A Phenomenological Study of Learning Processes at Work: Confirming, Extending, and Challenging Holistic Learning Theory

Holistic learning theory (Yang, 2003) identified explicit, implicit, and emancipatory knowledge facets in learning. A phenomenological study of how participants��� experienced interactions between knowledge facets showed the facets expressed, informed, changed, and guided one another. The complexity of learning and the role of spirituality in learning were explored.

The Integrated Process of Engagement in Adult Learning

This paper explores the role of engagement in adult learning based on Illeris��� three dimensional model of learning and Yang���s holistic theory of knowledge and learning. Engagement and learning are integrated processes by which adult learners gain a deeper understanding and make meaning of the activities he or she is exposed to in a given learning environment.

Maximizing Hospitality Learning Outcomes: An Integrated Experiential In-Class Approach

This study explored the influence of an experiential, in-class approach to the hospitality curriculum as a means of increasing its efficiency and effectiveness. Specifically, the study provides an example of how hospitality faculty might utilize an experiential, in-class approach to integrate additional hospitality-specific content along with process and content issues for working in teams and team decision-making. The results of this study support the efficient and effective use of an experiential inclass teaching method. The value of this study is twofold: (1) it provides an initial test of this approach���s usefulness and (2) it provides a forum for continued conversations of how experiential approaches can be utilized to enhance and reinforce other hospitality content and managerial skills and to bridge the gap between vocational and liberal education outcomes.

Transformational learning-environments in the post-secondary classroom: through the experience of indigenous women

This study investigated the physical, mental, emotional, and spiritual aspects of transformational learning, including the elements, attributes, factors, and catalysis of the transformational learning environment, as Indigenous women experienced them in the Indigenous Studies 3040H: The Meaning of Work in the Contemporary World (INDG 3040H) course at Trent University. Using a holistic model developed in connection with the Medicine Wheel an Indigenous epistemology is integrated into the study. Qualitative interviews were conducted with eight Indigenous women. The data collected from the interviews indicated that for Indigenous women, the transformational learning environment of the post-secondary classroom is heavily connected to the relationship students develop with themselves, their peers, and the faculty member alongside the content of the course.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

<p>Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.</p>

Transformative Learning Theory and Its Relevance to Managing Group Dynamics in a Competitive Work Environment

In any environment, group dynamics would exist. How we deal with it in a competitive work environment defines who we are using transformative learning. This paper provides useful information from a number of theorists who share perspectives on the complex nature of groups.

Digital anthropology and educational eGames: learning through behavioural patterns in digital, game-based contexts

The selected publications are focused on the relations between users, eGames and the educational context, and how they interact together, so that both learning and user performance are improved through feedback provision. A key part of this analysis is the identification of behavioural, anthropological patterns, so that users can be clustered based on their actions, and the steps taken in the system (e.g. social network, online community, or virtual campus). In doing so, we can analyse large data sets of information made by a broad user sample,which will provide more accurate statistical reports and readings. Furthermore, this research is focused on how users can be clustered based on individual and group behaviour, so that a personalized support through feedback is provided, and the personal learning process is improved as well as the group interaction. We take inputs from every person and from the group they belong to, cluster the contributions, find behavioural patterns and provide personalized feedback to the individual and the group, based on personal and group findings. And we do all this in the context of educational games integrated in learning communities and learning management systems. To carry out this research we design a set of research questions along the 10-year published work presented in this thesis. We ask if the users can be clustered together based on the inputs provided by them and their groups; if and how these data are useful to improve the learner performance and the group interaction; if and how feedback becomes a useful tool for such pedagogical goal; if and how eGames become a powerful context to deploy the pedagogical methodology and the various research methods and activities that make use of that feedback to encourage learning and interaction; if and how a game design and a learning design must be defined and implemented to achieve these objectives, and to facilitate the productive authoring and integration of eGames in pedagogical contexts and frameworks. We conclude that educational games are a resourceful tool to provide a user experience towards a better personalized learning performance and an enhance group interaction along the way. To do so, eGames, while integrated in an educational context, must follow a specific set of user and technical requirements, so that the playful context supports the pedagogical model underneath. We also conclude that, while playing, users can be clustered based on their personal behaviour and interaction with others, thanks to the pattern identification. Based on this information, a set of recommendations are provided Digital Anthropology and educational eGames 6 /216 to the user and the group in the form of personalized feedback, timely managed for an optimum impact on learning performance and group interaction level. In this research, Digital Anthropology is introduced as a concept at a late stage to provide a backbone across various academic fields including: Social Science, Cognitive Science, Behavioural Science, Educational games and, of course, Technology-enhance learning. Although just recently described as an evolution of traditional anthropology, this approach to digital behaviour and social structure facilitates the understanding amongst fields and a comprehensive view towards a combined approach. This research takes forward the already existing work and published research onusers and eGames for learning, and turns the focus onto the next step ��� the clustering of users based on their behaviour and offering proper, personalized feedback to the user based on that clustering, rather than just on isolated inputs from every user. Indeed, this pattern recognition in the described context of eGames in educational contexts, and towards the presented aim of personalized counselling to the user and the group through feedback, is something that has not been accomplished before.

An integrated psychology virtual research ethics committee

Research and professional ethics are an integral part of every Psychology degree, as this is seen as a key graduate learning outcome for students leaving to become clinicians working with clients and patients. The development of these skills is embedded in teaching, but they culminate in the final year of a degree when final year students must gain formal ethical approval for their final research project. Decision as to the ethical appropriateness of research are made by a Departmental Research Ethics Committee, which considers all research project proposals submitted by staff and students within the department. One of the challenges of this practice is the scale of work involved for committee members (Doyle & Buckley, 2014) who are all faculty members, and the tracking of applications and decisions, alongside the quality assurance required to ensure that all applications are treated fairly and equally. The time involved in performing this work is often underestimated by Universities, and the variety and complexity of decisions requires extensive discussion and negotiation. Traditionally, these decisions are reached by committee discussions, however this presents logistical difficulties as it requires meetings with quorate attendance. The University of Westminster launched a virtual tool in 2014 to facilitate the management of the Research Ethics Committee, to help track the progress of applications and to allow discussions to occur and be managed virtually. The Department of Psychology adopted the tools in September 2014 to deal with all ethics applications. Here we report on how this virtual committee has affected the role and practices of a working committee that deals with over 300 applications per year, and how an online ethics procedure has facilitated an integrated developmental approach to ethical education.

Understanding what drives informal learning at work: A resource-based view.

The resource-based view identifies a number of factors that may influence employees��� informal learning. In a cross-sectional survey of 113 German employees in the energy sector, we examined a number of potential predictors of informal learning and a more positive informal learning attitude. The results showed that proactive help-seeking and professional self-efficacy were positive predictors of informal learning. Employees who were older, who enjoyed learning, sought help and were self-efficacious learners had a more positive attitude towards formal learning. Employees who had a more positive attitude about informal learning rated organisational learning provisions as less important, potentially due to being proactive help-seekers. Managers rated organisational learning resources as less important than non-managerial employees. However, managers also reported higher professional self-efficacy. These circumstances may also influence their decision-making regarding the need to provide learning resources to others in the workplace.

A supervised machine learning method for word sense disambiguation of Portuguese nouns

Disserta����o de Mestrado, Ci��ncias da Linguagem, Faculdade de Ci��ncias Humanas e Sociais, Universidade do Algarve, 2010

Form, Intention, and Indexing: The Liminal and Integrated Conceptions of Work in Knowledge Organization

This paper proposes a dual conception of work in knowledge organization. The first part is a conception of work as liminal, set apart from everyday work. The second is integrated, without separation. This talk is the beginning of a larger project where we will characterize work in knowledge organization, both as it is set out in our literature (S��auperl, 2004; Hj��rland 2003 Wilson, 1968), and in a philosophical argument for its fundamental importance in the activities of society (Shera, 1972; Zandonade, 2004).But in order to do this, we will co-opt the conception of liminality from the anthropology of religion (Turner, 1967), and Zen Buddhist conceptions of moral action, intention, and integration (Harvey, 2000 and cf., Harada, S., 2008).The goal for this talk is to identify the acts repeated (form) and the purpose of those acts (intention), in knowledge organization, with specific regard to thresholds (liminal points) of intention present in those acts.We can then ask the questions: Where is intention in knowledge organization liminal and where is it integrated? What are the limits of knowledge organization work when considered at a foundational level of the intention labor practices? Answering such questions, in this context, allows us to reconsider the assumptions we have about knowledge organization work and its increasingly important role in society. As a consequence, we can consider the limits of classification research if we see the foundations of knowledge organization work when we see forms and intentions. I must also say that incorporating Zen Buddhist philosophy into knowledge organization research seems like it fits well with ethics and ethical responses the practice of knowledge organization. This is because 20th Century Western interpretations of Zen are often rooted in ethical considerations. This translates easily to work.

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.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of ���mobile for intelligence���, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Construction Of A Manual Of Work Processes And Techniques From Centro De Dispensa����o De Medicamentos De Alto Custo (cedmac), Hospital De Cl��nicas, Unicamp.

The Centers for High Cost Medication (Centros de Medica����o de Alto Custo, CEDMAC), Health Department, S��o Paulo were instituted by project in partnership with the Clinical Hospital of the Faculty of Medicine, USP, sponsored by the Foundation for Research Support of the State of S��o Paulo (Funda����o de Amparo �� Pesquisa do Estado de S��o Paulo, FAPESP) aimed at the formation of a statewide network for comprehensive care of patients referred for use of immunobiological agents in rheumatological diseases. The CEDMAC of Hospital de Cl��nicas, Universidade Estadual de Campinas (HC-Unicamp), implemented by the Division of Rheumatology, Faculty of Medical Sciences, identified the need for standardization of the multidisciplinary team conducts, in face of the specificity of care conducts, verifying the importance of describing, in manual format, their operational and technical processes. The aim of this study is to present the methodology applied to the elaboration of the CEDMAC/HC-Unicamp Manual as an institutional tool, with the aim of offering the best assistance and administrative quality. In the methodology for preparing the manuals at HC-Unicamp since 2008, the premise was to obtain a document that is participatory, multidisciplinary, focused on work processes integrated with institutional rules, with objective and didactic descriptions, in a standardized format and with electronic dissemination. The CEDMAC/HC-Unicamp Manual was elaborated in 10 months, with involvement of the entire multidisciplinary team, with 19 chapters on work processes and techniques, in addition to those concerning the organizational structure and its annexes. Published in the electronic portal of HC Manuals in July 2012 as an e-Book (ISBN 978-85-63274-17-5), the manual has been a valuable instrument in guiding professionals in healthcare, teaching and research activities.