Results for 'autotelic learning'

998 found
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  1. VALIDITY: A Learning Game Approach to Mathematical Logic.Steven James Bartlett - 1973 - Hartford, CT: Lebon Press. Edited by E. J. Lemmon.
    The first learning game to be developed to help students to develop and hone skills in constructing proofs in both the propositional and first-order predicate calculi. It comprises an autotelic (self-motivating) learning approach to assist students in developing skills and strategies of proof in the propositional and predicate calculus. The text of VALIDITY consists of a general introduction that describes earlier studies made of autotelic learning games, paying particular attention to work done at the Law (...)
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  2. 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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  3. 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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  4. 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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  5. 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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  6.  92
    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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. Good Learning and Epistemic Transformation.Kunimasa Sato - 2023 - Episteme 20 (1):181-194.
    This study explores a liberatory epistemic virtue that is suitable for good learning as a form of liberating socially situated epistemic agents toward ideal virtuousness. First, I demonstrate that the weak neutralization of epistemically bad stereotypes is an end of good learning. Second, I argue that weak neutralization represents a liberatory epistemic virtue, the value of which derives from liberating us as socially situated learners from epistemic blindness to epistemic freedom. Third, I explicate two distinct forms of epistemic (...)
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  15. Machine learning, justification, and computational reliabilism.Juan Manuel Duran - 2023
    This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can be shown about (...)
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  16. 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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  17. 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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  18. 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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  19. Learning from Fiction.Greg Currie, Heather Ferguson, Jacopo Frascaroli, Stacie Friend, Kayleigh Green & Lena Wimmer - 2023 - In Alison James, Akihiro Kubo & Françoise Lavocat (eds.), The Routledge Handbook of Fiction and Belief. Routledge. pp. 126-138.
    The idea that fictions may educate us is an old one, as is the view that they distort the truth and mislead us. While there is a long tradition of passionate assertion in this debate, systematic arguments are a recent development, and the idea of empirically testing is particularly novel. Our aim in this chapter is to provide clarity about what is at stake in this debate, what the options are, and how empirical work does or might bear on its (...)
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  20. 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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  21. 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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  22. 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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  23. 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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  24. Modular Distance Learning: A Blueprint to English Writing Proficiency.Erica Mae D. Dipay - 2023 - International Journal of Multidisciplinary Educational Research and Innovation 1 (1):14-23.
    Face-to-face learning engagement has been suspended due to the health crisis that affected the whole world. This led to the adaptation of modular distance learning to continue delivering quality education. As the transition continues, its effectiveness has been frequently assessed. The key purpose of this study is to determine the relationship between the level of implementation of modular distance learning and English Writing Proficiency among the senior high school students in Saint Joseph College Maasin City School year (...)
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  25. 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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  26. Learning Matters: The Role of Learning in Concept Acquisition.Eric Margolis & Stephen Laurence - 2011 - Mind and Language 26 (5):507-539.
    In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are (...)
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  27. 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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  28. 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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  29. 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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  30.  43
    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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  31. Machine learning in scientific grant review: algorithmically predicting project efficiency in high energy physics.Vlasta Sikimić & Sandro Radovanović - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...)
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  32.  75
    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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  33. 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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  34. Online Learning Challenges and Effects on Mental Health of the Hospitality Management Students at Romblon State University.Gina Mapalad - 2023 - Apcore Online Journal of Proceedings 3 (1):163-172.
    Evidence shows that the COVID-19 pandemic has increased stress and despair levels. During the outbreak, everyone’s health and safety are given high importance. The only practical option at this time is for schools and institutions all around the world to switch to online classes. Students, parents, professors, and teachers in the Philippines are aware of the long-term difficulties of online learning, notably their effects on college student’s mental health. This hasn't, however, been adequately documented. The challenges and consequences of (...)
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  35. 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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  36. 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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  37. Art and Learning: A Predictive Processing Proposal.Jacopo Frascaroli - 2022 - Dissertation, University of York
    This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The work (...)
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  38. Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework.Tosin ige, Christopher Kiekintveld & Aritran Piplai - forthcoming - Proceedings of the IEEE:8.
    The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...)
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  39. 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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  40. 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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  41. 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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  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. 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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  44. DHF Learning Module Design for Elementary School Teachers Within the Region Kuta Raja District, Banda Aceh City.Hermansyah Hermansyah, Ismail Ismail, Munazar Munazar & Halimatussakdiah Halimatussakdiah - 2023 - Jurnal Penelitian Pendidikan Ipa 9 (11):9749-9755.
    The high number of Dengue Hemorrhagic Fever (DHF) cases from year to year and the endemic status of DHF in Kutaraja District, Banda Aceh City encourages us to always make efforts to reduce the morbidity rate of DHF. The involvement of elementary school teachers is very important to participate in DHF control which will teach their students. This study was conducted to assess the effect of before and after administration of the DHF learning module on the average knowledge score (...)
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  45. 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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  46. 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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  47. Egalitarian Machine Learning.Clinton Castro, David O’Brien & Ben Schwan - 2023 - Res Publica 29 (2):237–264.
    Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian (...)
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  48. 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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  49. The Impact of Learning and Development Programs on the Core Competencies of Pasay City Government.Rosanie Estuche, Maria Krisna Zen Nirvana Magdasoc Valencia & Cynic Tenedero - 2024 - International Journal of Multidisciplinary Educational Research and Innovation 2 (1):108- 116.
    Learning and Development (L&D) is an organizational process which helps in the development of knowledge and achievement of individual and organizational goals. This is quantitative research employed a descriptive method to determine the impact of Learning and Development Programs on the Core Competencies of Level II employees of the Pasay City Government. The respondents of this study were the two hundred forty-two (242) randomly selected PCG Level II employees of the City Government of Pasay. This research used a (...)
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  50. ONLINE LEARNING AND EMOTIONAL PRESENCE OF SENIOR HIGH SCHOOL STUDENTS.Jowenie A. Mangarin - 2023 - Get International Research Journal 1 (2):66-67.
    This study delved into the realm of emotional presence and its potential impact on students' academic performance within collaborative online learning environments. While emotions were recognized as influential factors in the learning process, the specific relationship between emotional presence and cognitive outcomes remained elusive, particularly in the context of virtual classrooms. This research addressed a critical gap by investigating the relationships between emotional presence and students’ academic achievements in online classes. An exploratory sequential mixed-methods approach incorporating both quantitative (...)
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