Results for 'future learning'

974 found
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  1. Pragmatism : A learning theory for the future.Bente Elkjaer - 2009 - In Knud Illeris (ed.), Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. Routledge. pp. 74-89.
    A theory of learning for the future advocates the teaching of a preparedness to respond in a creative way to difference and otherness. This includes an ability to act imaginatively in situations of uncertainties. John Dewey’s pragmatism holds the key to such a learning theory his view of the continuous meetings of individuals and environments as experimental and playful. That pragmatism has not yet been acknowledged as a relevant learning theory for the future may be (...)
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  2. Learning from the Past to the Future in Metaphysics.Jani Hakkarainen - 2023 - In Jani Sinokki & Eero Kaila (eds.), Acta Philosophica Fennica XCVIII. Finnish Philosophical Society. pp. 125-141.
    I propose that metaphysical study is initially indifferent to the truth of Metaphysical Realism about Metaphysics (MRM) and Metaphysical Realism and does not presuppose them. Metaphysical Realism is a metaphysical doctrine the truth of which cannot be settled logically prior to metaphysical investigation. MRM presupposes Metaphysical Realism and therefore, one should not hold MRM uncritically. An epistemological consequence of this is that arguments against the possibility of cognition about metaphysically real entities (by e.g., Hume) are not arguments against the epistemic (...)
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  3. Electrifying the Future, 11th Budapest Visual Learning Conference.Kristof Nyiri (ed.) - 2024 - Budapest: Hungarian Academy of Science.
    The present online volume contains the papers prepared for the 11th Budapest Visual Learning Conference – ENVISIONING AN ELECTRIFYING FUTURE – held in a physical-online blended form on Nov. 13, 2024, organized by the University of Pécs (represented by Prof. Gábor Szécsi, Dean, Faculty of Cultural Sciences, Education and Regional Development), and the Hungarian Academy of Sciences (represented by Prof. Kristóf Nyíri, Member of the Hungarian Academy of Sciences). Nyíri and Szécsi were responsible for sending out the call (...)
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  4.  21
    Efficient Machine Learning Algorithm for Future Gold Price Prediction.A. Ravikumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-18.
    The project titled "Efficient Machine Learning Algorithm for Future Gold Price Prediction" focuses on the development of a machine learning model that can accurately predict future gold prices using historical data and various economic indicators. Gold has long been regarded as a safe-haven asset, and its price is influenced by multiple factors, including global economic conditions, inflation rates, interest rates, and geopolitical events. This research aims to design and implement a robust machine learning model that (...)
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  5. Learning from Arguments: An Introduction to Philosophy.Daniel Z. Korman - 2022 - The PhilPapers Foundation.
    Learning from Arguments advances accessible versions of key philosophical arguments, in a form that students can emulate in their own writing, and with the primary aim of cultivating an understanding of the dynamics of philosophical argumentation. -/- The book contains ten core chapters, covering the problem of evil, Pascal’s wager, personal identity, the irrationality of fearing death, free will and determinism, Cartesian skepticism, the problem of induction, the problem of political authority, the violinist argument, the future-like-ours argument, the (...)
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  6. 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 (...)
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  7.  52
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (...)
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  8. The Future of Human-Artificial Intelligence Nexus and its Environmental Costs.Petr Spelda & Vit Stritecky - 2020 - Futures 117.
    The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable (...)
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  9. 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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  10.  28
    Deep Learning - Driven Data Leakage Detection for Secure Cloud Computing.Yoheswari S. - 2024 - International Journal of Engineering Innovations and Management Strategies 5 (1):1-4.
    Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional Neural (...)
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  11. Addressing Students Learning Gaps in Mathematics through Differentiated Instruction.Hernalyn Aguhayon, Roselyn Tingson & Jupeth Pentang - 2023 - International Journal of Educational Management and Development Studies 4 (1):69-87.
    The study aimed to determine if differentiated instruction effectively addresses learning gaps in mathematics. In particular, it explored how it can improve the student’s learning gaps concerning mathematical performance and confidence. The study employed a quasi-experimental design with 30 purposively-selected Grade 10 participants divided into differentiated (n = 15) and control groups (n = 15), ensuring the utmost ethical measures. The mean and standard deviation were used to describe the participants’ performance and confidence. Independent samples t-tests were used (...)
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  12. Building machines that learn and think about morality.Christopher Burr & Geoff Keeling - 2018 - In Christopher Burr & Geoff Keeling (eds.), Proceedings of the Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2018). Society for the Study of Artificial Intelligence and Simulation of Behaviour.
    Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also (...)
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  13. What Time-travel Teaches Us About Future-Bias.Kristie Miller - 2021 - Philosophies 6 (38):38.
    Future-biased individuals systematically prefer positively valenced events to be in the future (positive future-bias) and negatively valenced events to be in the past (negative future-bias). The most extreme form of future-bias is absolute future-bias, whereby we completely discount the value of past events when forming our preferences. Various authors have thought that we are absolutely future-biased (Sullivan (2018:58); Parfit (1984:173) and that future-bias (absolute or otherwise) is at least rationally permissible (Prior (1959), (...)
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  14. Information, learning and falsification.David Balduzzi - 2011
    There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, statistical (...) theory, has introduced measures of capacity that control (in part) the expected risk of classifiers [3]. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. Solomonoff and Hutter have applied algorithmic information to prove remarkable results on universal induction. Shannon information provides the mathematical foundation for communication and coding theory. However, both approaches have shortcomings. Algorithmic information is not computable, severely limiting its practical usefulness. Shannon information refers to ensembles rather than actual events: it makes no sense to compute the Shannon information of a single string – or rather, there are many answers to this question depending on how a related ensemble is constructed. Although there are asymptotic results linking algorithmic and Shannon information, it is unsatisfying that there is such a large gap – a difference in kind – between the two measures. This note describes a new method of quantifying information, effective information, that links algorithmic information to Shannon information, and also links both to capacities arising in statistical learning theory [4, 5]. After introducing the measure, we show that it provides a non-universal analog of Kolmogorov complexity. We then apply it to derive basic capacities in statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies [6], counted in two different ways for the two capacities. We also discuss how effective information relates to information gain, Shannon and mutual information. (shrink)
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  15. A DEEP LEARNING APPROACH FOR LSTM BASED COVID-19 FORECASTING SYSTEM.K. Jothimani - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):28-38.
    : COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts (...)
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  16. Mindset and Levels of Conceptual Understanding in the Problem-Solving of Preservice Mathematics Teachers in an Online Learning Environment.Ma Luisa Mariano-Dolesh, Leila Collantes, Edwin Ibañez & Jupeth Pentang - 2022 - International Journal of Learning, Teaching and Educational Research 21 (6):18-33.
    Mindset plays a vital role in tackling the barriers to improving the preservice mathematics teachers’ (PMTs) conceptual understanding of problem-solving. As the COVID-19 pandemic has continued to pose a challenge, online learning has been adopted. This led this study to determining the PMTs’ mindset and level of conceptual understanding in problem-solving in an online learning environment utilising Google Classroom and the Khan Academy. A quantitative research design was employed specifically utilising a descriptive, comparative, and correlational design. Forty-five PMTs (...)
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  17.  98
    Machine Learning-Based Cyberbullying Detection System with Enhanced Accuracy and Speed.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):421-429.
    The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...)
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  18. Continuing The Distance Learning Modality of Graduate Studies in Post-Covid Philippines: A Survey.Jayrome Nuñez, Louie P. Gula, Evaflor Alindan, John Clinton Colcol, Aristonie Sangco, Jairoh Taracina, Sammy Dolba, Al John Escobañez, Kevin Sumayang, Mark Anthony Jamisal & Francis Jim Tuscano - 2023 - FDLA Journal 7 (1):1-17.
    Getting a graduate education is one of the most important parts of a professional in a field. It allows them to climb higher in the professional rankings or even get higher pay for their academic work. All graduate students are adults and self-directed due to their past experiences in work or practice. However, when the pandemic hit the world, these self-directed learners were not spared from shutting of schools. In the Philippines, most graduate schools deliver their lessons through the traditional (...)
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  19. The Use of Machine Learning Methods for Image Classification in Medical Data.Destiny Agboro - forthcoming - International Journal of Ethics.
    Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. (...)
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  20. The Acceptability of Online Learning Action Cell Session Practice to Tagumpay National High School Teachers.Ann Michelle S. Medina, Mari Cris O. Lim & Aldren E. Camposagrado - 2023 - Universal Journal of Educational Research 2 (2):99-109.
    This quantitative study explores the acceptability of Online Learning Action Cell (LAC) practice as a school-based professional development strategy for Tagumpay National High School (TNHS) teachers. The research was motivated by the Department of Education (DepEd) Order No. 35 s. 2016 which prompts public schools to comply with the implementation of LAC sessions because it has a positive impact on teachers’ beliefs and practices resulting in education reforms for learners’ benefit. However, in compliance with DepEd’s policy on maximizing Time-On-Task (...)
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  21.  73
    Hybrid Cloud-Machine Learning Framework for Efficient Cardiovascular Disease Risk Prediction and Treatment Planning.Kannan K. S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):460-480.
    Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, (...)
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  22. Reliability and Future True Belief: Reply to Olsson and Jönsson.Christoph Jäger - 2011 - Theoria 77 (3):223-237.
    In “Process Reliabilism and the Value Problem” I argue that Erik Olsson and Alvin Goldman's conditional probability solution to the value problem in epistemology is unsuccessful and that it makes significant internalist concessions. In “Kinds of Learning and the Likelihood of Future True Beliefs” Olsson and Martin Jönsson try to show that my argument does “not in the end reduce the plausibility” of Olsson and Goldman's account. Here I argue that, while Olsson and Jönsson clarify and amend the (...)
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  23.  28
    Crime Prediction Using Machine Learning and Deep Learning.S. Venkatesh - 2024 - Journal of Science Technology and Research (JSTAR) 6 (1):1-13.
    Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. (...)
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  24. Falsification and future performance.David Balduzzi - manuscript
    We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy (...)
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  25. Learning in the forest: environmental perception of Brazilian teenagers.Christiana Cabicieri Profice, Fernando Enrique Grenno, Ana Cláudia Fandi, Stela Maria Menezes, Cecília Inés Seminara & Camila Righetto Cassano - 2023 - Frontiers in Psychology 14:1046405.
    In this study, we consider that enabling young people to experience direct contact with nearby natural environments can positively influence their knowledge and feelings about the biodiversity that occurs there, contributing to its protection and conservation for current and future generations. In this study, we explore how teenagers (n = 17) aged between 13 and 17 years old describe and perceive the nearby natural environment before and after an interpretive trail in Una, Bahia, Brazil. Participants were asked to draw (...)
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  26. Evaluation of the Differentiated Learning Training Program at The Mathematics Subject Teachers’ Meeting (MGMP).Abdul Karim & Nurul Anriani - 2024 - Edunesia: Jurnal Ilmiah Pendidikan 5 (1):569-585.
    The purpose of this study was to evaluate the differentiated learning training program at the mathematics subject teachers' meeting (MGMP). A descriptive quantitative approach was used to identify the successes of the program and areas that require improvement. The sample included 21 mathematics teachers in Sub Rayon 2 of Lebak District. The instruments used were questionnaires in which data on participants' responses to resource persons, materials, and suggestions for future activities were collected, and the results of direct observations. (...)
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  27.  70
    Integrating Life-Wide Learning in the Bachelor of Science in Exercise and Sports Science Program in Selected State Universities in Region III: A Case Study.Jay Mark D. Sinag & Norita E. Manly - 2024 - Universal Journal of Educational Research 3 (4):330-348.
    The study investigates the Bachelor of Science in Exercise and Sports Science (BSESS) program curriculum within Region III, specifically studying its alignment with the Commission on Higher Education Memorandum Order (CMO) No. 81, series of 2017, to distinguish potential curriculum and policy developments that backing life-wide learning and student employability. The research identifies existing gaps in career alignment, stakeholder engagement, graduate employability preparation, and policies supporting lifelong learning within the curriculum. Through multiple case study design, it explores curricular (...)
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  28. Effectiveness of Problem-Based Learning on Secondary Students’ Achievement in Science: A Meta-Analysis.Aaron Funa & Maricar Prudente - 2021 - International Journal of Instruction 14 (4):69-84.
    Preparing students for the real challenges in life is one of the most important goals in education. Constructivism is an approach that uses real-life experiences to construct knowledge. Problem-Based Learning (PBL), for almost five decades now, has been the most innovative constructivist pedagogy used worldwide. However, with the rising popularity, there is a need to revisit empirical studies regarding PBL to serve as a guide and basis for designing new studies, making institutional policies, and evaluating educational curricula. This need (...)
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  29. Usage of electronic infrastructures and students’ learning effectiveness in Nigerian universities: A polytomous logistic prediction.Valentine Joseph Owan & John Asuquo Ekpenyong - 2022 - Ubiquitous Learning: An International Journal 15 (2):87-104.
    A preponderance of empirical research in higher education exists on the use of electronic resources to promote university education and learning. This suggests that this area of research has attracted significant interest worldwide. However, there seems to be inadequate information on the association between specific electronic infrastructures, how they are utilized for learning, and their effects on students’ learning effectiveness in higher institutions in Nigeria. This research draws on previous studies and seeks to establish how different electronic (...)
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  30.  53
    Revolutionizing Agriculture with Deep Learning-Based Plant Health Monitoring.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-666.
    By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...)
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  31. Lived Experience of Overcoming the Feeling of Isolation in Distance Learning in the Philippines: A Phenomenological Inquiry.Jayrome Lleva Nuñez - 2021 - Pakistan Journal of Distance and Online Learning 2 (7):55-68.
    In this research, the author presents the lived experience of a graduate student and how she has overcome the feeling of isolation and challenges in distance learning. The participant of this qualitative phenomenological study is student from Visayas State University Open University (VSU-OU). This qualitative phenomenological study (Chambers, 2013) seeks to measure the in-depth experience of a distance learner from the southern Philippines and systematically analyze the culture of distance learning in order to understand the phenomenon of isolation (...)
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  32. A time for learning and for counting – Egyptians, Greeks and empirical processes in Plato’s Timaeus.Barbara M. Sattler - 2010 - In Richard Mohr (ed.), One Book, the Whole Universe: Plato's Timaeus Today: Plato's Timaeus Today. Las Vegas: Parmenides Publishing. pp. 249-266.
    This paper argues that processes in the sensible realm can be in accord with reason in the Timaeus, since rationality is understood here as being based on regularity, which is conferred onto processes by time. Plato uses two different temporal structures in the Timaeus, associated with the contrast there drawn between Greek and Egyptian approaches to history. The linear order of before and after marks natural processes as rational and underlies the Greek treatment of history. By contrast, a bidirectional temporal (...)
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  33. Learning from Fiction to Change our Personal Narratives.Andrew J. Corsa - 2021 - Croatian Journal of Philosophy 21 (61):93-109.
    Can fictional literature help us lead better lives? This essay argues that some works of literature can help us both change our personal narratives and develop new narratives that will guide our actions, enabling us to better achieve our goals. Works of literature can lead us to consider the hypothesis that we might beneficially change our future-oriented, personal narratives. As a case study, this essay considers Ben Lerner’s novel, 10:04, which focuses on humans’ ability to develop new narratives, and (...)
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  34. A matter of trust: : Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics.Kyle M. L. Jones, Alan Rubel & Ellen LeClere - forthcoming - JASIST: Journal of the Association for Information Science and Technology.
    Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however (...)
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  35. Causation: Empirical Trends and Future Directions.David Rose & David Danks - 2012 - Philosophy Compass 7 (9):643-653.
    Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major thread of empirical research on our concept of causation has investigated the (...)
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  36. Educating Judgment: Learning from the didactics of philosophy and sloyd.Birgit Schaffar & Camilla Kronqvist - 2017 - Revista Española de Educación Comparada 29:110–128.
    Teachers in vocational education face two problems. (1) Learning involves the ability to transcend and modify learned knowledge to new circumstances. How should vocational education prepare students for future, unknown tasks? (2) Students should strive to produce work of good quality. How does vocational education help them develop their faculty of judgment to differentiate between better and worse quality? These two ques- tions are tightly interwoven. The paper compares the didactics of philosophy and sloyd. Both developed independently, but (...)
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  37. Predictive Analysis of Lottery Outcomes Using Deep Learning and Time Series Analysis.Asil Mustafa Alghoul & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):1-6.
    Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...)
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  38. The development of human causal learning and reasoning.M. K. Goddu & Alison Gopnik - 2024 - Nature Reviews Psychology 3:319-339.
    Causal understanding is a defining characteristic of human cognition. Like many animals, human children learn to control their bodily movements and act effectively in the environment. Like a smaller subset of animals, children intervene: they learn to change the environment in targeted ways. Unlike other animals, children grow into adults with the causal reasoning skills to develop abstract theories, invent sophisticated technologies and imagine alternate pasts, distant futures and fictional worlds. In this Review, we explore the development of human-unique causal (...)
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  39. Advice seeking network structures and the learning organization.Jarle Aarstad, Marcus Selart & Sigurd Troye - 2011 - Problems and Perspectives in Management 9 (2):44-51.
    Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level (...)
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  40. AI-Driven Learning: Advances and Challenges in Intelligent Tutoring Systems.Amjad H. Alfarra, Lamis F. Amhan, Msbah J. Mosa, Mahmoud Ali Alajrami, Faten El Kahlout, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Applied Research (Ijaar) 8 (9):24-29.
    Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized (...)
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  41. Augmenting Seasoned English Language Teachers’ ICT Skills through a Service-Learning Activity-based TPACK.John Rey Pelila, Shirley L. Ayao-ao, Ma Theresa B. Nollido, Princess Precious Gem D. Ico, Jackielou H. Cabral, Shaira Nadine A. Capiral & Mark Anthony T. Gavina - 2022 - Edulangue 5 (2):1-25.
    Due to the emergence of ICT in ELT sector, seasoned English teachers find it resistant to such a shift despite having a positive attitude towards its use. This quasi-experimental study aimed to examine the extent to which seasoned English language teachers developed their ICT skills through a Service-Learning Activity (SLA). Using a one-group pre- and post-test design, this study collected the data through a modified Needs Assessment Survey (NAS) distributed to fourteen purposively selected participants. It was administered to examine (...)
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  42.  73
    Scalable Cloud Solutions for Cardiovascular Disease Risk Management with Optimized Machine Learning Techniques.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):454-470.
    The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that (...)
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  43.  66
    Integrating Sustainable Agriculture in Teaching Mathematics: Development of a Project- based Learning Prototype.Leonor Roann De Amor Labarca, Sweet Cristine Capin, Elmer Javel Jr, Marvin Valentin, Ivy Lyt Abina & Orville J. Evardo Jr - 2024 - International Journal of Multidisciplinary Educational Research and Innovation 2 (4):1-10.
    This developmental study seeks to develop a Project-Based learning prototype that integrates sustainable agriculture in teaching mathematics. Data was gathered through the in-depth interviews using the developed semi-structured questionnaire. The study used the Clarke and Braun thematic analysis to analyze the data collected. The researchers found the following themes: Profitability of Aquaponics, Requisite for Computational Skills, Integration of Aquaponics in Teaching Unit Conversion, and Promoting Competencies through Self-discovery as Elements in Crafting Teaching-Learning Package for Sustainable Agriculture. Based on (...)
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  44. Stanisław Lem’s Visions of a Technological Future: Toward Philosophy in Technology.Paweł Polak & Roman Krzanowski - 2022 - Filozofia i Nauka. Studia Filozoficzne I Interdyscyplinarne 1 (10):41-50.
    Stanisław Lem is mostly known as a sci-fi writer and not widely perceived as a visionary of the cyber age, despite the fact that he foresaw the future of information technology better than most scientific experts. Indeed, his visions of future information-based societies have proved to be remarkably accurate. Lem’s stories fuse together elements of fantasy, philosophy, and science, but what we can really learn from them is the nature of humanity, technology, and philosophy, as well as the (...)
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  45. Basic beliefs and the perceptual learning problem: A substantial challenge for moderate foundationalism.Bram Vaassen - 2016 - Episteme 13 (1):133-149.
    In recent epistemology many philosophers have adhered to a moderate foundationalism according to which some beliefs do not depend on other beliefs for their justification. Reliance on such ‘basic beliefs’ pervades both internalist and externalist theories of justification. In this article I argue that the phenomenon of perceptual learning – the fact that certain ‘expert’ observers are able to form more justified basic beliefs than novice observers – constitutes a challenge for moderate foundationalists. In order to accommodate perceptual (...) cases, the moderate foundationalist will have to characterize the ‘expertise’ of the expert observer in such a way that it cannot be had by novice observers and that it bestows justification on expert basic beliefs independently of any other justification had by the expert. I will argue that the accounts of expert basic beliefs currently present in the literature fail to meet this challenge, as they either result in a too liberal ascription of justification or fail to draw a clear distinction between expert basic beliefs and other spontaneously formed beliefs. Nevertheless, some guidelines for a future solution will be provided. (shrink)
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  46. Parental Factors Related to Students’ Self-Concept and Academic Performance amid COVID-19 and Distance Learning.Nelda B. Caasi & Jupeth Pentang - 2022 - Universal Journal of Educational Research 1 (4):202-209.
    Parental factors impact students’ self-concept and academic performance during the pandemic. Thus, this study determined the students’ self-concept and academic performance and the parental factors related to it. The research design was descriptive-correlational, and 500 nonrandom college students in West Philippines participated in the study. Researcher-made instruments were used, which were subjected to reliability and validity evaluation. Data were collected online from June 2021 to July 2022 and were analyzed using descriptive (frequency counts and percentage) and inferential statistics (Spearman correlation). (...)
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  47.  57
    A Novel Deep Learning-Based Framework for Intelligent Malware Detection in Cybersecurity.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):666-669.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...)
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  48. Breakthroughs in Breast Cancer Detection: Emerging Technologies and Future Prospects.Ola I. A. Lafi, Rawan N. A. Albanna, Dina F. Alborno, Raja E. Altarazi, Amal Nabahin, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):8-15.
    Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are (...)
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  49. Parents’ Level of Engagement in the Modular Distance Learning of Elementary School Students.Jovenil Bacatan, Henry Vance Suico, Ronel Olila & Argi Macatuay - 2022 - Iconic Research and Engineering Journals 6 (6):79-87.
    The primary purpose of the study was to determine the level of parents’ engagement in the modular learning of elementary school students. The researchers used survey questionnaires as an instrument, utilized the descriptive type of research and total population sampling in the selection of the respondents, the entire parents of Grades 4-6 was studied. The respondents of this study are the 30 parents which are categorized based on their educational attainment and family monthly income. The researchers used the adapted (...)
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  50. Infatuation, Romantic Relationship and Learning Behaviour among School Going Adolescents.Gururaj Itagi - 2021 - SRINIVAS PUBLICATION 6 (1):71-82.
    The adolescence is an important period in which one's character, behaviour, habits, and future lifestyles are developed. Influence of peer is more than the influence of adults, parents, and teachers in this period. Therefore, this research paper aims to explore the potential impact of infatuation and romantic relation to learning behaviour of school-going adolescents. Total of 108 adolescent students were surveyed using the questionnaire method. Both the primary and secondary data are used in this study and it is (...)
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