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Learning

Distribution by Scientific Domains
Medical Sciences23%
Education19%
Humanities and Social Sciences14%
Business, Economics, Finance and Accounting13%
Life Sciences13%
Psychology8%
Engineering2%
Information Science and Computing2%
5 Other Domains6%
Distribution within Medical Sciences
Medical Professional Development17%
Nursing General13%
55 Other Subdomains70%

Kinds of Learning

  • action learning
  • active learning
  • adaptive learning
  • adult learning
  • association learning
  • associative learning
  • avoidance learning
  • category learning
  • children learning
  • clinical learning
  • collaborative learning
  • complex learning
  • continuous learning
  • cooperative learning
  • cost-sensitive learning
  • cultural learning
  • deep learning
  • discrimination learning
  • distance learning
  • early learning
  • effective learning
  • emotional learning
    		•
  • experiential learning
  • foreign language learning
  • formal learning
  • generative learning
  • hippocampus-dependent learning
  • impaired learning
  • implicit learning
  • individual learning
  • informal learning
  • initial learning
  • institutional learning
  • interactive learning
  • interprofessional learning
  • language learning
  • life-long learning
  • lifelong learning
  • machine learning
  • meaningful learning
  • motor learning
  • mutual learning
  • new learning
  • olfactory learning
    		•
  • on-the-job learning
  • online learning
  • organisational learning
  • organizational learning
  • own learning
  • passive avoidance learning
  • perceptual learning
  • personal learning
  • policy learning
  • previous learning
  • procedural learning
  • professional learning
  • reflective learning
  • reinforcement learning
  • response learning
  • reversal learning
  • reward learning
  • science learning
  • second language learning
  • self-directed learning
  • self-regulated learning
  • service learning
    		•
  • shared learning
  • situated learning
  • skill learning
  • social learning
  • song learning
  • spatial learning
  • statistical learning
  • student learning
  • supervised learning
  • support learning
  • support student learning
  • teacher learning
  • team learning
  • technology-enhanced learning
  • transformative learning
  • verbal learning
  • vocabulary learning
  • vocal learning
  • word learning
  • workplace learning

    • Terms modified by Learning

  • learning ability
  • learning activity
  • learning agenda
  • learning algorithm
  • learning algorithms
  • learning approach
  • learning behavior
  • learning behaviour
  • learning by doing
  • learning capability
  • learning center
  • learning climate
  • learning community
  • learning context
  • learning control
  • learning course
  • learning culture
  • learning curriculum
  • learning curve
  • learning cycle
  • learning deficit
  • learning design
  • learning difficulty
  • learning disabilities
    		•
  • learning disability
  • learning disability services
  • learning disorders
  • learning economy
  • learning effect
  • learning effects
  • learning english
  • learning environment
  • learning event
  • learning exercise
  • learning experience
  • learning framework
  • learning gain
  • learning goal
  • learning guide
  • learning impairment
  • learning literature
  • learning material
  • learning mechanism
  • learning method
  • learning methods
  • learning model
  • learning models
  • learning module
    		•
  • learning motivation
  • learning need
  • learning object
  • learning objective
  • learning opportunity
  • learning organization
  • learning orientation
  • learning outcome
  • learning package
  • learning paradigm
  • learning performance
  • learning period
  • learning perspective
  • learning phase
  • learning point
  • learning practice
  • learning preference
  • learning principle
  • learning problem
  • learning process
  • learning program
  • learning programme
  • learning project
  • learning rate
    		•
  • learning research
  • learning resource
  • learning rule
  • learning set
  • learning situation
  • learning space
  • learning strategy
  • learning style
  • learning success
  • learning support
  • learning system
  • learning take
  • learning task
  • learning technique
  • learning techniques
  • learning technology
  • learning test
  • learning theory
  • learning time
  • learning tool
  • learning transfer
  • learning transfer system inventory
  • learning vector quantization

    • Selected Abstracts

    ON SOCIAL LEARNING AND ROBUST EVOLUTIONARY ALGORITHM DESIGN IN THE COURNOT OLIGOPOLY GAME

    COMPUTATIONAL INTELLIGENCE, Issue 2 2007
    Floortje Alkemade
    Agent-based computational economics (ACE) combines elements from economics and computer science. In this article, the focus is on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. This article compares two important approaches that are dominating in ACE and shows that the above practice may hinder the performance of the genetic algorithm and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. [source]

    APPLYING MACHINE LEARNING TO LOW-KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMS

    COMPUTATIONAL INTELLIGENCE, Issue 4 2005
    Tom Carchrae
    This paper addresses the question of allocating computational resources among a set of algorithms to achieve the best performance on scheduling problems. Our primary motivation in addressing this problem is to reduce the expertise needed to apply optimization technology. Therefore, we investigate algorithm control techniques that make decisions based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach "low knowledge" since it does not rely on complex prediction models, either of the problem domain or of algorithm behavior. We show that a low-knowledge approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low-knowledge approach achieves performance equivalent to a perfect high-knowledge classification approach. [source]

    THE GOVERNMENTALIZATION OF LEARNING AND THE ASSEMBLAGE OF A LEARNING APPARATUS

    EDUCATIONAL THEORY, Issue 4 2008
    Maarten Simons
    Doubting whether these concepts and related historical-analytical perspectives are still useful, the authors suggest the concept "learning apparatus" as a point of departure for an analysis of the "grammar of learning." They draw on Michel Foucault's analysis of governmentality to describe how learning has become a matter of both government and self-government. In describing the governmentalization of learning and the current assemblage of a "learning apparatus," Simons and Masschelein indicate how the concept of learning has become disconnected from education and teaching and has instead come to refer to a kind of capital, to something for which the learner is personally responsible, to something that can and should be managed, and to something that must be employable. Finally, the authors elaborate how these discourses combine to play a crucial role in contemporary advanced liberalism that seeks to promote entrepreneurship. [source]

    HUMAN CAPITAL AND THE LABOR OF LEARNING: A CASE OF MISTAKEN IDENTITY

    EDUCATIONAL THEORY, Issue 2 2007
    Alexander M Sidorkin
    Specifically, human capital theorists underestimate the private cost of schooling by taking low-level manual labor as the basis for estimating students' forgone earnings. This does not take into consideration the nature of students' labor of learning. In the essay, Sidorkin describes student work as a form of labor, not an investment activity, and considers the implications such an understanding of student work has for school reform. [source]

    RULES, TECHNIQUE, AND PRACTICAL KNOWLEDGE: A WITTGENSTEINIAN EXPLORATION OF VOCATIONAL LEARNING

    EDUCATIONAL THEORY, Issue 4 2006
    Christopher WinchArticle first published online: 30 NOV 200
    He argues that most rule-following is only successful when it involves a degree of flexibility. For instance, most technical work that involves rule-following requires flexibility and situational awareness for success. Technical education that fails to take account of the need to apply rules in a way that accounts for a wide variety of situations is likely to be unsuccessful. Winch offers an account of professional judgment based on Stephen Toulmin's theory of argumentation and discusses progression from novice to expert in terms of Toulmin's analysis. He also considers the relation between vocational education and other practices in the context of the wider civic implications of occupational practice. [source]

    PEAK SHIFT DISCRIMINATION LEARNING AS A MECHANISM OF SIGNAL EVOLUTION

    EVOLUTION, Issue 6 2005
    Spencer K. Lynn
    Abstract "Peak shift" is a behavioral response bias arising from discrimination learning in which animals display a directional, but limited, preference for or avoidance of unusual stimuli. Its hypothesized evolutionary relevance has been primarily in the realm of aposematic coloration and limited sexual dimorphism. Here, we develop a novel functional approach to peak shift, based on signal detection theory, which characterizes the response bias as arising from uncertainty about stimulus appearance, frequency, and quality. This approach allows the influence of peak shift to be generalized to the evolution of signals in a variety of domains and sensory modalities. The approach is illustrated with a bumblebee (Bombus impatiens) discrimination learning experiment. Bees exhibited peak shift while foraging in an artificial Batesian mimicry system. Changes in flower abundance, color distribution, and visitation reward induced bees to preferentially visit novel flower colors that reduced the risk of flower-type misidentification. Under conditions of signal uncertainty, peak shift results in visitation to rarer, but more easily distinguished, morphological variants of rewarding species in preference to their average morphology. Peak shift is a common and taxonomically widespread phenomenon. This example of the possible role of peak shift in signal evolution can be generalized to other systems in which a signal receiver learns to make choices in situations in which signal variation is linked to the sender's reproductive success. [source]

    SONG LEARNING ACCELERATES ALLOPATRIC SPECIATION

    EVOLUTION, Issue 9 2004
    R. F. Lachlan
    Abstract The songs of many birds are unusual in that they serve a role in identifying conspecific mates, yet they are also culturally transmitted. Noting the apparently high rate of diversity in one avian taxon, the songbirds, in which song learning appears ubiquitous, it has often been speculated that cultural transmission may increase the rate of speciation. Here we examine the possibility that song learning affects the rate of allopatric speciation. We construct a population-genetic model of allopatric divergence that explores the evolution of genes that underlie learning preferences (predispositions to learn some songs over others). We compare this with a model in which mating signals are inherited only genetically. Models are constructed for the cases where songs and preferences are affected by the same or different loci, and we analyze them using analytical local stability analysis combined with simulations of drift and directional sexual selection. Under nearly all conditions examined, song divergence occurs more readily in the learning model than in the nonlearning model. This is a result of reduced frequency-dependent selection in the learning models. Cultural evolution causes males with unusual genotypes to tend to learn from the majority of males around them, and thus develop songs compatible with the majority of the females in the population. Unusual genotypes can therefore be masked by learning. Over a wide range of conditions, learning therefore reduces the waiting time for speciation to occur and can be predicted to accelerate the rate of speciation. [source]

    TECHNOLOGICAL PROGRESS UNDER LEARNING BY IMITATION,

    INTERNATIONAL ECONOMIC REVIEW, Issue 2 2009
    Morgan Kelly
    I analyze technological progress when knowledge has a large tacit component so that transmission of knowledge takes place through direct personal imitation. It is shown that the rate of technological progress depends on the number of innovators in the same knowledge network. Assuming the diffusion of knowledge to mirror the geographical pattern of trade,the greater the trade between two sites, the greater the probability that technical knowledge flows between them,I show that a gradual expansion of trade causes a sudden rise in the rate of technological progress. [source]

    MORAL AGENCY AND THE UNITY OF THE WORLD: THE NEO-CONFUCIAN CRITIQUE OF "VULGAR LEARNING"

    JOURNAL OF CHINESE PHILOSOPHY, Issue 4 2006
    YOUNGMIN KIM
    [source]

    ORDINARY LEAST SQUARES LEARNING AND NONLINEARITIES IN MACROECONOMICS

    JOURNAL OF ECONOMIC SURVEYS, Issue 1 2010
    Orlando Gomes
    Abstract The paper reviews the literature on adaptive learning in macroeconomic settings where the formation of expectations is particularly relevant. Special attention will be given to simple two-period overlapping generations models with a unique fixed point perfect foresight equilibrium; in this kind of scenario, eventual long-term periodic and a-periodic cycles are exclusively the result of the process of learning. The outcome that high rates of money growth have a potentially destabilizing effect generating periodic fluctuations and chaos is emphasized. The persistence of systematic forecast errors in a scenario where agents are supposed to act rationally is relevant in this context and it will be thoroughly discussed resorting to the notions of self-fulfilling mistakes, consistent expectations equilibria and beliefs equilibria. [source]

    TEAM LEARNING VERSUS TRADITIONAL LECTURE: MEASURING THE EFFICACY OF TEACHING METHOD IN LEGAL STUDIES

    JOURNAL OF LEGAL STUDIES EDUCATION, Issue 1 2001
    Laurie A. Lucas
    [source]

    1. BRAIN-BASED LEARNING: Introduction

    JOURNAL OF PHILOSOPHY OF EDUCATION, Issue 3-4 2008
    Article first published online: 22 JAN 200
    [source]

    SUPPLIER INNOVATIVENESS AND THE ROLE OF INTERORGANIZATIONAL LEARNING IN ENHANCING MANUFACTURER CAPABILITIES

    JOURNAL OF SUPPLY CHAIN MANAGEMENT, Issue 4 2008
    ARASH AZADEGAN
    Manufacturers increasingly rely on innovation from their suppliers to improve the cost, quality, and timeliness of their products. Manufacturing capabilities are enhanced by supplier innovativeness directly, because of the embedded nature of the supplied component, and indirectly, as the manufacturer learns from its suppliers. We use organizational learning theory to develop a conceptual model of learning factors that act as contingencies and magnify the effect of supplier innovativeness. First, we argue that a manufacturer's absorptive capacity, its ability to learn and use external knowledge, positively moderates the impact of supplier innovativeness on the manufacturer's performance. Second, we examine how different combinations of manufacturer,supplier learning styles lead to relatively more or less interorganizational learning, contingent upon whether the outsourcing is design versus manufacturing oriented. Our model can help managers consider knowledge transfer as part of their supplier selection criteria. [source]

    II. A CONNECTIONIST MODEL FOR EARLY LEARNING ABOUT ANIMATES AND INANIMATES

    MONOGRAPHS OF THE SOCIETY FOR RESEARCH IN CHILD DEVELOPMENT, Issue 1 2008
    Article first published online: 16 MAR 200
    First page of article [source]

    ASSOCIATIONIST LEARNING AS A BASIS OF KNOWLEDGE IN INFANCY

    MONOGRAPHS OF THE SOCIETY FOR RESEARCH IN CHILD DEVELOPMENT, Issue 1 2008
    Lisa M. Oakes
    First page of article [source]

    SOLVING DYNAMIC WILDLIFE RESOURCE OPTIMIZATION PROBLEMS USING REINFORCEMENT LEARNING

    NATURAL RESOURCE MODELING, Issue 1 2005
    CHRISTOPHER J. FONNESBECK
    ABSTRACT. An important technical component of natural resource management, particularly in an adaptive management context, is optimization. This is used to select the most appropriate management strategy, given a model of the system and all relevant available information. For dynamic resource systems, dynamic programming has been the de facto standard for deriving optimal state-specific management strategies. Though effective for small-dimension problems, dynamic programming is incapable of providing solutions to larger problems, even with modern microcomputing technology. Reinforcement learning is an alternative, related procedure for deriving optimal management strategies, based on stochastic approximation. It is an iterative process that improves estimates of the value of state-specific actions based in interactions with a system, or model thereof. Applications of reinforcement learning in the field of artificial intelligence have illustrated its ability to yield near-optimal strategies for very complex model systems, highlighting the potential utility of this method for ecological and natural resource management problems, which tend to be of high dimension. I describe the concept of reinforcement learning and its approach of estimating optimal strategies by temporal difference learning. I then illustrate the application of this method using a simple, well-known case study of Anderson [1975], and compare the reinforcement learning results with those of dynamic programming. Though a globally-optimal strategy is not discovered, it performs very well relative to the dynamic programming strategy, based on simulated cumulative objective return. I suggest that reinforcement learning be applied to relatively complex problems where an approximate solution to a realistic model is preferable to an exact answer to an oversimplified model. [source]

    DISPOSITIONAL INFLUENCES ON TRANSFER OF LEARNING IN MULTISTAGE TRAINING PROGRAMS

    PERSONNEL PSYCHOLOGY, Issue 4 2002
    DAVID M. HEROLD
    The training effectiveness literature has paid little attention to the potentially dynamic interaction of individual differences with various phases of training in determining ultimate training success. This study investigates the role of individual differences in explaining the transfer of learning from 1 phase of training to another among pilot trainees in a multistage, aviation training program. Using 3 of the Big Five factors (Conscientiousness, Emotional Stability, Openness to Experience), the results showed these measures to contribute to the prediction of the number of hours it took for trainees to attain their private pilot's license. Significant interactions between some of these measures and success on an earlier, simulator phase of the training program were also found. The results are discussed in terms of both the role of individual differences in training research as well as the broader issue of transfer of learning between phases of training. [source]

    THE EFFECT OF LEARNING ON THE MAKE/BUY DECISION

    PRODUCTION AND OPERATIONS MANAGEMENT, Issue 3 2002
    EDWARD G. ANDERSON JR.
    By including the effects of learning over time on both the production of components and their integration into complete products, we develop an engineering-based model of outsourcing. This model provides an alternative explanation for much of what other outsourcing theories predict, as well as making several new predictions. In particular, we show that outsourcing decisions can create a path-dependent outsourcing trap in which a firm experiences higher long-run costs after an immediate cost benefit. We also describe conditions under which outsourcing a small fraction of component production may dominate either complete insourcing or complete outsourcing. Finally, we show that, with discounting, there is a convex, curvilinear relationship between the optimal outsourcing fraction and the rate of technological change. [source]

    POWER LEARNING OR PATH DEPENDENCY?

    PUBLIC ADMINISTRATION, Issue 2 2010
    INVESTIGATING THE ROOTS OF THE EUROPEAN FOOD SAFETY AUTHORITY
    A key motive for establishing the European Food Safety Authority (EFSA) was restoring public confidence in the wake of multiplying food scares and the BSE crisis. Scholars, however, have paid little attention to the actual political and institutional logics that shaped this new organization. This article explores the dynamics underpinning the making of EFSA. We examine the way in which learning and power shaped its organizational architecture. It is demonstrated that the lessons drawn from the past and other models converged on the need to delegate authority to an external agency, but diverged on its mandate, concretely whether or not EFSA should assume risk management responsibilities. In this situation of competitive learning, power and procedural politics conditioned the mandate granted to EFSA. The European Commission, the European Parliament and the European Council shared a common interest in preventing the delegation of regulatory powers to an independent EU agency in food safety policy. [source]

    INNOVATION CYCLES AND LEARNING AT THE PATENT OFFICE: DOES THE EARLY PATENT GET THE DELAY?,

    THE JOURNAL OF INDUSTRIAL ECONOMICS, Issue 2 2010
    PIERRE RÉGIBEAU
    We study the relationship between the length of patent review and the importance of inventions. We build a simple model of the U.S. patent review process. Among the model predictions are that, controlling for a patent's position in a new technology cycle, more important innovations would be approved more quickly. Also, the approval delay is likely to decrease as an industry moves from the early stages of an innovation cycle to later stages. These predictions are in line with the evidence we obtain from a data set on U.S. patents granted in the field of genetically modified crops from 1983 to 1999. We also show that failing to account for the innovation lifecycle , as previous studies have done , is likely to bias upwards the estimates of the relationship between delay and importance. [source]

    UNCERTAINTY, LEARNING AND GROWTH

    THE MANCHESTER SCHOOL, Issue 5 2008
    RAGCHAASUREN GALINDEV
    The paper extends Blackburn and Galindev's (Economics Letters, Vol. 79 (2003), pp. 417,421) stochastic growth model in which productivity growth entails both external and internal learning behaviour with a constant relative risk aversion utility function and productivity shocks. Consequently, the relationship between long-term growth and short-term volatility depends not only on the relative importance of each learning mechanism but also on a parameter measuring individuals' attitude towards risk. [source]

    LEARNING, EXTERNALITIES, AND THE SALE OF INVENTIONS TO FIRMS WITH CORRELATED VALUATIONS

    AUSTRALIAN ECONOMIC PAPERS, Issue 4 2004
    JOHN T. KING
    I examine how an inventor's ability to learn affects the bargaining outcome when she attempts to sell a discovery to one of two oligopolistically competitive firms with correlated and private valuations. It is shown that learning gives the inventor an incentive to lower her proposed price to the first firm approached since being rejected would cause her to be pessimistic when dealing with the second firm. At the same time, however, the inventor would like to raise her proposed price since this pessimism is weaker if she is rejected upon making a high proposal. Another incentive to raise the proposal comes from the fact that learning increases the first firm's willingness to pay for the invention. Computational results suggest that the first effect dominates and thus the inventor lowers her proposal in the first round. When dealing with the second firm, it is shown that learning results in a lower equilibrium proposal and contracting with more types. Moreover, it is shown that the cost of lowering the proposed price outweighs the benefit of contracting with more types so that learning in general reduces the continuation value associated with contracting in the second round. [source]

    Creative learning 3-11 and how we document it , Edited by Anna Craft Creative LEARNING,Activities and games that REALLY engage people , By Robert W Lucas

    BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, Issue 3 2009
    John Cowan
    No abstract is available for this article. [source]

    ON MULTI-CLASS COST-SENSITIVE LEARNING

    COMPUTATIONAL INTELLIGENCE, Issue 3 2010
    Zhi-Hua Zhou
    Rescaling,is possibly the most popular approach to cost-sensitive learning. This approach works by rebalancing the classes according to their costs, and it can be realized in different ways, for example, re-weighting or resampling the training examples in proportion to their costs, moving the decision boundaries of classifiers faraway from high-cost classes in proportion to costs, etc. This approach is very effective in dealing with two-class problems, yet some studies showed that it is often not so helpful on multi-class problems. In this article, we try to explore why the rescaling approach is often helpless on multi-class problems. Our analysis discloses that the rescaling approach works well when the costs are,consistent, while directly applying it to multi-class problems with,inconsistent,costs may not be a good choice. Based on this recognition, we advocate that before applying the rescaling approach, the,consistency,of the costs must be examined at first. If the costs are consistent, the rescaling approach can be conducted directly; otherwise it is better to apply rescaling after decomposing the multi-class problem into a series of two-class problems. An empirical study involving 20 multi-class data sets and seven types of cost-sensitive learners validates our proposal. Moreover, we show that the proposal is also helpful for class-imbalance learning. [source]

    LEARNING TO SOLVE PROBLEMS FROM EXERCISES

    COMPUTATIONAL INTELLIGENCE, Issue 4 2008
    Prasad Tadepalli
    It is a common observation that learning easier skills makes it possible to learn the more difficult skills. This fact is routinely exploited by parents, teachers, textbook writers, and coaches. From driving, to music, to science, there hardly exists a complex skill that is not learned by gradations. Natarajan's model of "learning from exercises" captures this kind of learning of efficient problem solving skills using practice problems or exercises (Natarajan 1989). The exercises are intermediate subproblems that occur in solving the main problems and span all levels of difficulty. The learner iteratively bootstraps what is learned from simpler exercises to generalize techniques for solving more complex exercises. In this paper, we extend Natarajan's framework to the problem reduction setting where problems are solved by reducing them to simpler problems. We theoretically characterize the conditions under which efficient learning from exercises is possible. We demonstrate the generality of our framework with successful implementations in the Eight Puzzle, symbolic integration, and simulated robot planning domains illustrating three different representations of control knowledge, namely, macro-operators, control rules, and decision lists. The results show that the learning rates for the exercises framework are competitive with those for learning from problems solved by the teacher. [source]

    Web Discovery and Filtering Based on Textual Relevance Feedback Learning

    COMPUTATIONAL INTELLIGENCE, Issue 2 2003
    Wai Lam
    We develop a new approach for Web information discovery and filtering. Our system, called WID, allows the user to specify long-term information needs by means of various topic profile specifications. An entire example page or an index page can be accepted as input for the discovery. It makes use of a simulated annealing algorithm to automatically explore new Web pages. Simulated annealing algorithms possess some favorable properties to fulfill the discovery objectives. Information retrieval techniques are adopted to evaluate the content-based relevance of each page being explored. The hyperlink information, in addition to the textual context, is considered in the relevance score evaluation of a Web page. WID allows users to provide three forms of the relevance feedback model, namely, the positive page feedback, the negative page feedback, and the positive keyword feedback. The system is domain independent and does not rely on any prior knowledge or information about the Web content. Extensive experiments have been conducted to demonstrate the effectiveness of the discovery performance achieved by WID. [source]

    Learning-based 3D face detection using geometric context

    COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 4-5 2007
    Yanwen Guo
    Abstract In computer graphics community, face model is one of the most useful entities. The automatic detection of 3D face model has special significance to computer graphics, vision, and human-computer interaction. However, few methods have been dedicated to this task. This paper proposes a machine learning approach for fully automatic 3D face detection. To exploit the facial features, we introduce geometric context, a novel shape descriptor which can compactly encode the distribution of local geometry and can be evaluated efficiently by using a new volume encoding form, named integral volume. Geometric contexts over 3D face offer the rich and discriminative representation of facial shapes and hence are quite suitable to classification. We adopt an AdaBoost learning algorithm to select the most effective geometric context-based classifiers and to combine them into a strong classifier. Given an arbitrary 3D model, our method first identifies the symmetric parts as candidates with a new reflective symmetry detection algorithm. Then uses the learned classifier to judge whether the face part exists. Experiments are performed on a large set of 3D face and non-face models and the results demonstrate high performance of our method. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    New methodologies in teaching e-structural mechanics using WWW,

    COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, Issue 3 2008
    Carmelo Maiorana
    Abstract A recently initiated phase of experimentation and research in the online Distance Learning (DL) is here described. The project has been developed by the Department of Construction and Transportation Engineering of the Faculty of Engineering at the University of Padua along with the well-established e-learning experience of the SSIS Veneto (Institute for the Formation of Secondary School's Teachers) of Cŕ Foscari,University of Venice, in collaboration with the webmaster management of TCN-EnginSoft of Padua. The work deals with teaching methodologies supported by the net, computer communication and information technologies, finalized to give both widespread access to useful resources and to create a more flexible exchange due to net communication. The experimentation of using web-based technologies to support traditional teaching for working students is described; in fact, Internet-based innovations offer opportunities for a curriculum improvement to those categories of students who could be considered at a disadvantage, like worker students or students with ear or motion deafness. © 2008 Wiley Periodicals, Inc. Comput Appl Eng Educ 16: 189,210, 2008; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae20167 [source]

    Teaching, Exploring, Learning,Developing Tutorials for In-Class Teaching and Self-Learning

    COMPUTER GRAPHICS FORUM, Issue 4 2007
    S. Beckhaus
    Abstract This paper presents an experience report on a novel approach for a course on intermediate and advanced computer graphics topics. The approach uses Teachlet Tutorials, a combination of traditional seminar,type teaching with interactive exploration of the content by the audience, plus development of self-contained tutorials on the topic. In addition to a presentation, an interactive software tool is developed by the students to assist the audience in learning and exploring the topic's details. This process is guided through set tasks. The resulting course material is developed for two different contexts: (a) for classroom presentation and (b) as an interactive, self-contained, self-learning tutorial. The overall approach results in a more thorough understanding of the topic both for the student teachers as well as for the class participants. In addition to detailing the Teachlet Tutorial approach, this paper presents our experiences implementing the approach in our Advanced Computer Graphics course and presents the resultant projects. Most of the final Teachlet Tutorials were surprisingly good and we had excellent feedback from the students on the approach and course. [source]

    Learning with an active e-course in the Knowledge Grid environment

    CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 3 2006
    Hai Zhuge
    Abstract An active e-course is an open, self-representable and self-organizable media mechanism. Its kernel idea is to organize learning materials in a concept space rather than in a page space. The tailored content and flexible structure of the e-courses can be dynamically formed to cater for different learners with different backgrounds, capabilities and expectations, at different times and venues. The active e-course can also assess learners' learning performances and give appropriate suggestions to guide them in further learning. An authoring tool for constructing course ontology and a system prototype have been developed to support an active e-course, enabling a learner-centred, highly interactive and adaptive learning approach. The results of an empirical study show that the system can help enhance the effectiveness and efficiency of learning. Copyright © 2005 John Wiley & Sons, Ltd. [source]