 %) believed that weather change was the main cause of acute URTIs in their children. Although 1098 (82.8 %) of parents were aware that the recurrent use of antibiotics leads to a decrease in effectiveness due to bacterial resistance, 859 (64.6 %) of the respondents reported that they would give antibiotics without prescription. Fathers (135, 40.2 %), were significantly more aware that URTIs follow its natural course without antibiotic administration compared to mothers (N = 327, 32.9 %), respectively (p = 0.005). Conclusion: There is a lack of adequate parental knowledge concerning the use and misuse of antibiotics in children in Jordan. National publicity campaign should be mounted to improve awareness. Furthermore, existing laws should be enforced to prevent parents from purchasing antibiotics over-thecounter (OTC).

Possibilities and problems of teaching the history of the present time through media and information literacy.

Le système éducatif encourage une histoire positiviste, ordonnée, unilatérale et universelle; par l´incorporation de le découpage chronologique de l´histoire en quatre étapes. Mais, est-ce qu´il serait posible que les élèves puissent étudier leur propre présent? Mon commuication poursuit d´exposer, comme Saab affirmait, le présent est “le point de départ et d´arrivée de l´enseignement de l´histoire détermine les allers et les retours au passé”. La façon d´approcher l´enseignement de l´histoire est confortable. Il n´y a pas de questions, il n´y a pas de discussions. Cette vision de l´histoire interprétée par l´homme blancoccidental-hétérosexuel s´inscrit dans le projet de la modernité du Siècle des Lumières. Par conséquent, cette histoire obvie que nous vivons dans una société postmoderne de la suspicion, de la pensée débile. En ce qui concerne la problématique autour de la pollution audiovisuelle et la façon dont les enseignants et les élèves sont quotidiennement confrontés à ce problème. Par conséquent, il est nécessaire de réfléchir à la question de l´enseignement de l´histoire quadripartite. Actuellement, les médias et les nouvelles technologies sont en train de changer la vie de l´humanité. Il est indispensable que l´élève connaisse son histoire presente et les scénarioshistoriques dans l´avenir. Je pense en la nécessité d´adopter une didactique de l’histoire presente et par conséquent, nous devons utiliser la maîtrise des médias et de l´information. Il faut une formation des enseignants que pose, comme Gadamer a dit: “le passé y le présent se trouvent par une négociation permanente”. Una formation des enseignants qui permette de comprendre et penser l´histoire future / les histoires futures. À mon avis, si les élèves comprennent la complexité de leur monde et leurs multiples visions, les élèves seront plus tolérantes et empathiques.

A PHENOMENOLOGY OF MIMETIC LEARNING AND MULTIMODAL COGNITION: INTEGRATING EXPERIENTIAL KNOWLEDGE INTO PROGRAMS IN RHETORIC, COMPOSITION, AND TECHNICAL COMMUNICATION

My dissertation emphasizes a cognitive account of multimodality that explicitly integrates experiential knowledge work into the rhetorical pedagogy that informs so many composition and technical communication programs. In these disciplines, multimodality is widely conceived in terms of what Gunther Kress calls “socialsemiotic” modes of communication shaped primarily by culture. In the cognitive and neurolinguistic theories of Vittorio Gallese and George Lakoff, however, multimodality is described as a key characteristic of our bodies’ sensory-motor systems which link perception to action and action to meaning, grounding all communicative acts in knowledge shaped through body-engaged experience. I argue that this “situated” account of cognition – which closely approximates Maurice Merleau-Ponty’s phenomenology of perception, a major framework for my study – has pedagogical precedence in the mimetic pedagogy that informed ancient Sophistic rhetorical training, and I reveal that training’s multimodal dimensions through a phenomenological exegesis of the concept mimesis. Plato’s denigration of the mimetic tradition and his elevation of conceptual contemplation through reason, out of which developed the classic Cartesian separation of mind from body, resulted in a general degradation of experiential knowledge in Western education. But with the recent introduction into college classrooms of digital technologies and multimedia communication tools, renewed emphasis is being placed on the “hands-on” nature of inventive and productive praxis, necessitating a revision of methods of instruction and assessment that have traditionally privileged the acquisition of conceptual over experiential knowledge. The model of multimodality I construct from Merleau-Ponty’s phenomenology, ancient Sophistic rhetorical pedagogy, and current neuroscientific accounts of situated cognition insists on recognizing the significant role knowledges we acquire experientially play in our reading and writing, speaking and listening, discerning and designing practices.

A literary approach to teaching english language in a multicultural classroom

Literature is not generally considered as a coherent branch of the curriculum in relation to language development in either native or foreign language teaching. As teachers of English in multicultural Indian classrooms, we come across students with varying degrees of competence in English language learning. Although language learning is a natural process for natives, students of other languages put in colossal efforts to learn it. Despite their sincere efforts, they face challenges regarding pronunciation, spelling, and vocabulary. Indian classrooms are a microcosm of the larger society, so teaching English language in a manner that equips the students to face the cutthroat competition has become a necessity and a challenge for English language teachers. English today has become the key determinant for being successful in their careers. The hackneyed and stereotypical methods of teaching are not acceptable now. Teachers are no longer arbitrary dispensers of knowledge, but they are playing the role of a guide and facilitator for the students. Teachers of English are using innovative ideas to make English language teaching and learning interesting and simple. Teachers have started using literary texts and their analyses to explore and ignite the imagination and creative skills of the students. One needs to think and rethink the contribution of literature to intelligent thinking as well as its role in the process of teaching/learning. This article is, therefore, an attempt at exploring the nature of the literary experience in the present-day classrooms and the broader role of literature in life.

Clase Invertida

En este segundo número de reflexiones pedagógicas se presenta una revisión de la denominada clase invertida (flipped classroom). Este texto presentan los componentes que caracterizan esta estrategia. Se comparan igualmente los elementos que la diferencian de la clase tradicional y se destacan los pasos para adelantar esta innovación y su forma de funcionamiento. De igual manera se muestran algunos indicadores que pueden llevar a una reflexión de la pacífica pedagógica y se concluye con estudios que muestran sus aportes e investigaciones que la soportan.

Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.

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.

The role of networked learning in academics’ writing

This article explores academics’ writing practices, focusing on the ways in which they use digital platforms in their processes of collaborative learning. It draws on interview data from a research project that has involved working closely with academics across different disciplines and institutions to explore their writing practices, understanding academic literacies as situated social practices. The article outlines the characteristics of academics’ ongoing professional learning, demonstrating the importance of collaborations on specific projects in generating learning in relation to using digital platforms and for sharing and collaborating on scholarly writing. A very wide range of digital platforms have been identified by these academics, enabling new kinds of collaboration across time and space on writing and research; but challenges around online learning are also identified, particularly the dangers of engaging in learning in public, the pressures of ‘always-on’-ness and the different values systems around publishing in different forums.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.