50,715 research outputs found

    Reinventing College Physics for Biologists: Explicating an epistemological curriculum

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    The University of Maryland Physics Education Research Group (UMd-PERG) carried out a five-year research project to rethink, observe, and reform introductory algebra-based (college) physics. This class is one of the Maryland Physics Department's large service courses, serving primarily life-science majors. After consultation with biologists, we re-focused the class on helping the students learn to think scientifically -- to build coherence, think in terms of mechanism, and to follow the implications of assumptions. We designed the course to tap into students' productive conceptual and epistemological resources, based on a theoretical framework from research on learning. The reformed class retains its traditional structure in terms of time and instructional personnel, but we modified existing best-practices curricular materials, including Peer Instruction, Interactive Lecture Demonstrations, and Tutorials. We provided class-controlled spaces for student collaboration, which allowed us to observe and record students learning directly. We also scanned all written homework and examinations, and we administered pre-post conceptual and epistemological surveys. The reformed class enhanced the strong gains on pre-post conceptual tests produced by the best-practices materials while obtaining unprecedented pre-post gains on epistemological surveys instead of the traditional losses.Comment: 35 pages including a 15 page appendix of supplementary material

    Research on learning: Potential for improving college ecology teaching

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    Provides pedagogical insight concerning learners' pre-conceptions and misconceptions about ecology The resource being annotated is: http://www.dlese.org/dds/catalog_DLESE-000-000-003-202.htm

    Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks

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    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain

    Technology Solutions for Developmental Math: An Overview of Current and Emerging Practices

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    Reviews current practices in and strategies for incorporating innovative technology into the teaching of remedial math at the college level. Outlines challenges, emerging trends, and ways to combine technology with new concepts of instructional strategy

    Disciplinary integration of digital games for science learning

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    A Teacher in the Living Room? Educational Media for Babies, Toddlers, and Preschoolers

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    Examines available research, and arguments by proponents and critics, of electronic educational media use by young children. Examines educational claims in marketing and provides recommendations for developing research and product standards

    Applying science of learning in education: Infusing psychological science into the curriculum

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    The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

    Learning analytics and psychophysiology : understanding the learning process in a STEM game

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    This study focuses on the exploration of player experience in educational games and its potential impact on predicting learning outcomes. Specifically, the research aims to investigate the connection psychophysiology data, obtained through a summative study involving nine participants, and the results of a learning analytics model derived from a larger field test. The study incorporates eye tracking and electrodermal activity data to gain insights into the predictive power of this data. Through the analysis of player experience data, the study sheds light on the factors that contribute to effective educational game design. By examining the eye tracking and EDA data, the researchers explored the participants' engagement levels, attention patterns, and emotional arousal during gameplay. These findings revealed a connection between spikes of visual attention and EDA during interactions with character faces as well as in game cinematics. In conclusion, the outcomes of this study provide valuable insights for future educational game designers. By understanding the relationship between user experience indicators and learning analytics, designers can tailor game elements to enhance engagement, attention, and emotional arousal, ultimately leading to improved learning outcomes. The integration of eye tracking and EDA data in user experience studies adds a new dimension to the evaluation and design of educational games. The findings pave the way for future research in the field and highlight the importance of considering user experience as a crucial factor in educational game design and development.Includes bibliographical references

    SOCIETY, SCIENCE, AND ECONOMICS: THE DELICATE BALANCE BETWEEN IDEOLOGY AND EPISTEMOLOGY AND THE CONCEPT OF FAIRNESS

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    Ideological Dominance and Epistemological Relevance; Modelling and Theory Formulation; Instrumentalism; Free Market and Regulated Market; Theory of Imperfect Competition, Fairness, Consensual Correctness, Representational Correctness.

    Behavioral Economics: Past, Present, Future

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    Behavioral economics increases the explanatory power of economics by providing it with more realistic psychological foundations. This book consists of representative recent articles in behavioral economics. This chapter is intended to provide an introduction to the approach and methods of behavioral economics, and to some of its major findings, applications, and promising new directions. It also seeks to fill some unavoidable gaps in the chapters’ coverage of topics
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