Results for 'learning'

954 found
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  1. Learning Organizations and Their Role in Achieving Organizational Excellence in the Palestinian Universities.Mazen J. Al Shobaki, Samy S. Abu Naser, Youssef M. Abu Amuna & Amal A. Al Hila - 2017 - International Journal of Digital Publication Technology 1 (2):40-85.
    The research aims to identify the learning organizations and their role in achieving organizational excellence in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) workers from the Palestinian universities was selected and the recovery (...)
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  2. Learning Motivation and Utilization of Virtual Media in Learning Mathematics.Almighty Tabuena & Jupeth Pentang - 2021 - Asia-Africa Journal of Recent Scientific Research 1 (1):65-75.
    This study aims to describe the learning motivation of students using virtual media when they are learning mathematics in grade 5. The research design applied in this research is classroom action research. The research is conducted in two phases which involve planning, action and observation and reflection. The results of the study revealed that intrinsic motivation to learn is most prevalent in the form of fun to learn mathematics with virtual media. Other forms of intrinsic motivation include curiosity, (...)
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  3. Deep learning and synthetic media.Raphaël Millière - 2022 - Synthese 200 (3):1-27.
    Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...)
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  4. Distributed learning: Educating and assessing extended cognitive systems.Richard Heersmink & Simon Knight - 2018 - Philosophical Psychology 31 (6):969-990.
    Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualising how we learn to assemble extended cognitive systems by internalising cultural norms and practices. Having a better grip on (...)
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  5. Amidst the Online Learning Modality: The Social Support and Its Relationship to the Anxiety of Senior High School Students.Jastine Joy Basilio, Twinkle Pangilinan, Jeremiah Joy Kalong & Jhoselle Tus - 2022 - Psychology Abd Education: A Multidisciplinary Journal 1 (1):1-6.
    Senior high school is known to be part of the newly implemented K-12 program in the Philippines' educational system. Hence, this program added two years to the academic learning program of students, which mainly focuses on different theoretical and vocational strands that aim to prepare and fully furnish the students for education and employment in the future. Due to adjustments to new online learning amidst the pandemic, students begin to experience various challenges, primarily social support and mental well-being. (...)
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  6. The Learning-Consciousness Connection.Jonathan Birch, Simona Ginsburg & Eva Jablonka - 2021 - Biology and Philosophy 36 (5):1-14.
    This is a response to the nine commentaries on our target article “Unlimited Associative Learning: A primer and some predictions”. Our responses are organized by theme rather than by author. We present a minimal functional architecture for Unlimited Associative Learning that aims to tie to together the list of capacities presented in the target article. We explain why we discount higher-order thought theories of consciousness. We respond to the criticism that we have overplayed the importance of learning (...)
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  7. Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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  8.  76
    (2 other versions)Learning from Learning from our Mistakes.Clayton Littlejohn - 2016 - In Martin Grajner & Pedro Schmechtig (eds.), Epistemic Reasons, Epistemic Norms, Epistemic Goals. De Gruyter. pp. 51-70.
    What can we learn from cases of knowledge from falsehood? Critics of knowledge-first epistemology have argued that these cases provide us with good reason for rejecting the knowledge accounts of evidence, justification, and the norm of belief. I shall offer a limited defense of the knowledge-first approach to these matters. Knowledge from falsehood cases should undermine our confidence in like-from-like reasoning in epistemology. Just as we should be open to the idea that knowledge can come from non-knowledge, we should be (...)
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  9. Learning to love the reviewer.Quan-Hoang Vuong - 2017 - European Science Editing 43 (4):83-83.
    Learning to love the reviewer -/- Issue: 43(4) November 2017. Viewpoint Page 83 -/- Quan Hoang Vuong Western University Hanoi, Centre for Interdisciplinary Social Research, Hanoi, Vietnam.
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  10. Learning in the social being system.Zoe Jenkin & Lori Markson - 2024 - Behavioral and Brain Sciences 47:e132.
    We argue that the core social being system is unlike other core systems in that it participates in frequent, widespread learning. As a result, the social being system is less constant throughout the lifespan and less informationally encapsulated than other core systems. This learning supports the development of the precursors of bias, but also provides avenues for preempting it.
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  11. Perceptual learning and reasons‐responsiveness.Zoe Jenkin - 2022 - Noûs 57 (2):481-508.
    Perceptual experiences are not immediately responsive to reasons. You see a stick submerged in a glass of water as bent no matter how much you know about light refraction. Due to this isolation from reasons, perception is traditionally considered outside the scope of epistemic evaluability as justified or unjustified. Is perception really as independent from reasons as visual illusions make it out to be? I argue no, drawing on psychological evidence from perceptual learning. The flexibility of perceptual learning (...)
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  12. Networked Learning and Three Promises of Phenomenology.Lucy Osler - forthcoming - In Phenomenology in Action for Researching Networked Learning Experiences.
    In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical (...)
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  13. Perceptual learning.Zoe Jenkin - 2023 - Philosophy Compass 18 (6):e12932.
    Perception provides us with access to the external world, but that access is shaped by our own experiential histories. Through perceptual learning, we can enhance our capacities for perceptual discrimination, categorization, and attention to salient properties. We can also encode harmful biases and stereotypes. This article reviews interdisciplinary research on perceptual learning, with an emphasis on the implications for our rational and normative theorizing. Perceptual learning raises the possibility that our inquiries into topics such as epistemic justification, (...)
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  14. Deep Learning as Method-Learning: Pragmatic Understanding, Epistemic Strategies and Design-Rules.Phillip H. Kieval & Oscar Westerblad - manuscript
    We claim that scientists working with deep learning (DL) models exhibit a form of pragmatic understanding that is not reducible to or dependent on explanation. This pragmatic understanding comprises a set of learned methodological principles that underlie DL model design-choices and secure their reliability. We illustrate this action-oriented pragmatic understanding with a case study of AlphaFold2, highlighting the interplay between background knowledge of a problem and methodological choices involving techniques for constraining how a model learns from data. Building successful (...)
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  15. Learning Concepts: A Learning-Theoretic Solution to the Complex-First Paradox.Nina Laura Poth & Peter Brössel - 2020 - Philosophy of Science 87 (1):135-151.
    Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept learning, (...)
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  16. Learning to Act.Jan Bransen - 2016 - Symposion: Theoretical and Applied Inquiries in Philosophy and Social Sciences 3 (1):11-35.
    In this paper I argue that to understand minded agency – the capacity we typically find instantiated in instances of human behaviour that could sensibly be questioned by asking “What did you do?” – one needs to understand childhood, i.e. the trajectory of learning to act. I discuss two different types of trajectory, both of which seem to take place during childhood and both of which might be considered crucial to learning to act: a growth of bodily control (...)
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  17. Perceptual Learning and the Contents of Perception.Kevin Connolly - 2014 - Erkenntnis 79 (6):1407-1418.
    Suppose you have recently gained a disposition for recognizing a high-level kind property, like the property of being a wren. Wrens might look different to you now. According to the Phenomenal Contrast Argument, such cases of perceptual learning show that the contents of perception can include high-level kind properties such as the property of being a wren. I detail an alternative explanation for the different look of the wren: a shift in one’s attentional pattern onto other low-level properties. Philosophers (...)
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  18. Perceptual Learning Explains Two Candidates for Cognitive Penetration.Valtteri Arstila - 2016 - Erkenntnis 81 (6):1151-1172.
    The cognitive penetrability of perceptual experiences has been a long-standing topic of disagreement among philosophers and psychologists. Although the notion of cognitive penetrability itself has also been under dispute, the debate has mainly focused on the cases in which cognitive states allegedly penetrate perceptual experiences. This paper concerns the plausibility of two prominent cases. The first one originates from Susanna Siegel’s claim that perceptual experiences can represent natural kind properties. If this is true, then the concepts we possess change the (...)
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  19. Learning to Discriminate: The Perfect Proxy Problem in Artificially Intelligent Criminal Sentencing.Benjamin Davies & Thomas Douglas - 2022 - In Jesper Ryberg & Julian V. Roberts (eds.), Sentencing and Artificial Intelligence. Oxford: OUP.
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that (...)
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  20. Modular Learning Efficiency: Learner’s Attitude and Performance Towards Self-Learning Modules.April Clarice C. Bacomo, Lucy P. Daculap, Mary Grace O. Ocampo, Crystalyn D. Paguia, Jupeth Pentang & Ronalyn M. Bautista - 2022 - IOER International Multidisciplinary Research Journal 4 (2):60-72.
    Learner’s attitude towards modular distance learning catches uncertainties as a world crisis occurs up to this point. As self-learning modules (SLMs) become a supplemental means of learning in new normal education, this study investigated efficiency towards the learners’ attitude and performance. Specifically, the study described the learners’ profile and their attitude and performance towards SLMs. It also ascertained the relationship between the learner’s profile with their attitude and performance, as well as the relationship between attitude and performance (...)
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  21. Learning Networks and Connective Knowledge.Stephen Downes - 2010 - In Harrison Hao Yang & Steve Chi-Yin Yuen (eds.), Collective Intelligence and E-Learning 2.0: Implications of Web-Based Communities and Networking. IGI Global.
    The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a (...)
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  22. (1 other version)Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen C. King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...)
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  23. Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  24. Learning and Business Incubation Processes and Their Impact on Improving the Performance of Business Incubators.Shehada Y. Rania, El Talla A. Suliman, J. Shobaki Mazen & Samy S. Abu-Naser - 2020 - International Journal of Academic Multidisciplinary Research (IJAMR) 4 (5):120-142.
    This study aimed to identify the learning and business incubation processes and their impact on developing the performance of business incubators in Gaza Strip, and the study relied on the descriptive analytical approach, and the study population consisted of all employees working in business incubators in Gaza Strip in addition to experts and consultants in incubators where their total number reached (62) individuals, and the researchers used the questionnaire as a main tool to collect data through the comprehensive survey (...)
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  25. Learning Models in the Transition Towards Complexity as a Challenge to Simplicity.Jefferson Alexander Moreno-Guaicha, Alexis Mena Zamora & Levis Zerpa Morloy - 2024 - Sophía: Colección de Filosofía de la Educación 1 (36):67-108.
    This research is motivated by the need to unravel the progression of learning models, which have been adapting to meet the demands of society in its constant dynamics of fluctuation and transformation. The aim of this work is to systematically examine the evolution of learning models, highlighting the paradigmatic changes that have favored the transition from traditional learning approaches to more innovative and transdisciplinary proposals. To achieve this, a bibliographic analysis is carried out, supported by the hermeneutic (...)
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  26. Machines learning values.Steve Petersen - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. Oxford University Press.
    Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: (...)
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  27. Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment.Fares Wael Al-Gharabawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):10-17.
    This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to (...)
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  28. The function of perceptual learning.Zoe Jenkin - 2023 - Philosophical Perspectives 37 (1):172-186.
    Our perceptual systems are not stagnant but can learn from experience. Why is this so? That is, what is the function of perceptual learning? I consider two answers to this question: The Offloading View, which says that the function of perceptual learning is to offload tasks from cognition onto perception, thereby freeing up cognitive resources (Connolly, 2019) and the Perceptual View, which says that the function of perceptual learning is to improve the functioning of perception. I argue (...)
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  29. LEARNING ENVIRONMENT RELATED FACTORS AFFECTING AFGHAN EFL UNDERGRADUATES’ SPEAKING SKILL.Hazrat Usman Mashwani & Siti Maftuhah Damio - 2022 - Journal of Foreign Language Teaching and Learning (JFLTL) 7 (2):225-244.
    Of the four language skills, speaking is usually considered an indicator of proficiency in a language. As an EFL student, one should master speaking skill (Nazara, 2012). Unfortunately, most Afghan EFL undergraduates are not as good at speaking as they are in the other three English language skills (reading, writing and listening). Most Afghan undergraduate EFL learners are good at reading and writing, but in part of oral communication, they are not accurate and fluent (Zia & Sulan, 2015). Hence, this (...)
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  30. Learning Strategies, Motivation, and Its Relationship to the Online Learning Environment Among College Students.Ana Mhey M. Tabinas, Jemimah Abigail R. Panuncio, Dianah Marie T. Salvo, Rebecca A. Oliquino, Shaena Bernadette D. Villar & Jhoselle Tus - 2023 - Psychology and Education: A Multidisciplinary Journal 11 (2):622-628.
    Online education has become an essential component of education. Thus, several factors, such as the student’s learning strategy and motivation, generally contribute to their academic success. This study investigates the relationship between learning strategies, motivation, and online learning environment among 150 first-year college students. Employing correlational design, the statistical findings of the study reveal that the r coefficient of 0.59 indicates a moderate positive correlation between the variables. The p-value of 0.00, which is less than 0.05, leads (...)
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  31. Learning, evolvability and exploratory behaviour: extending the evolutionary reach of learning.Rachael L. Brown - 2013 - Biology and Philosophy 28 (6):933-955.
    Traditional accounts of the role of learning in evolution have concentrated upon its capacity as a source of fitness to individuals. In this paper I use a case study from invasive species biology—the role of conditioned taste aversion in mitigating the impact of cane toads on the native species of Northern Australia—to highlight a role for learning beyond this—as a source of evolvability to populations. This has two benefits. First, it highlights an otherwise under-appreciated role for learning (...)
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  32. Unlimited Associative Learning and the Origins of Consciousness: A Primer and Some Predictions.Jonathan Birch, Simona Ginsburg & Eva Jablonka - 2020 - Biology and Philosophy 35 (6):1-23.
    Over the past two decades, Ginsburg and Jablonka have developed a novel approach to studying the evolutionary origins of consciousness: the Unlimited Associative Learning framework. The central idea is that there is a distinctive type of learning that can serve as a transition marker for the evolutionary transition from non-conscious to conscious life. The goal of this paper is to stimulate discussion of the framework by providing a primer on its key claims and a clear statement of its (...)
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  33. Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding (...)
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  34. Learning Recovery: Teacher’s Strategies and Challenges.Janekin Hamoc - 2023 - Asian Journal of Advanced Multidisciplinary Researches 3 (2):1-5.
    This study aimed to explore the teachers' experiences in addressing the learning gaps during the resumption of in-person classes post-pandemic. Specifically, it sought to determine the learning recovery strategies implemented and the challenges encountered by the teachers. Six (6) teachers from DepEd Zamboanga City Division were involved in this study employing a qualitative-phenomenological research design. The participants were purposively selected based on the criteria defined in this paper. The data were collected through in-depth interviews with semi-structured interview questions. (...)
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  35. Learning from Conditionals.Benjamin Eva, Stephan Hartmann & Soroush Rafiee Rad - 2020 - Mind 129 (514):461-508.
    In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian (...)
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  36. (1 other version)Learning as Differentiation of Experiential Schemas.Jan Halák - 2019 - In Jim Parry & Pete Allison (eds.), Experiential Learning and Outdoor Education: Traditions of practice and philosophical perspectives. Routledge. pp. 52-70.
    The goal of this chapter is to provide an interpretation of experiential learning that fully detaches itself from the epistemological presuppositions of empiricist and intellectualist accounts of learning. I first introduce the concept of schema as understood by Kant and I explain how it is related to the problems implied by the empiricist and intellectualist frameworks. I then interpret David Kolb’s theory of learning that is based on the concept of learning cycle and represents an attempt (...)
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  37. Learning from errors in digital patient communication: Professionals’ enactment of negative knowledge and digital ignorance in the workplace.Rikke Jensen, Charlotte Jonasson, Martin Gartmeier & Jaana Parviainen - 2023 - Journal of Workplace Learning 35 (5).
    Purpose. The purpose of this study is to investigate how professionals learn from varying experiences with errors in health-care digitalization and develop and use negative knowledge and digital ignorance in efforts to improve digitalized health care. Design/methodology/approach. A two-year qualitative field study was conducted in the context of a public health-care organization working with digital patient communication. The data consisted of participant observation, semistructured interviews and document data. Inductive coding and a theoretically informed generation of themes were applied. Findings. The (...)
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  38. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...)
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  39. Learning How to Represent: An Associationist Account.Nancy Salay - 2019 - Journal of Mind and Behavior 40 (2):121-14.
    The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, enactivist (...)
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  40. Fish Classification Using Deep Learning.M. N. Ayyad & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):51-58.
    Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...)
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  41. Learning as Hypothesis Testing: Learning Conditional and Probabilistic Information.Jonathan Vandenburgh - manuscript
    Complex constraints like conditionals ('If A, then B') and probabilistic constraints ('The probability that A is p') pose problems for Bayesian theories of learning. Since these propositions do not express constraints on outcomes, agents cannot simply conditionalize on the new information. Furthermore, a natural extension of conditionalization, relative information minimization, leads to many counterintuitive predictions, evidenced by the sundowners problem and the Judy Benjamin problem. Building on the notion of a `paradigm shift' and empirical research in psychology and economics, (...)
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  42. Teaching & learning guide for: Carbon pricing ethics.Kian Mintz-Woo - 2022 - Philosophy Compass 17 (2):e12816.
    This teaching and learning guide accompanies the following article: Mintz-Woo, K., 2022. Carbon Pricing Ethics. Philosophy Compass 17(1):article e12803. doi:10.1111/phc3.12803. [Open access].
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  43. Machine Learning, Misinformation, and Citizen Science.Adrian K. Yee - 2023 - European Journal for Philosophy of Science 13 (56):1-24.
    Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.
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  44. Perceptual learning, the mere exposure effect and aesthetic antirealism.Bence Nanay - 2017 - Leonardo 50:58-63.
    It has been argued that some recent experimental findings about the mere exposure effect can be used to argue for aesthetic antirealism: the view that there is no fact of the matter about aesthetic value. The aim of this paper is to assess this argument and point out that this strategy, as it stands, does not work. But we may still be able to use experimental findings about the mere exposure effect in order to engage with the aesthetic realism/antirealism debate. (...)
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  45. Social learning through process improvements in Russia.Tatiana Medvedeva & Stuart Umpleby - 2002 - In Robert Trappl (ed.), Cybernetics and Systems. Austrian Society for Cybernetics Studies. pp. 2.
    The Russian people are struggling to learn how to create a democracy and a market economy. This paper reviews the results of reform efforts to date and what the Russian people are learning as indicated by changes in answers to public opinion surveys. As a way to continue the social learning process in Russia we suggest the widespread use of process improvement methods in organizations. This paper describes some Russian experiences in using process improvement methods and proposes a (...)
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  46. Two Great Problems of Learning.Nicholas Maxwell - 2003 - Teaching in Higher Education, 8 (January):129-134.
    Two great problems of learning confront humanity: learning about the universe, and learning how to live wisely. The first problem was solved with the creation of modern science, but the second problem has not been solved. This combination puts humanity into a situation of unprecedented danger. In order to solve the second problem we need to learn from our solution to the first problem. This requires that we bring about a revolution in the overall aims and methods (...)
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  47. Learning from Failure: Shame and Emotion Regulation in Virtue as Skill.Matt Stichter - 2020 - Ethical Theory and Moral Practice 23 (2):341-354.
    On an account of virtue as skill, virtues are acquired in the ways that skills are acquired. In this paper I focus on one implication of that account that is deserving of greater attention, which is that becoming more skillful requires learning from one’s failures, but that turns out to be especially challenging when dealing with moral failures. In skill acquisition, skills are improved by deliberate practice, where you strive to correct past mistakes and learn how to overcome your (...)
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  48. MACHINE LEARNING IMPROVED ADVANCED DIAGNOSIS OF SOFT TISSUES TUMORS.M. Bavadharani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):112-123.
    Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...)
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  49. Learning to Imagine.Amy Kind - 2022 - British Journal of Aesthetics 62 (1):33-48.
    Underlying much current work in philosophy of imagination is the assumption that imagination is a skill. This assumption seems to entail not only that facility with imagining will vary from one person to another, but also that people can improve their own imaginative capacities and learn to be better imaginers. This paper takes up this issue. After showing why this is properly understood as a philosophical question, I discuss what it means to say that one imagining is better than another (...)
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  50. Should the use of adaptive machine learning systems in medicine be classified as research?Robert Sparrow, Joshua Hatherley, Justin Oakley & Chris Bain - 2024 - American Journal of Bioethics 24 (10):58-69.
    A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they (...)
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