Results for 'Learning Cycle'

998 found
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  1. Improving Algebraic Thinking Skill, Beliefs And Attitude For Mathematics Throught Learning Cycle Based On Beliefs.Widodo Winarso & Toheri - 2017 - Munich University Library.
    In the recent years, problem-solving become a central topic that discussed by educators or researchers in mathematics education. it’s not only as the ability or as a method of teaching. but also, it is a little in reviewing about the components of the support to succeed in problem-solving, such as student's belief and attitude towards mathematics, algebraic thinking skills, resources and teaching materials. In this paper, examines the algebraic thinking skills as a foundation for problem-solving, and learning cycle (...)
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  2. 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. London: 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 (...)
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  3. 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 (...)
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  4. Machine Learning Application to Predict The Quality of Watermelon Using JustNN.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):1-8.
    In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent accurate (...)
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  5. Cognitive Skills in Philosophy.Steven James Bartlett - 1978-1979 - Aitia 6 (3):12-21.
    Two fundamentally distinct approaches to the teaching of philosophy are contrasted: On the one hand, there is the “information-oriented” approach which has dominated classrooms and which emphasizes the understanding of historically important philosophical works. On the other hand, there is the “cognitive skills” approach. The two approaches may be distinguished under the headings of ‘knowing that’ as opposed to ‘knowing how’. This paper describes and discusses four perspectives relating to the teaching of cognitive skills: (i) the discovery-oriented approach, (ii) Piagetian (...)
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  6. The social lab as a method for experimental engagement in participatory research.Ilse Marschalek & Vincent Blok - 2022 - Journal of Responsible Innovation 1 (1):1.
    How does the Social Lab methodology support participatory research? This paper provides an evidence-based analysis of experiences of 19 implemented Social Labs applying experiential learning cycles on the question of how to induce Responsible Research and Innovation in the Horizon2020 research funding scheme of the European Commission and beyond. It looks at the potentials of Social Labs to allow participation in research and innovation addressing societal challenges and contrasts empirical results with the theoretical conceptualisation of a scientific Social Lab (...)
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  7. Mapping Value Sensitive Design onto AI for Social Good Principles.Steven Umbrello & Ibo van de Poel - 2021 - AI and Ethics 1 (3):283–296.
    Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, (...)
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  8. The scientific study of passive thinking: Methods of mind wandering research.Samuel Murray, Zachary C. Irving & Kristina Krasich - 2022 - In Felipe de Brigard & Walter Sinnott-Armstrong (eds.), Neuroscience and philosophy. Cambridge, Massachusetts: The MIT Press. pp. 389-426.
    The science of mind wandering has rapidly expanded over the past 20 years. During this boom, mind wandering researchers have relied on self-report methods, where participants rate whether their minds were wandering. This is not an historical quirk. Rather, we argue that self-report is indispensable for researchers who study passive phenomena like mind wandering. We consider purportedly “objective” methods that measure mind wandering with eye tracking and machine learning. These measures are validated in terms of how well they predict (...)
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  9. Projection, Problem Space and Anchoring.David Kirsh - 2009 - Proceedings of the 31st Annual Conference of the Cognitive Science Society:2310-2315.
    When people make sense of situations, illustrations, instructions and problems they do more than just think with their heads. They gesture, talk, point, annotate, make notes and so on. What extra do they get from interacting with their environment in this way? To study this fundamental problem, I looked at how people project structure onto geometric drawings, visual proofs, and games like tic tac toe. Two experiments were run to learn more about projection. Projection is a special capacity, similar to (...)
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  10. AI and its new winter: from myths to realities.Luciano Floridi - 2020 - Philosophy and Technology 33 (1):1-3.
    An AI winter may be defined as the stage when technology, business, and the media come to terms with what AI can or cannot really do as a technology without exaggeration. Through discussion of previous AI winters, this paper examines the hype cycle (which by turn characterises AI as a social panacea or a nightmare of apocalyptic proportions) and argues that AI should be treated as a normal technology, neither as a miracle nor as a plague, but rather as (...)
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  11. Interactivity and multimedia interfaces.David Kirsh - 1997 - Instructional Science 25:79-96.
    Multimedia technology offers instructional designers an unprecedented opportunity to create richly interactive learning environments. With greater design freedom comes complexity. The standard answer to the problems of too much choice, disorientation, and complex navigation is thought to lie in the way we design interactivity in a system. Unfortunately, the theory of interactivity is at an early state of development. After critiquing the decision cycle model of interaction—the received theory in human computer interaction—I present arguments and observational data to (...)
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  12. Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, run, and the (...)
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  13. The Role of Philosophy Teaching Methods in Development of Critical Thinking.Levon Babajanyan - 2020 - Scientific and Methodical Journal 1 (Scientific-Methodical Articles):15-26.
    Modern educational systems face challenges arising from technological development, like an extension of media-manipulations, fake news, mass unemployment etc. Modern educational systems integrate the methods of development of the critical thinking in educational process to overcome such challenges, that promotes the development of analytic, synthetic and evaluative skills of the students, as well as helps them to be protected against media-manipulations and fake news, and be competitive, informed and demanded in the labor market. Teaching the scientific discipline of philosophy and (...)
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  14. Life Sustains Life 2. The Ways of Re-Engagement With the Living Earth.James Tully - 2019 - In Akeel Bilgrami (ed.), Nature and Value. New York: Columbia University Press.
    This article argues that we need to learn from the living earth how living systems sustain themselves and use this knowledge to transform our unsustainable and destructive social systems into sustainable and symbiotic systems within systems. I first set out what I take to be four central features of sustainable living systems according to the life and earth sciences. Secondly, I set out what I take to be the main features of our unsustainable social system that cause damage to the (...)
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  15. A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers.Lorna Green - manuscript
    June 2022 A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers We are in a unique moment of our history unlike any previous moment ever. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. As I write, the planet is in deep trouble, heat, fires, great storms, and record flooding, (...)
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  16. Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model.Roope Oskari Kaaronen & Nikita Strelkovskii - 2020 - One Earth 2 (1):85-97.
    To reach sustainability transitions, we must learn to leverage social systems into tipping points, where societies exhibit positive-feedback loops in the adoption of sustainable behavioral and cultural traits. However, much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human (...)
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  17. A call to World Governments; Save youth generations from obsoleted education systems !Dr Dalia Mabrouk - 2021 - American Journal of Educational Research and Reviews 4 (AJERR (2021) 4:85):15-29.
    My research is a result of accumulated provocation of obsolete and paralyzing education that has been frozen since the middle ages. We have to admit that before the pandemic, education was already in crisis. Governments have been ignoring to adopt any comprehensive plan to reform the educational systems till it has been unprecedently disrupted by COVID-19. I try through this paper to make a global call for governments to immediately start cooperating together for setting international qualifications framework that best suit (...)
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  18. Review of The Inflationary Universe by Alan Guth (1997).Michael Starks - 2016 - In Suicidal Utopian Delusions in the 21st Century: Philosophy, Human Nature and the Collapse of Civilization-- Articles and Reviews 2006-2017 2nd Edition Feb 2018. Michael Starks. pp. 615-618.
    This is one of the best popular cosmology books ever written and Guth is now (2016) a top physics Professor at MIT. He tells the extremely complex story of inflation and related areas of particle physics in such an absorbing style that it reads like a detective novel-in fact, it is a detective novel-how he and others found out how the universe started! The interweaving of his personal story and that of many colleagues along with their photos and many wonderfully (...)
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  19. Predicting Whether a Couple is Going to Get Divorced or Not Using Artificial Neural Networks.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):49-55.
    In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.
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  20. Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.
    Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.
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  21. 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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  22. 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. Life cycle: formation, structure, management.Sergii Sardak, Igor Britchenko, Radostin Vazov & Oleksandr P. Krupskyi - 2021 - Списание «Икономически Изследвания (Economic Studies)» 30 (6):126-142.
    The article aims to define the management mechanism of complex, open dynamic systems with human participation. The following parts of the system life-cycle were identified and unified in the theoretical scope: general and specific compositional elements of repeating changes, marginal index boundaries, the dynamics of the compositional elements of the lifecycle, the key points of the change in the character of the index dynamics. In the practical scope, two common trends of socio-economical system life-cycle management are considered. The (...)
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  24. 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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  25. 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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  26. 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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  27. 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: Oxford University Press.
    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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  28. 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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  29. 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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  30. Logical ignorance and logical learning.Richard Pettigrew - 2021 - Synthese 198 (10):9991-10020.
    According to certain normative theories in epistemology, rationality requires us to be logically omniscient. Yet this prescription clashes with our ordinary judgments of rationality. How should we resolve this tension? In this paper, I focus particularly on the logical omniscience requirement in Bayesian epistemology. Building on a key insight by Hacking :311–325, 1967), I develop a version of Bayesianism that permits logical ignorance. This includes: an account of the synchronic norms that govern a logically ignorant individual at any given time; (...)
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  31. Perceptual Learning (Network for Sensory Research/University of York Perceptual Learning Workshop, Question One).Kevin Connolly, Dylan Bianchi, Craig French, Lana Kuhle & Andy MacGregor - manuscript
    This is an excerpt of a report that highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York in March, 2012. This portion of the report explores the question: What is perceptual learning?
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  32. Learning from experience and conditionalization.Peter Brössel - 2023 - Philosophical Studies 180 (9):2797-2823.
    Bayesianism can be characterized as the following twofold position: (i) rational credences obey the probability calculus; (ii) rational learning, i.e., the updating of credences, is regulated by some form of conditionalization. While the formal aspect of various forms of conditionalization has been explored in detail, the philosophical application to learning from experience is still deeply problematic. Some philosophers have proposed to revise the epistemology of perception; others have provided new formal accounts of conditionalization that are more in line (...)
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  33. 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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  34. 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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  35. 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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  36. Pedagogical Rhythms: Practices and Reflections on Practices.Rebecca DeYoung - 2011 - In James K. A. Smith & David Smith (eds.), Teaching, Learning, and Christian Practice. Grand Rapids: Eerdmans.
    In this chapter, DeYoung looks at the concept of practices and goes on to argue why they are needed and how they can be useful. Beginning with the past traditions of practices and reflection on practices of the Desert Fathers and their followers, DeYoung takes the conversation to the classroom to discuss how such traditionally embedded practices can still be used. She emphasizes the cycle of doing practices and reflecting upon practices within the regular rhythm of the classroom.
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  37. Pupils’ Learning Styles and Academic Performance in Modular Learning.June Albert V. Cavite & Maria Victoria A. Gonzaga - 2023 - International Journal of Multidisciplinary Educational Research and Innovation 1 (3): 72-88.
    This study assesses the student learning styles and academic performance in modular learning among Grade IV, V, and VI learners of Hindang Central School. This considered the learning styles and academic performance of the respondents in modular learning. A total of 252 learners from Hindang Central School participated as respondents in the evaluative method of research that consists of two parts questionnaires. This study used a modified survey questionnaire from the University of California at Merced, Student (...)
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  38. Learning Implicit Biases from Fiction.Kris Goffin & Stacie Friend - 2022 - Journal of Aesthetics and Art Criticism 80 (2):129-139.
    Philosophers and psychologists have argued that fiction can ethically educate us: fiction supposedly can make us better people. This view has been contested. It is, however, rarely argued that fiction can morally “corrupt” us. In this article, we focus on the alleged power of fiction to decrease one's prejudices and biases. We argue that if fiction has the power to change prejudices and biases for the better, then it can also have the opposite effect. We further argue that fictions are (...)
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  39. A Product Life Cycle Ontology for Additive Manufacturing.Munira Mohd Ali, Rahul Rai, J. Neil Otte & Barry Smith - 2019 - Computers in Industry 105:191-203.
    The manufacturing industry is evolving rapidly, becoming more complex, more interconnected, and more geographically distributed. Competitive pressure and diversity of consumer demand are driving manufacturing companies to rely more and more on improved knowledge management practices. As a result, multiple software systems are being created to support the integration of data across the product life cycle. Unfortunately, these systems manifest a low degree of interoperability, and this creates problems, for instance when different enterprises or different branches of an enterprise (...)
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  40. Machines learning values.Steve Petersen - 2020 - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, USA: 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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  41. 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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  42. 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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  43. 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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  44. Economic Cycles, Crises, and the Global Periphery.Leonid Grinin, Arno Tausch & Andrey Korotayev (eds.) - 2016 - Switzerland: Springer International Publishing Switzerland.
    This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this (...)
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  45. 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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  46. Intelligence cycle.Nicolae Sfetcu - manuscript
    The intelligence cycle is a set of processes used to provide useful information for decision-making. The cycle consists of several processes. The related counter-intelligence area is tasked with preventing information efforts from others. A basic model of the process of collecting and analyzing information is called the "intelligence cycle". This model can be applied, and, like all the basic models, it does not reflect the fullness of real-world operations. Through intelligence cycle activities, information is collected and (...)
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  47. 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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  48. Learning and Selection Processes.Marc Artiga - 2010 - Theoria 25 (2):197-209.
    In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections.
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  49.  22
    Learning from the Radical Behavioral Challenge.Hasko von Kriegstein - 2024 - Business Ethics Journal Review 11 (2):8-14.
    I (mostly) accept Ancell’s argument that my proposal for dealing with the radical behavioral challenge entails what he calls ‘the excessive recusal problem’. I argue that this is no reason to reject my proposal, but rather an opportunity for further reflection on what behavioral and normative ethicists can learn from each other. I make some suggestions for future lines of inquiry for both fields.
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  50. 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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