Generative Learning Objects Instantiated with Random Numbers Based Expressions

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Ciprian Bogdan Chirila

2015-12-01

Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

Learning Faster by Discovering and Exploiting Object Similarities

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Tadej Janež

2013-03-01

Full Text Available In this paper we explore the question: “Is it possible to speed up the learning process of an autonomous agent by performing experiments in a more complex environment (i.e., an environment with a greater number of different objects?” To this end, we use a simple robotic domain, where the robot has to learn a qualitative model predicting the change in the robot's distance to an object. To quantify the environment's complexity, we defined cardinal complexity as the number of objects in the robot's world, and behavioural complexity as the number of objects' distinct behaviours. We propose Error reduction merging (ERM, a new learning method that automatically discovers similarities in the structure of the agent's environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent's learning. We performed a series of experiments in worlds of increasing complexity. The results in our simple domain indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.

Error-Driven Learning in Visual Categorization and Object Recognition: A Common-Elements Model

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Soto, Fabian A.; Wasserman, Edward A.

2010-01-01

A wealth of empirical evidence has now accumulated concerning animals' categorizing photographs of real-world objects. Although these complex stimuli have the advantage of fostering rapid category learning, they are difficult to manipulate experimentally and to represent in formal models of behavior. We present a solution to the representation…

On hierarchical models for visual recognition and learning of objects, scenes, and activities

CERN Document Server

Spehr, Jens

2015-01-01

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model...

Repurposeable Learning Objects Linked to Teaching and Learning Styles

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Jeremy Dunning

2004-02-01

Full Text Available Multimedia learning objects are an essential component of high quality, technology-mediated instruction. Learning objects allow the student to use the content learned in a particular part of a course and; 1. demonstrate mastery of the content, 2. apply that knowledge to solving a problem, and 3. use the content in a critical thinking exercise that both demonstrates mastery and allows the student to place the content within the context of the larger topic of the course. The difficulty associated with the use of learning objects on a broad scale is that they require programming skills most professors and instructors do not possess. Learning objects also tend to be custom productions and are defined in terms of the programming and code terminology, further limiting the professor's ability to understand how they are created. Learning objects defined in terms of styles of learning and teaching allow professors and instructors to develop a deeper understanding of the learning objects and the design process. A set of learning objects has been created that are designed for some of the important styles of learning and teaching. They include; visual learning, writing skills, critical thinking, time-revealed scenarios, case studies and empirical observation. The learning objects are designed and described in terms that the average instructor can readily understand , redesign and incorporate into their own courses. They are also designed in such a way that they can readily be repurposed for new applications in other courses and subject areas, with little or no additional programming.

Diagram, a Learning Environment for Initiation to Object-Oriented Modeling with UML Class Diagrams

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Py, Dominique; Auxepaules, Ludovic; Alonso, Mathilde

2013-01-01

This paper presents Diagram, a learning environment for object-oriented modelling (OOM) with UML class diagrams. Diagram an open environment, in which the teacher can add new exercises without constraints on the vocabulary or the size of the diagram. The interface includes methodological help, encourages self-correcting and self-monitoring, and…

Building a Smart E-Portfolio Platform for Optimal E-Learning Objects Acquisition

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Chih-Kun Ke

2013-01-01

Full Text Available In modern education, an e-portfolio platform helps students in acquiring e-learning objects in a learning activity. Quality is an important consideration in evaluating the desirable e-learning object. Finding a means of determining a high quality e-learning object from a large number of candidate e-learning objects is an important requirement. To assist student learning in a modern e-portfolio platform, this work proposed an optimal selection approach determining a reasonable e-learning object from various candidate e-learning objects. An optimal selection approach which uses advanced information techniques is proposed. Each e-learning object undergoes a formalization process. An Information Retrieval (IR technique extracts and analyses key concepts from the student’s previous learning contexts. A context-based utility model computes the expected utility values of various e-learning objects based on the extracted key concepts. The expected utility values of e-learning objects are used in a multicriteria decision analysis to determine the optimal selection order of the candidate e-learning objects. The main contribution of this work is the demonstration of an effective e-learning object selection method which is easy to implement within an e-portfolio platform and which makes it smarter.

Learning Objects Web

DEFF Research Database (Denmark)

Blåbjerg, Niels Jørgen

2005-01-01

Learning Objects Web er et DEFF projekt som Aalborg Universitetsbibliotek har initieret. Projektet tager afsæt i de resultater og erfaringer som er opnået med vores tidligere projekt Streaming Webbased Information Modules (SWIM). Vi har et internationalt netværk af interessenter som giver os...... sparring og feedback i forhold til udviklingskoncept både omkring de teoretiske rammer og i forhold til praktisk anvendelse af vores undervisningskoncept. Med disse rygstød og input har vi forfulgt ønsket om at videreudvikle SWIM i det nye projekt Learning Objects Web. Udgivelsesdato: juni...

A Learning Object Approach To Evidence based learning

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Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

OpenAIRE

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

2016-01-01

Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

Checklist for Evaluating SREB-SCORE Learning Objects

Science.gov (United States)

Southern Regional Education Board (SREB), 2007

2007-01-01

This checklist is based on "Evaluation Criteria for SREB-SCORE Learning Objects" and is designed to help schools and colleges determine the quality and effectiveness of learning objects. It is suggested that each learning object be rated to the extent to which it meets the criteria and the SREB-SCORE definition of a learning object. A learning…

Object interaction competence model v. 2.0

DEFF Research Database (Denmark)

Bennedsen, Jens; Schulte, C.

2013-01-01

Teaching and learning object oriented programming has to take into account the specific object oriented characteristics of program execution, namely the interaction of objects during runtime. Prior to the research reported in this article, we have developed a competence model for object interaction...

Constraints on reusability of learning objects

DEFF Research Database (Denmark)

May, Michael; Hussmann, Peter Munkebo; Jensen, Anne Skov

2010-01-01

It is the aim of this paper to discuss some didactic constraints on the use and reuse of digital modular learning objects. Engineering education is used as the specific context of use with examples from courses in introductory electronics and mathematics. Digital multimedia and modular learning....... Constraints on reuse arise from the nature of conceptual understanding in higher education and the functionality of learning objects within present technologies. We will need didactic as well as technical perspectives on learning objects in designing for understanding....

Mere exposure alters category learning of novel objects

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Jonathan R Folstein

2010-08-01

Full Text Available We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Mere exposure alters category learning of novel objects.

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Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Educational Rationale Metadata for Learning Objects

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Tom Carey

2002-10-01

Full Text Available Instructors searching for learning objects in online repositories will be guided in their choices by the content of the object, the characteristics of the learners addressed, and the learning process embodied in the object. We report here on a feasibility study for metadata to record process-oriented information about instructional approaches for learning objects, though a set of Educational Rationale [ER] tags which would allow authors to describe the critical elements in their design intent. The prototype ER tags describe activities which have been demonstrated to be of value in learning, and authors select the activities whose support was critical in their design decisions. The prototype ER tag set consists descriptors of the instructional approach used in the design, plus optional sub-elements for Comments, Importance and Features which implement the design intent. The tag set was tested by creators of four learning object modules, three intended for post-secondary learners and one for K-12 students and their families. In each case the creators reported that the ER tag set allowed them to express succinctly the key instructional approaches embedded in their designs. These results confirmed the overall feasibility of the ER tag approach as a means of capturing design intent from creators of learning objects. Much work remains to be done before a usable ER tag set could be specified, including evaluating the impact of ER tags during design to improve instructional quality of learning objects.

A unified computational model of the development of object unity, object permanence, and occluded object trajectory perception.

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Franz, A; Triesch, J

2010-12-01

The perception of the unity of objects, their permanence when out of sight, and the ability to perceive continuous object trajectories even during occlusion belong to the first and most important capacities that infants have to acquire. Despite much research a unified model of the development of these abilities is still missing. Here we make an attempt to provide such a unified model. We present a recurrent artificial neural network that learns to predict the motion of stimuli occluding each other and that develops representations of occluded object parts. It represents completely occluded, moving objects for several time steps and successfully predicts their reappearance after occlusion. This framework allows us to account for a broad range of experimental data. Specifically, the model explains how the perception of object unity develops, the role of the width of the occluders, and it also accounts for differences between data for moving and stationary stimuli. We demonstrate that these abilities can be acquired by learning to predict the sensory input. The model makes specific predictions and provides a unifying framework that has the potential to be extended to other visual event categories. Copyright © 2010 Elsevier Inc. All rights reserved.

Personalised Learning Object System Based on Self-Regulated Learning Theories

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Ali Alharbi

2014-06-01

Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

THE BLENDED LEARNING OF ELECTRICITY USING LEARNING OBJECTS IN ENGINEERING

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Lilia Maria Siqueira

2010-09-01

Full Text Available This work presents a proposal for the blended learning of Electricity education in Engineering, using resources called learning objects. The experience occurred with students enrolled on the Electrical Engineering and Computer Engineering courses at PUCPR University. It made possible the contact with interdisciplinary themes related to the study of electricity and the professional curriculum contents. The learning objects, offered during the semester, were anchored on PUCPR’s proprietary virtual educational environment, called Eureka. The students’ evaluation results showed that the study through learning objects in a virtual environment is significant for learning.

Learning Object Retrieval and Aggregation Based on Learning Styles

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Ramirez-Arellano, Aldo; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel

2017-01-01

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step…

Deep Learning through Reusable Learning Objects in an MBA Program

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Rufer, Rosalyn; Adams, Ruifang Hope

2013-01-01

It has well been established that it is important to be able to leverage any organization's processes and core competencies to sustain its competitive advantage. Thus, one learning objective of an online MBA is to teach students how to apply the VRIO (value, rarity, inimitable, operationalized) model, developed by Barney and Hesterly (2006), in…

Learning Ontology from Object-Relational Database

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Kaulins Andrejs

2015-12-01

Full Text Available This article describes a method of transformation of object-relational model into ontology. The offered method uses learning rules for such complex data types as object tables and collections – arrays of a variable size, as well as nested tables. Object types and their transformation into ontologies are insufficiently considered in scientific literature. This fact served as motivation for the authors to investigate this issue and to write the article on this matter. In the beginning, we acquaint the reader with complex data types and object-oriented databases. Then we describe an algorithm of transformation of complex data types into ontologies. At the end of the article, some examples of ontologies described in the OWL language are given.

Vicarious learning from human models in monkeys.

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Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

2012-01-01

We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

ROBOT LEARNING OF OBJECT MANIPULATION TASK ACTIONS FROM HUMAN DEMONSTRATIONS

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Maria Kyrarini

2017-08-01

Full Text Available Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW and Gaussian Mixture Model (GMM, the model of demonstrated trajectories is learned. The learned trajectory is generated by Gaussian mixture regression (GMR from the learned Gaussian mixture model.  In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully.

Vicarious Learning from Human Models in Monkeys

OpenAIRE

Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

2012-01-01

We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was app...

Methodology for Evaluating Quality and Reusability of Learning Objects

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Kurilovas, Eugenijus; Bireniene, Virginija; Serikoviene, Silvija

2011-01-01

The aim of the paper is to present the scientific model and several methods for the expert evaluation of quality of learning objects (LOs) paying especial attention to LOs reusability level. The activities of eQNet Quality Network for a European Learning Resource Exchange (LRE) aimed to improve reusability of LOs of European Schoolnet's LRE…

Real-world visual statistics and infants' first-learned object names.

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Clerkin, Elizabeth M; Hart, Elizabeth; Rehg, James M; Yu, Chen; Smith, Linda B

2017-01-05

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Vicarious learning from human models in monkeys.

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Rossella Falcone

Full Text Available We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

Liberating Learning Object Design from the Learning Style of Student Instructional Designers

Science.gov (United States)

Akpinar, Yavuz

2007-01-01

Learning objects are a new form of learning resource, and the design of these digital environments has many facets. To investigate senior instructional design students' use of reflection tools in designing learning objects, a series of studies was conducted using the Reflective Action Instructional Design and Learning Object Review Instrument…

Integrating language and content learning objectives : the Bilkent University adjunct model

OpenAIRE

Doğan, Egemen Barış

2003-01-01

Cataloged from PDF version of article. In response to a global interest in learning English, many instructional approaches, methods, and techniques have been developed. Some have been short-lived, and others have sustained themselves for longer periods of time. Content-based instruction (CBI) — a particular approach to CBI involving a pairing of language and content classes with shared language and content learning objectives — have been considered as viable ways to teach la...

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

Science.gov (United States)

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

An object-based visual attention model for robotic applications.

Science.gov (United States)

Yu, Yuanlong; Mann, George K I; Gosine, Raymond G

2010-10-01

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

How learning might strengthen existing visual object representations in human object-selective cortex.

Science.gov (United States)

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

Unifying Learning Object Repositories in MACE

NARCIS (Netherlands)

Prause, Christian; Ternier, Stefaan; De Jong, Tim; Apelt, Stefan; Scholten, Marius; Wolpers, Martin; Eisenhauer, Markus; Vandeputte, Bram; Specht, Marcus; Duval, Erik

2007-01-01

Prause, C., Ternier, S., De Jong, T., Apelt, S., Scholten, M., Wolpers, M., et al. (2007). Unifying Learning Object Repositories in MACE. In D. Massart, J.-N. Colin & F. V. Assche (Eds.). Proceedings of the First International Workshop on Learning Object Discovery & Exchange (LODE'07). September,

Authoring of Learning Objects in Context

Science.gov (United States)

Specht, Marcus; Kravcik, Milos

2006-01-01

Learning objects and content interchange standards provide new possibilities for e-learning. Nevertheless the content often lacks context data to find appropriate use for adaptive learning on demand and personalized learning experiences. In the Remotely Accessible Field Trips (RAFT) project mobile authoring of learning content in context has shown…

Large-scale weakly supervised object localization via latent category learning.

Science.gov (United States)

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Object-oriented user interfaces for personalized mobile learning

CERN Document Server

Alepis, Efthimios

2014-01-01

This book presents recent research in mobile learning and advanced user interfaces. It is shown how the combination of this fields can result in personalized educational software that meets the requirements of state-of-the-art mobile learning software. This book provides a framework that is capable of incorporating the software technologies, exploiting a wide range of their current advances and additionally investigating ways to go even further by providing potential solutions to future challenges. The presented approach uses the well-known Object-Oriented method in order to address these challenges. Throughout this book, a general model is constructed using Object-Oriented Architecture. Each chapter focuses on the construction of a specific part of this model, while in the conclusion these parts are unified. This book will help software engineers build more sophisticated personalized software that targets in mobile education, while at the same time retaining a high level of adaptivity and user-friendliness w...

An Exploratory Study into the Efficacy of Learning Objects

Directory of Open Access Journals (Sweden)

Nicholas W. Farha, Ph.D.

2009-07-01

Full Text Available Learning objects have quickly become a widely accepted approach to instructional technology, particularly in on-line and computer-based learning environments. While there is a substantial body of literature concerning learning objects, very little of it verifies their efficacy. This research investigated the effectiveness of learning objects by comparing learning outcomes using a learning object with outcomes using a traditional textbook-based method of instruction. Participants were 327 undergraduate college students at a traditional public four-year coed institution, a private four-year women’s college, a private four-year engineering institution, and a public two-year community college. Through a series of independent samples t-tests and Analyses of Variance, results revealed mean scores for the learning object group that were nearly three times higher than the mean scores for the textbook-taught group. Gaming experience, age, gender, and learner preference were evaluated for their potential influence on the results; no statistically significant differences were found, implying that the learning object itself was central to the outcomes achieved. The future of learning objects is bright, and more empirical research is called for in the area of learning object effectiveness.

Incremental online object learning in a vehicular radar-vision fusion framework

Energy Technology Data Exchange (ETDEWEB)

Ji, Zhengping [Los Alamos National Laboratory; Weng, Juyang [Los Alamos National Laboratory; Luciw, Matthew [IEEE; Zeng, Shuqing [IEEE

2010-10-19

In this paper, we propose an object learning system that incorporates sensory information from an automotive radar system and a video camera. The radar system provides a coarse attention for the focus of visual analysis on relatively small areas within the image plane. The attended visual areas are coded and learned by a 3-layer neural network utilizing what is called in-place learning, where every neuron is responsible for the learning of its own signal processing characteristics within its connected network environment, through inhibitory and excitatory connections with other neurons. The modeled bottom-up, lateral, and top-down connections in the network enable sensory sparse coding, unsupervised learning and supervised learning to occur concurrently. The presented work is applied to learn two types of encountered objects in multiple outdoor driving settings. Cross validation results show the overall recognition accuracy above 95% for the radar-attended window images. In comparison with the uncoded representation and purely unsupervised learning (without top-down connection), the proposed network improves the recognition rate by 15.93% and 6.35% respectively. The proposed system is also compared with other learning algorithms favorably. The result indicates that our learning system is the only one to fit all the challenging criteria for the development of an incremental and online object learning system.

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

Science.gov (United States)

Hsu, I-Ching

2012-01-01

The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability, and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM lacks the…

Learning Spatial Object Localization from Vision on a Humanoid Robot

Directory of Open Access Journals (Sweden)

Jürgen Leitner

2012-12-01

Full Text Available We present a combined machine learning and computer vision approach for robots to localize objects. It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range of objects seen. Biologically inspired approaches, such as Artificial Neural Networks (ANN and Genetic Programming (GP, are trained to provide these position estimates using the two cameras and the joint encoder readings. No camera calibration or explicit knowledge of the robot's kinematic model is needed. We find that ANN and GP are not just faster and have lower complexity than traditional techniques, but also learn without the need for extensive calibration procedures. In addition, the approach is localizing objects robustly, when placed in the robot's workspace at arbitrary positions, even while the robot is moving its torso, head and eyes.

Applying CIPP Model for Learning-Object Management

Science.gov (United States)

Morgado, Erla M. Morales; Peñalvo, Francisco J. García; Martín, Carlos Muñoz; Gonzalez, Miguel Ángel Conde

Although knowledge management process needs to receive some evaluation in order to determine their suitable functionality. There is not a clear definition about the stages where LOs need to be evaluated and the specific metrics to continuously promote their quality. This paper presents a proposal for LOs evaluation during their management for e-learning systems. To achieve this, we suggest specific steps for LOs design, implementation and evaluation into the four stages proposed by CIPP model (Context, Input, Process, Product).

Elaborazione didattica di Learning Objects.

Directory of Open Access Journals (Sweden)

Luigi Guerra

2006-01-01

Full Text Available L’idea di un modello didattico problematico per la realizzazione di Learning Objects riprende i temi del problematicismo pedagogico e si impegna a definire un’ipotesi formativa complessa capace di valorizzare la possibile positiva compresenza integrata di strategie didattiche diverse (finanche antitetiche ma componibili in una logica appunto di matrice problematicista. Il punto di partenza del modello proposto è rappresentato dalla opportunità di definire tre tipologie fondamentali di Learning Objects, rispettivamente centrati sull’oggetto, sul processo e sul soggetto dell’apprendimento.

Moving object detection in video satellite image based on deep learning

Science.gov (United States)

Zhang, Xueyang; Xiang, Junhua

2017-11-01

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Early age-dependent impairments of context-dependent extinction learning, object recognition, and object-place learning occur in rats.

Science.gov (United States)

Wiescholleck, Valentina; Emma André, Marion Agnès; Manahan-Vaughan, Denise

2014-03-01

The hippocampus is vulnerable to age-dependent memory decline. Multiple forms of memory depend on adequate hippocampal function. Extinction learning comprises active inhibition of no longer relevant learned information concurrent with suppression of a previously learned reaction. It is highly dependent on context, and evidence exists that it requires hippocampal activation. In this study, we addressed whether context-based extinction as well as hippocampus-dependent tasks, such as object recognition and object-place recognition, are equally affected by moderate aging. Young (7-8 week old) and older (7-8 month old) Wistar rats were used. For the extinction study, animals learned that a particular floor context indicated that they should turn into one specific arm (e.g., left) to receive a food reward. On the day after reaching the learning criterion of 80% correct choices, the floor context was changed, no reward was given and animals were expected to extinguish the learned response. Both, young and older rats managed this first extinction trial in the new context with older rats showing a faster extinction performance. One day later, animals were returned to the T-maze with the original floor context and renewal effects were assessed. In this case, only young but not older rats showed the expected renewal effect (lower extinction ratio as compared to the day before). To assess general memory abilities, animals were tested in the standard object recognition and object-place memory tasks. Evaluations were made at 5 min, 1 h and 7 day intervals. Object recognition memory was poor at short-term and intermediate time-points in older but not young rats. Object-place memory performance was unaffected at 5 min, but impaired at 1 h in older but not young rats. Both groups were impaired at 7 days. These findings support that not only aspects of general memory, but also context-dependent extinction learning, are affected by moderate aging. This may reflect less flexibility in

Transforming existing content into reusable Learning Objects

NARCIS (Netherlands)

Doorten, Monique; Giesbers, Bas; Janssen, José; Daniels, Jan; Koper, Rob

2003-01-01

Please cite as: Doorten, M., Giesbers, B., Janssen, J., Daniëls, J, & Koper, E.J.R., (2004). Transforming existing content into reusable learning objects. In R. McGreal, Online Education using Learning Objects (pp. 116-127). London: RoutledgeFalmer.

Learning objects and interactive whiteboards: a evaluation proposal of learning objects for mathematics teaching

Directory of Open Access Journals (Sweden)

Silvio Henrique Fiscarelli

2016-05-01

Full Text Available The current conditions of the classroom learning tend to be a one-way process based in teacher exposition, this make a negative impact on learning make it a mechanical and not meaningful activity. One possibility to improve the quality of teaching is to innovate methodologies and varying forms of presenting information to students, such as the use of technology in the teaching process. The Interactive Whiteboard (IBW is one of the technologies that are being implemented in Brazilian schools. One of the promising possibilities to add value to the use of LDI in classroom are "learning objects" (LO. However, one problem is that often the LO are not fully suited to the dynamics of IWB, whether functional or pedagogical point of view. The objective of this study is to analyze and propose a set of indicators that evaluate the learning objects for use in conjunction with Interactive Whiteboards. The selection and definition of evaluation indicators was carried from the literature review on the subject and based on LDI experiences of use in Municipal Elementary School. After defining the set of indicators was conducted a evaluation of a sample of 30 OA utilized to teaching mathematics in 3rd grade of elementary school. The results of the evaluation indicate that the proposed indicators are suitable for a pre-analysis of OA and assisting in the process of selection of these.

Object recognition and concept learning with Confucius

Energy Technology Data Exchange (ETDEWEB)

Cohen, B; Sammut, C

1982-01-01

A learning program produces, as its output, a Boolean function which describes a concept. The function returns true if and only if the argument is an object which satisfies the logical expression in the body of the function. The learning program's input is a set of objects which are instances of the concept to be learnt. The paper describes an algorithm devised to learn concept descriptions in this form. 15 references.

Learning Object-Orientation through ICT-mediated Apprenticeship

DEFF Research Database (Denmark)

Fjuk, A.; Berge, O.; Bennedsen, J.

2004-01-01

In this paper, we show how sociocultural theories inform the design of a course in object-oriented programming. An essential learning objective within this philosophy is the programming processes as such. To move toward this learning goal, the course design incorporates a combination of the so...

Tagging the didactic functionality of learning objects

DEFF Research Database (Denmark)

Hansen, Per Skafte; Brostroem, Stig

2002-01-01

From a components-in-a-network point of view, the most important issues are: a didactically based typing of the learning objects themselves; the entire design superstructure, into which the learning objects must be fitted; and the symmetry of the interfaces, as seen by each pair of the triad...

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

Science.gov (United States)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

How does the brain rapidly learn and reorganize view-invariant and position-invariant object representations in the inferotemporal cortex?

Science.gov (United States)

Cao, Yongqiang; Grossberg, Stephen; Markowitz, Jeffrey

2011-12-01

All primates depend for their survival on being able to rapidly learn about and recognize objects. Objects may be visually detected at multiple positions, sizes, and viewpoints. How does the brain rapidly learn and recognize objects while scanning a scene with eye movements, without causing a combinatorial explosion in the number of cells that are needed? How does the brain avoid the problem of erroneously classifying parts of different objects together at the same or different positions in a visual scene? In monkeys and humans, a key area for such invariant object category learning and recognition is the inferotemporal cortex (IT). A neural model is proposed to explain how spatial and object attention coordinate the ability of IT to learn invariant category representations of objects that are seen at multiple positions, sizes, and viewpoints. The model clarifies how interactions within a hierarchy of processing stages in the visual brain accomplish this. These stages include the retina, lateral geniculate nucleus, and cortical areas V1, V2, V4, and IT in the brain's What cortical stream, as they interact with spatial attention processes within the parietal cortex of the Where cortical stream. The model builds upon the ARTSCAN model, which proposed how view-invariant object representations are generated. The positional ARTSCAN (pARTSCAN) model proposes how the following additional processes in the What cortical processing stream also enable position-invariant object representations to be learned: IT cells with persistent activity, and a combination of normalizing object category competition and a view-to-object learning law which together ensure that unambiguous views have a larger effect on object recognition than ambiguous views. The model explains how such invariant learning can be fooled when monkeys, or other primates, are presented with an object that is swapped with another object during eye movements to foveate the original object. The swapping procedure is

Learning while Babbling: Prelinguistic Object-Directed Vocalizations Indicate a Readiness to Learn

Science.gov (United States)

Goldstein, Michael H.; Schwade, Jennifer; Briesch, Jacquelyn; Syal, Supriya

2010-01-01

Two studies illustrate the functional significance of a new category of prelinguistic vocalizing--object-directed vocalizations (ODVs)--and show that these sounds are connected to learning about words and objects. Experiment 1 tested 12-month-old infants' perceptual learning of objects that elicited ODVs. Fourteen infants' vocalizations were…

A Framework for the Flexible Content Packaging of Learning Objects and Learning Designs

Science.gov (United States)

Lukasiak, Jason; Agostinho, Shirley; Burnett, Ian; Drury, Gerrard; Goodes, Jason; Bennett, Sue; Lockyer, Lori; Harper, Barry

2004-01-01

This paper presents a platform-independent method for packaging learning objects and learning designs. The method, entitled a Smart Learning Design Framework, is based on the MPEG-21 standard, and uses IEEE Learning Object Metadata (LOM) to provide bibliographic, technical, and pedagogical descriptors for the retrieval and description of learning…

Localization-Aware Active Learning for Object Detection

OpenAIRE

Kao, Chieh-Chi; Lee, Teng-Yok; Sen, Pradeep; Liu, Ming-Yu

2018-01-01

Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis,...

Mosaic model for sensorimotor learning and control.

Science.gov (United States)

Haruno, M; Wolpert, D M; Kawato, M

2001-10-01

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

On the Concepts of Usability and Reusability of Learning Objects

Directory of Open Access Journals (Sweden)

Miguel-Angel Sicilia

2003-10-01

Full Text Available “Reusable learning objects” oriented towards increasing their potential reusability are required to satisfy concerns about their granularity and their independence of concrete contexts of use. Such requirements also entail that the definition of learning object “usability,” and the techniques required to carry out their “usability evaluation” must be substantially different from those commonly used to characterize and evaluate the usability of conventional educational applications. In this article, a specific characterization of the concept of learning object usability is discussed, which places emphasis on “reusability,” the key property of learning objects residing in repositories. The concept of learning object reusability is described as the possibility and adequacy for the object to be usable in prospective educational settings, so that usability and reusability are considered two interrelated – and in many cases conflicting – properties of learning objects. Following the proposed characterization of two characteristics or properties of learning objects, a method to evaluate usability of specific learning objects will be presented.

Learning with hierarchical-deep models.

Science.gov (United States)

Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Model-observer similarity, error modeling and social learning in rhesus macaques.

Directory of Open Access Journals (Sweden)

Elisabetta Monfardini

Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.

Patterns of Learning Object Reuse in the Connexions Repository

Science.gov (United States)

Duncan, S. M.

2009-01-01

Since the term "learning object" was first published, there has been either an explicit or implicit expectation of reuse. There has also been a lot of speculation about why learning objects are, or are not, reused. This study quantitatively examined the actual amount and type of learning object use, to include reuse, modification, and translation,…

Modellus: Learning Physics with Mathematical Modelling

Science.gov (United States)

Teodoro, Vitor

Computers are now a major tool in research and development in almost all scientific and technological fields. Despite recent developments, this is far from true for learning environments in schools and most undergraduate studies. This thesis proposes a framework for designing curricula where computers, and computer modelling in particular, are a major tool for learning. The framework, based on research on learning science and mathematics and on computer user interface, assumes that: 1) learning is an active process of creating meaning from representations; 2) learning takes place in a community of practice where students learn both from their own effort and from external guidance; 3) learning is a process of becoming familiar with concepts, with links between concepts, and with representations; 4) direct manipulation user interfaces allow students to explore concrete-abstract objects such as those of physics and can be used by students with minimal computer knowledge. Physics is the science of constructing models and explanations about the physical world. And mathematical models are an important type of models that are difficult for many students. These difficulties can be rooted in the fact that most students do not have an environment where they can explore functions, differential equations and iterations as primary objects that model physical phenomena--as objects-to-think-with, reifying the formal objects of physics. The framework proposes that students should be introduced to modelling in a very early stage of learning physics and mathematics, two scientific areas that must be taught in very closely related way, as they were developed since Galileo and Newton until the beginning of our century, before the rise of overspecialisation in science. At an early stage, functions are the main type of objects used to model real phenomena, such as motions. At a later stage, rates of change and equations with rates of change play an important role. This type of equations

Learning Objectives for Master's theses at DTU Management Engineering

DEFF Research Database (Denmark)

Hansen, Claus Thorp; Rasmussen, Birgitte; Hinz, Hector Nøhr

2010-01-01

, different. The DTU Study Handbook states that:”Learning objectives are an integrated part of the supervision”, which provides you with the opportunity – naturally in cooperation with your supervisor – to formulate learning objectives for your Master's thesis. There are at least three good reasons for being...... that you formulate precise and useful learning objectives for your Master's thesis. These notes of inspiration have been written to help you do exactly this. The notes discuss the requirements for the learning objectives, examples of learning objectives and the assessment criteria defined by DTU Management...... Engineering as well as, but not least, some useful things to remember concerning your submission and the assessment of the Master's thesis. DTU Management Engineering Claus Thorp Hansen Birgitte Rasmussen Hector Nøhr Hinz © DTU Management Engineering 2010 ISBN nr. 978-87-90855-94-7 This document...

Active learning in the lecture theatre using 3D printed objects.

Science.gov (United States)

Smith, David P

2016-01-01

The ability to conceptualize 3D shapes is central to understanding biological processes. The concept that the structure of a biological molecule leads to function is a core principle of the biochemical field. Visualisation of biological molecules often involves vocal explanations or the use of two dimensional slides and video presentations. A deeper understanding of these molecules can however be obtained by the handling of objects. 3D printed biological molecules can be used as active learning tools to stimulate engagement in large group lectures. These models can be used to build upon initial core knowledge which can be delivered in either a flipped form or a more didactic manner. Within the teaching session the students are able to learn by handling, rotating and viewing the objects to gain an appreciation, for example, of an enzyme's active site or the difference between the major and minor groove of DNA. Models and other artefacts can be handled in small groups within a lecture theatre and act as a focal point to generate conversation. Through the approach presented here core knowledge is first established and then supplemented with high level problem solving through a "Think-Pair-Share" cooperative learning strategy. The teaching delivery was adjusted based around experiential learning activities by moving the object from mental cognition and into the physical environment. This approach led to students being able to better visualise biological molecules and a positive engagement in the lecture. The use of objects in teaching allows the lecturer to create interactive sessions that both challenge and enable the student.

Object recognition in images via a factor graph model

Science.gov (United States)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

Improving learning of anatomy with reusable learning objects

Directory of Open Access Journals (Sweden)

P Rad

2015-12-01

Full Text Available Introduction: The use of modern educational technologies is useful for learning, durability, sociability, and upgrading professionalism. The aim of this study was evaluating the effect of reusable learning objects on improving learning of anatomy. Methods: This was a quasi-experimental study. Fourteen (reusable learning objects RLO from different parts of anatomy of human body including thorax, abdomen, and pelvis were prepared for medical student in Yasuj University of Medical Sciences in 2009. The length of the time for RLO was between 11-22 min. Because their capacities were low, so they were easy to use with cell phone or MP4. These materials were available to the students before the classes. The mean scores of students in anatomy of human body group were compared to the medical students who were not used this method and entered the university in 2008. A questionnaire was designed by the researcher to evaluate the effect of RLO and on, content, interest and motivation, participation, preparation and attitude. Result: The mean scores of anatomy of human body of medical student who were entered the university in 2009 have been increased compare to the students in 2008, but this difference was not significant. Based on the questionnaire data, it was shown that the RLO had a positive effect on improving learning anatomy of human body (75.5% and the effective relationship (60.6%. The students were interested in using RLO (74.6%, some students (54.2% believed that this method should be replaced by lecture. Conclusion: The use of RLO could promote interests and effective communication among the students and led to increasing self-learning motivation.

Participative Knowledge Production of Learning Objects for E-Books.

Science.gov (United States)

Dodero, Juan Manuel; Aedo, Ignacio; Diaz, Paloma

2002-01-01

Defines a learning object as any digital resource that can be reused to support learning and thus considers electronic books as learning objects. Highlights include knowledge management; participative knowledge production, i.e. authoring electronic books by a distributed group of authors; participative knowledge production architecture; and…

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models.

Science.gov (United States)

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

A Learning Object Approach To Evidence based learning

OpenAIRE

Zabin Visram; Bruce Elson; Patricia Reynolds

2005-01-01

This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has mea...

Analyzing the Quality of Students Interaction in a Distance Learning Object-Oriented Programming Discipline

Science.gov (United States)

Carvalho, Elizabeth Simão

2015-01-01

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open…

Model United Nations and Deep Learning: Theoretical and Professional Learning

Science.gov (United States)

Engel, Susan; Pallas, Josh; Lambert, Sarah

2017-01-01

This article demonstrates that the purposeful subject design, incorporating a Model United Nations (MUN), facilitated deep learning and professional skills attainment in the field of International Relations. Deep learning was promoted in subject design by linking learning objectives to Anderson and Krathwohl's (2001) four levels of knowledge or…

Learning Objects, Repositories, Sharing and Reusability

Science.gov (United States)

Koppi, Tony; Bogle, Lisa; Bogle, Mike

2005-01-01

The online Learning Resource Catalogue (LRC) Project has been part of an international consortium for several years and currently includes 25 institutions worldwide. The LRC Project has evolved for several pragmatic reasons into an academic network whereby members can identify and share reusable learning objects as well as collaborate in a number…

EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

OpenAIRE

Hayati .; Retno Dwi Suyanti

2013-01-01

The objective in this research: (1) Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2) Determine the level of motivation to learn in affects physics student learning outcomes. (3) Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all s...

Robustness and prediction accuracy of machine learning for objective visual quality assessment

OpenAIRE

HINES, ANDREW

2014-01-01

PUBLISHED Lisbon, Portugal Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reli- ability of ML-based techniques within objective quality as- sessment metrics is often questioned. In this study, the ro- bustness of ML in supporting objective quality assessment is investigated, specific...

Developing Learning Objectives for Accounting Ethics Using Bloom's Taxonomy

Science.gov (United States)

Kidwell, Linda A.; Fisher, Dann G.; Braun, Robert L.; Swanson, Diane L.

2013-01-01

The purpose of our article is to offer a set of core knowledge learning objectives for accounting ethics education. Using Bloom's taxonomy of educational objectives, we develop learning objectives in six content areas: codes of ethical conduct, corporate governance, the accounting profession, moral development, classical ethics theories, and…

The development of a National set of Physiology learning objectives ...

African Journals Online (AJOL)

International Journal of Medicine and Health Development ... engagement that can be utilized to design a national set of learning objectives towards improving learning ... Key words: Learning objectives, Nigeria, Medical education, curriculum ...

A Model for Semi-Automatic Composition of Educational Content from Open Repositories of Learning Objects

Directory of Open Access Journals (Sweden)

Paula Andrea Rodríguez Marín

2014-04-01

Full Text Available Learning objects (LOs repositories are important in building educational content and should allow search, retrieval and composition processes to be successfully developed to reach educational goals. However, such processes require so much time-consuming and not always provide the desired results. Thus, the aim of this paper is to propose a model for the semiautomatic composition of LOs, which are automatically recovered from open repositories. For the development of model, various text similarity measures are discussed, while for calibration and validation some comparison experiments were performed using the results obtained by teachers. Experimental results show that when using a value of k (number of LOs selected of at least 3, the percentage of similarities between the model and such made by experts exceeds 75%. To conclude, it can be established that the model proposed allows teachers to save time and effort for LOs selection by performing a pre-filter process.

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

Science.gov (United States)

Grossberg, Stephen

2015-09-24

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

ROBUSTNESS AND PREDICTION ACCURACY OF MACHINE LEARNING FOR OBJECTIVE VISUAL QUALITY ASSESSMENT

OpenAIRE

Hines, Andrew; Kendrick, Paul; Barri, Adriaan; Narwaria, Manish; Redi, Judith A.

2014-01-01

Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptim...

Searching for and Positioning of Contextualized Learning Objects

Science.gov (United States)

Baldiris, Silvia; Graf, Sabine; Fabregat, Ramon; Mendez, Nestor Dario Duque

2012-01-01

Learning object economies are marketplaces for the sharing and reuse of learning objects (LO). There are many motivations for stimulating the development of the LO economy. The main reason is the possibility of providing the right content, at the right time, to the right learner according to adequate quality standards in the context of a lifelong…

Learn Objective-C for Java Developers

CERN Document Server

Bucanek, James

2009-01-01

Learn Objective-C for Java Developers will guide experienced Java developers into the world of Objective-C. It will show them how to take their existing language knowledge and design patterns and transfer that experience to Objective-C and the Cocoa runtime library. This is the express train to productivity for every Java developer who dreamt of developing for Mac OS X or iPhone, but felt that Objective-C was too intimidating. So hop on and enjoy the ride!

Smart learning objects for smart education in computer science theory, methodology and robot-based implementation

CERN Document Server

Stuikys, Vytautas

2015-01-01

This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist a

Elaboration of Statistics Learning Objects for Mobile Devices

Directory of Open Access Journals (Sweden)

Francisco Javier Tapia Moreno

2012-04-01

Full Text Available Mobile learning (m-learning allows a person to study using a mobile computer device anywhere and anytime. In this work we report the elaboration of learning objects for the teaching of introductory statistics using cellular phones.

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Directory of Open Access Journals (Sweden)

Paula A. Rodríguez

2013-03-01

Full Text Available Learning Objects (LOs are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

Bayesian feature weighting for unsupervised learning, with application to object recognition

OpenAIRE

Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

2003-01-01

International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

Science.gov (United States)

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

A Critique of Stephen Downes' "Learning Objects": A Chinese perspective

Directory of Open Access Journals (Sweden)

Fuhua (Oscar Lin

2001-07-01

Full Text Available This paper by Stephen Downes recommends a way of sharing online teaching/ course materials to accelerate course development and make education more cost-effective. His paper is a review of basic information about learning objects (LOs and includes examples that illustrate such technical terms as XML and TML. His paper, however, does not identify several important issues such as: a the level of granularity of learning objects; b selection and integration of learning objects in an appropriate way to form higher level units of study; c training of professors in the use of learning objects; d appropriate use of metadata to facilitate composition of higher level units; and e the potential of computer agents to facilitate the dynamic composition of personalized lessons. An unorganized aggregate of learning objects simply does not constitute a course. In order to create a properly designed final course, student and instructor interaction must be built in.

Evaluation of a Learning Object Based Learning Environment in Different Dimensions

Directory of Open Access Journals (Sweden)

Ünal Çakıroğlu

2009-11-01

Full Text Available Learning Objects (LOs are web based learning resources presented by Learning Object Repositories (LOR. For recent years LOs have begun to take place on web and it is suggested that appropriate design of LOs can make positive impact on learning. In order to support learning, research studies recommends LOs should have been evaluated pedagogically and technologically, and the content design created by using LOs should have been designed through appropriate instructional models. Since the use of LOs have recently begun, an exact pedagogical model about efficient use of LOs has not been developed. In this study a LOR is designed in order to be used in mathematics education. The LOs in this LOR have been evaluated pedagogically and technologically by mathematics teachers and field experts. In order to evaluate the designed LO based environment, two different questionnaires have been used. These questionnaires are developed by using the related literature about web based learning environments evaluation criteria and also the items are discussed with the field experts for providing the validity. The reliability of the questionnaires is calculated cronbach alpha = 0.715 for the design properties evaluation survey and cronbach alpha =0.726 for pedagogic evaluation. Both of two questionnaires are five point Likert type. The first questionnaire has the items about “Learning Support of LOs, Competency of LOR, The importance of LOs in mathematics education, the usability of LOs by students”. “The activities on LOs are related to outcomes of subjects, there are activities for students have different learning styles. There are activities for wondering students.” are examples for items about learning support of LOs. “System helps for exploration of mathematical relations”, “I think teaching mathematics with this system will be enjoyable.” are example items for importance of LOs in mathematics education. In the competency of LOR title, �

Exploring emerging learning needs: a UK-wide consultation on environmental sustainability learning objectives for medical education.

Science.gov (United States)

Walpole, Sarah C; Mortimer, Frances; Inman, Alice; Braithwaite, Isobel; Thompson, Trevor

2015-12-24

This study aimed to engage wide-ranging stakeholders and develop consensus learning objectives for undergraduate and postgraduate medical education. A UK-wide consultation garnered opinions of healthcare students, healthcare educators and other key stakeholders about environmental sustainability in medical education. The policy Delphi approach informed this study. Draft learning objectives were revised iteratively during three rounds of consultation: online questionnaire or telephone interview, face-to-face seminar and email consultation. Twelve draft learning objectives were developed based on review of relevant literature. In round one, 64 participants' median ratings of the learning objectives were 3.5 for relevance and 3.0 for feasibility on a Likert scale of one to four. Revisions were proposed, e.g. to highlight relevance to public health and professionalism. Thirty three participants attended round two. Conflicting opinions were explored. Added content areas included health benefits of sustainable behaviours. To enhance usability, restructuring provided three overarching learning objectives, each with subsidiary points. All participants from rounds one and two were contacted in round three, and no further edits were required. This is the first attempt to define consensus learning objectives for medical students about environmental sustainability. Allowing a wide range of stakeholders to comment on multiple iterations of the document stimulated their engagement with the issues raised and ownership of the resulting learning objectives.

Predictable Locations Aid Early Object Name Learning

Science.gov (United States)

Benitez, Viridiana L.; Smith, Linda B.

2012-01-01

Expectancy-based localized attention has been shown to promote the formation and retrieval of multisensory memories in adults. Three experiments show that these processes also characterize attention and learning in 16- to 18-month old infants and, moreover, that these processes may play a critical role in supporting early object name learning. The…

Reinforcement active learning in the vibrissae system: optimal object localization.

Science.gov (United States)

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Learning in Organizations - an Object Relations Perspective

DEFF Research Database (Denmark)

Andersen, Anders Siig

Learning in organizations – an object relations perspective As a researcher with a primary interest in the study of learning environments in organizations I have conducted a number of empirical research projects primarily concerning work places in the state sector. The aim of the research has been...... of organizations as learning environments for the employees. Theoretically I draw on object relations theory. Within this tradition the theoretical point of departure is twofold: the study of work conditions in hospitals carried out by Menzies (1975) and Hinschelwood & Skogstad (2000). With regard to the first...... positive and negative impact do they have with respect to the staff itself? With regard to Hinschelwood & Skogstad (2000) they are introduced to further develop and contrast Menzies’ theoretical ideas. Instead of only emphasizing the connection between the work organization and the defence techniques...

Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

2017-01-01

of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show...

Soft object deformation monitoring and learning for model-based robotic hand manipulation.

Science.gov (United States)

Cretu, Ana-Maria; Payeur, Pierre; Petriu, Emil M

2012-06-01

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.

Dew Point modelling using GEP based multi objective optimization

OpenAIRE

Shroff, Siddharth; Dabhi, Vipul

2013-01-01

Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. We aim to use Gene Expression Programming for modelling of dew point. Generally, accuracy of the model is the only objective used by selection mechanism of GEP. This will evolve...

Deep Learning for Detection of Object-Based Forgery in Advanced Video

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Ye Yao

2017-12-01

Full Text Available Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

Speckle-learning-based object recognition through scattering media.

Science.gov (United States)

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

LEARNING CREATIVE WRITING MODEL BASED ON NEUROLINGUISTIC PROGRAMMING

OpenAIRE

Rustan, Edhy

2017-01-01

The objectives of the study are to determine: (1) condition on learning creative writing at high school students in Makassar, (2) requirement of learning model in creative writing, (3) program planning and design model in ideal creative writing, (4) feasibility of model study based on creative writing in neurolinguistic programming, and (5) the effectiveness of the learning model based on creative writing in neurolinguisticprogramming.The method of this research uses research development of L...

A critique of Stephen Downes' article, "Learning Objects" -- A perspective from Bahrain

Directory of Open Access Journals (Sweden)

Muain H. Jamlan

2001-07-01

Full Text Available With the availability of technology, hardware, and software, learning objects become fundamental to the learning process and change the way in which learning materials are designed. The vast development of technology forces both teacher and learner to modify their roles. Teachers become facilitators, while learners became active and responsible for selecting modes and styles of learning. Assuming this attitude of implementing technology in the learning process and seeking new methods of facilitating learning, universities and colleges have to adopt new techniques. One of these new techniques is the use of learning objects. Although learning objects are considered products of technology developed in the USA, Japan, and European countries, universities in the Middle East have been influenced by this development. While there are differences in the quantity and quality of these technologies available in many Middle East countries, computer applications, especially those that deploy the Internet, have now become available. Educational authorities in Middle East countries are now turning to the availability of learning objects. Let me clarify some of the issues Downes discusses in his article on learning objects, Vol. 2, No. 1 of the International Review of Research in Open and Distance Learning.

Design of Learning Objects for Concept Learning: Effects of Multimedia Learning Principles and an Instructional Approach

Science.gov (United States)

Chiu, Thomas K. F.; Churchill, Daniel

2016-01-01

Literature suggests using multimedia learning principles in the design of instructional material. However, these principles may not be sufficient for the design of learning objects for concept learning in mathematics. This paper reports on an experimental study that investigated the effects of an instructional approach, which includes two teaching…

PARTICULARITIES OF EDUCATIONAL OBJECTS IN COMPUTER-ASSISTED LEARNING FOR PERSONS WITH DISABILITIES

Directory of Open Access Journals (Sweden)

Narcisa ISĂILĂ

2010-12-01

Full Text Available The current trend in computer-assisted learning is the creation of reusable learning objects. They can be used independently or can be coupled to make lessons that best fit the users' learning needs. From this perspective, the specific of learning objects for people with disabilities is to ensure accessibility and usability. Using standards in the process of creating learning objects provide flexibility in achieving lessons, thus being helpful for educational content creators (teachers. Metadata have an essential role in achieving interoperability and provide standardized information about the learning objects, allowing the searching, accessing and their finding. The compliance of eLearning standards ensures the compatibility and portability of materials from one system to another, which reduces the time and cost of development.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

Directory of Open Access Journals (Sweden)

Jui-Yuan Su

2015-04-01

Full Text Available In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

Learning Object Names at Different Hierarchical Levels Using Cross-Situational Statistics.

Science.gov (United States)

Chen, Chi-Hsin; Zhang, Yayun; Yu, Chen

2018-05-01

Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co-occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input. Copyright © 2017 Cognitive Science Society, Inc.

DROpS: an object of learning in computer simulation of discrete events

Directory of Open Access Journals (Sweden)

Hugo Alves Silva Ribeiro

2015-09-01

Full Text Available This work presents the “Realistic Dynamics Of Simulated Operations” (DROpS, the name given to the dynamics using the “dropper” device as an object of teaching and learning. The objective is to present alternatives for professors teaching content related to simulation of discrete events to graduate students in production engineering. The aim is to enable students to develop skills related to data collection, modeling, statistical analysis, and interpretation of results. This dynamic has been developed and applied to the students by placing them in a situation analogous to a real industry, where various concepts related to computer simulation were discussed, allowing the students to put these concepts into practice in an interactive manner, thus facilitating learning

Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Guide to good practices for developing learning objectives. DOE Handbook

Energy Technology Data Exchange (ETDEWEB)

NONE

1992-07-01

This guide to good practices provides information and guidance on the types of and development of learning objectives in a systematic approach to training program. This document can serve as a reference during the development of new learning objectives or refinement of existing ones.

Learning and Control Model of the Arm for Loading

Science.gov (United States)

Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

Digital learning objects in nursing consultation: technology assessment by undergraduate students.

Science.gov (United States)

Silveira, DeniseTolfo; Catalan, Vanessa Menezes; Neutzling, Agnes Ludwig; Martinato, Luísa Helena Machado

2010-01-01

This study followed the teaching-learning process about the nursing consultation, based on digital learning objects developed through the active Problem Based Learning method. The goals were to evaluate the digital learning objects about nursing consultation, develop cognitive skills on the subject using problem based learning and identify the students' opinions on the use of technology. This is an exploratory and descriptive study with a quantitative approach. The sample consisted of 71 students in the sixth period of the nursing program at the Federal University of Rio Grande do Sul. The data was collected through a questionnaire to evaluate the learning objects. The results showed positive agreement (58%) on the content, usability and didactics of the proposed computer-mediated activity regarding the nursing consultation. The application of materials to the students is considered positive.

Learning objects as coadjuvants in the human physiology teaching-learning process

Directory of Open Access Journals (Sweden)

Marcus Vinícius Lara

2014-08-01

Full Text Available The use of Information and Communication Technologies (ICTs in the academic environment of biomedical area has gained much importance, both for their ability to complement the understanding of the subject obtained in the classroom, is the ease of access, or makes more pleasure the learning process, since these tools are present in everyday of the students and use a simple language. Considering that, this study aims to report the experience of building learning objects in human physiology as a tool for learning facilitation, and discuss the impact of this teaching methodology

Single and multiple object tracking using log-euclidean Riemannian subspace and block-division appearance model.

Science.gov (United States)

Hu, Weiming; Li, Xi; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen; Zhang, Zhongfei

2012-12-01

Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.

Form over Substance: Learning Objectives in the Business Core

Science.gov (United States)

Stokes, Leonard; Rosetti, Joseph L.; King, Michelle

2010-01-01

While members of the business faculty community have been advocating active learning in the classroom, it appears that textbooks encourage learning from a passive perspective. A review of learning objectives from 16 textbooks used in Financial Accounting, Managerial Accounting, Finance, and Marketing demonstrates a focus on basically the same set…

Object based implicit contextual learning: a study of eye movements.

Science.gov (United States)

van Asselen, Marieke; Sampaio, Joana; Pina, Ana; Castelo-Branco, Miguel

2011-02-01

Implicit contextual cueing refers to a top-down mechanism in which visual search is facilitated by learned contextual features. In the current study we aimed to investigate the mechanism underlying implicit contextual learning using object information as a contextual cue. Therefore, we measured eye movements during an object-based contextual cueing task. We demonstrated that visual search is facilitated by repeated object information and that this reduction in response times is associated with shorter fixation durations. This indicates that by memorizing associations between objects in our environment we can recognize objects faster, thereby facilitating visual search.

Blended learning models for directing the self-learning activity of “Software Engineering” specialty students

Directory of Open Access Journals (Sweden)

Vera V. Lyubchenko

2014-12-01

Full Text Available The adoption of Law of Ukraine “On Higher Education” (2014 involves the increase in students’ self-learning activity part in the curriculum. Therefore the self-learning activities’ arrangement in a way augmenting the result quality becomes a top priority task. This research objective consists in elaborating the scenario for organization of the students’ qualitative self-study, based on blended learning models. The author analyzes four blended learning models: the rotation model, flex-model, self-blend model and online driver model, and gives examples of their use. It is shown that first two models are the most suitable for full-time students. A general scenario for the use of blended learning models is described. Although the use of blended learning models causes several difficulties, it also essentially contributes into students’ self-study monitoring and control support.

An Analysis on Usage Preferences of Learning Objects and Learning Object Repositories among Pre-Service Teachers

Science.gov (United States)

Yeni, Sabiha; Ozdener, Nesrin

2014-01-01

The purpose of the study is to investigate how pre-service teachers benefit from learning objects repositories while preparing course content. Qualitative and quantitative data collection methods were used in a mixed methods approach. This study was carried out with 74 teachers from the Faculty of Education. In the first phase of the study,…

ICT Competence-Based Learning Object Recommendations for Teachers

Science.gov (United States)

Sergis, Stylianos; Zervas, Panagiotis; Sampson, Demetrios G.

2014-01-01

Recommender Systems (RS) have been applied in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Object (LO) selection and retrieval. Most of the existing approaches, however, aim at accommodating the needs of learners and teacher-oriented RS are still an under-investigated field. Moreover, the systems that focus…

Technology and human issues in reusing learning objects

NARCIS (Netherlands)

Collis, Betty; Strijker, A.

2004-01-01

Reusing learning objects is as old as retelling a story or making use of libraries and textbooks, and in electronic form has received an enormous new impetus because of the World Wide Web and Web technologies. Are we at the brink of changing the "shape and form of learning, ... of being able to

e-Learning objects in the cloud: SCORM compliance, creation and deployment options

Directory of Open Access Journals (Sweden)

Stephanie Day

2017-12-01

Full Text Available In the field of education, cloud computing is changing the way learning is delivered and experienced, by providing software, storage, teaching resources, artefacts, and knowledge that can be shared by educators on a global scale. In this paper, the first objective is to understand the general trends in educational use of the cloud, particularly the provision of large scale education opportunities, use of open and free services, and interoperability of learning objects. A review of current literature reveals the opportunities and issues around managing learning and teaching related knowledge in the cloud. The educational use of the cloud will continue to grow as the services, pedagogies, personalization, and standardization of learning are refined and adopted. Secondly, the paper presents an example of how the cloud can support learning opportunities using SCORM interoperable learning objects. The case study findings show that, while the use of SCORM enables a variety of trackable learning opportunities, the constraints of maintaining the currency of the learning also need to be considered. It is recommended that the SCORM content are combined with cloud based student activities. These learning objects can be used to support alternative learning opportunities within blended and online learning environments.

The company objects keep: Linking referents together during cross-situational word learning.

Science.gov (United States)

Zettersten, Martin; Wojcik, Erica; Benitez, Viridiana L; Saffran, Jenny

2018-04-01

Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

Simplified production of multimedia based radiological learning objects using the flash format

International Nuclear Information System (INIS)

Jedrusik, P.; Preisack, M.; Dammann, F.

2005-01-01

Purpose: evaluation of the applicability of the flash format for the production of radiological learning objects used in an e-learning environment. Material and methods: five exemplary learning objects with different didactic purposes referring to radiological diagnostics are presented. They have been intended for the use within the multimedia, internet-based e-learning environment LaMedica. Interactive learning objects were composed using the Flash 5.0 software (Macromedia, San Francisco, USA) on the basis of digital CT and MR images, digitized conventional radiographs and different graphical elements prepared as TIFF files or in a vector graphics format. Results: after a short phase of initial skill adaptation training, a radiologist author was soon able to create independently all learning objects. The import of different types of images and graphical elements was carried out without complications. Despite manifold design options, handling of the program is easy due to clear arrangement and structure, thus enabling the creation of simple as well as complex learning objects that provided a high degree of attractiveness and interaction. Data volume and bandwidth demand for online use was significantly reduced by the flash format compression without a substantial loss of visual quality. (orig.)

Guide to good practices for developing learning objectives. DOE guideline

Energy Technology Data Exchange (ETDEWEB)

1992-07-01

This guide to good practices provides information and guidance on the types of, and the development of learning objectives in performance-based training system at reactor and nonreactor nuclear facilities. Contractors are encouraged to consider this guidance as a reference when developing new learning objectives or refining existing ones. Training managers, designers, developers, and instructors are the intended audiences.

Web based Interactive 3D Learning Objects for Learning Management Systems

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Stefan Hesse

2012-02-01

Full Text Available In this paper, we present an approach to create and integrate interactive 3D learning objects of high quality for higher education into a learning management system. The use of these resources allows to visualize topics, such as electro-technical and physical processes in the interior of complex devices. This paper addresses the challenge of combining rich interactivity and adequate realism with 3D exercise material for distance elearning.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

Science.gov (United States)

Rolls, Edmund T

2012-01-01

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Science.gov (United States)

Hauffen, Karin; Bart, Eugene; Brady, Mark; Kersten, Daniel; Hegdé, Jay

2012-01-01

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2. Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created

Learning object for teacher training aimed to develop communication skills

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Norma Esmeralda RODRÍGUEZ RAMÍREZ

2014-06-01

Full Text Available This article presents the results and reflections obtained across a research aimed to analyze the quality criteria of an opened learning object oriented to develop communication skills in order to be able to report and validate it according to its content, pedagogic structure, technological structure, graphical and textual language and usability to teacher training, in order to base it theoretically, pedagogically and technologically. The research question was: Which are the quality criteria that a learning object aimed to develop communication skills must cover? Under a quantitative approach, there were electronic questionnaires applied to: 34 Technological University teachers, eight experts about of communicative competence, teaching, technology and graphic design. The results indicated that some of the quality criteria of learning object are: the effective managing of the learning content, the balanced composition of his pedagogic structure, the technological structure efficiency and the proper managing of graphical and textual language.

Individualized Learning Through Non-Linear use of Learning Objects: With Examples From Math and Stat

DEFF Research Database (Denmark)

Rootzén, Helle

2015-01-01

Our aim is to ensure individualized learning that is fun, inspiring and innovative. We believe that when you enjoy, your brain will open up and learning will be easier and more effective. The methods use a non-linear learning environment based on self-contained learning objects which are pieced t...

EDUCATEE'S THESAURUS AS AN OBJECT OF MEASURING LEARNED MATERIAL OF THE DISTANCE LEARNING COURSE

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Alexander Aleksandrovich RYBANOV

2013-10-01

Full Text Available Monitoring and control over the process of studying the distance learning course are based on solving the problem of making out an adequate integral mark to the educatee for mastering entire study course, by testing results. It is suggested to use the degree of correspondence between educatee's thesaurus and the study course thesaurus as an integral mark for the degree of mastering the distance learning course. Study course thesaurus is a set of the course objects with relations between them specified. The article considers metrics of the study course thesaurus complexity, made on the basis of the graph theory and the information theory. It is suggested to use the amount of information contained in the study course thesaurus graph as the metrics of the study course thesaurus complexity. Educatee's thesaurus is considered as an object of measuring educational material learned at the semantic level and is assessed on the basis of amount of information contained in its graph, taking into account the factors of learning the thesaurus objects.

Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning

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Jay eHegdé

2012-06-01

Full Text Available Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects’ recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus reliably reflected, on a trial-to-trial basis, subjects’ object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned.

Object learning improves feature extraction but does not improve feature selection.

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Linus Holm

Full Text Available A single glance at your crowded desk is enough to locate your favorite cup. But finding an unfamiliar object requires more effort. This superiority in recognition performance for learned objects has at least two possible sources. For familiar objects observers might: 1 select more informative image locations upon which to fixate their eyes, or 2 extract more information from a given eye fixation. To test these possibilities, we had observers localize fragmented objects embedded in dense displays of random contour fragments. Eight participants searched for objects in 600 images while their eye movements were recorded in three daily sessions. Performance improved as subjects trained with the objects: The number of fixations required to find an object decreased by 64% across the 3 sessions. An ideal observer model that included measures of fragment confusability was used to calculate the information available from a single fixation. Comparing human performance to the model suggested that across sessions information extraction at each eye fixation increased markedly, by an amount roughly equal to the extra information that would be extracted following a 100% increase in functional field of view. Selection of fixation locations, on the other hand, did not improve with practice.

Bio-Inspired Neural Model for Learning Dynamic Models

Science.gov (United States)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Invariant visual object and face recognition: neural and computational bases, and a model, VisNet

Directory of Open Access Journals (Sweden)

Edmund T eRolls

2012-06-01

Full Text Available Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Learning Mathematics by Designing, Programming, and Investigating with Interactive, Dynamic Computer-Based Objects

Science.gov (United States)

Marshall, Neil; Buteau, Chantal

2014-01-01

As part of their undergraduate mathematics curriculum, students at Brock University learn to create and use computer-based tools with dynamic, visual interfaces, called Exploratory Objects, developed for the purpose of conducting pure or applied mathematical investigations. A student's Development Process Model of creating and using an Exploratory…

Digital learning object for diagnostic reasoning in nursing applied to the integumentary system

Directory of Open Access Journals (Sweden)

Cecília Passos Vaz da Costa

Full Text Available Objective: To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. Method: A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. Results: The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. Conclusion: This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

Learning Objects and Grasp Affordances through Autonomous Exploration

DEFF Research Database (Denmark)

Kraft, Dirk; Detry, Renaud; Pugeault, Nicolas

2009-01-01

We describe a system for autonomous learning of visual object representations and their grasp affordances on a robot-vision system. It segments objects by grasping and moving 3D scene features, and creates probabilistic visual representations for object detection, recognition and pose estimation...... image sequences as well as (3) a number of built-in behavioral modules on the one hand, and autonomous exploration on the other hand, the system is able to generate object and grasping knowledge through interaction with its environment....

Learning from Objects: A Future for 21st Century Urban Arts Education

Science.gov (United States)

Lasky, Dorothea

2009-01-01

In this technological age, where mind and body are increasingly disconnected in the classroom, object-based learning--along with strong museum-school partnerships--provide many benefits for student learning. In this article, the author first outlines some of the special mind-body connections that object-based learning in museums affords learners…

Development and assessment of learning objects about intramuscular medication administration

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Lilian Mayumi Chinen Tamashiro

2014-10-01

Full Text Available OBJECTIVES: to develop and assess a learning object about intramuscular medication administration for nursing undergraduates and nurses.METHOD: a random, intentional and non-probabilistic sample was selected of nurses from a Brazilian social network of nursing and students from the Undergraduate Program at the University of São Paulo School of Nursing to serve as research subjects and assess the object.RESULTS: the participants, 8 nurses and 8 students, studied the object and answered an assessment instrument that included the following criteria: educational aspects (relevance of the theme, objectives and texts/hypertexts, interface of the environment (navigation, accessibility and screen design and didactic resources (interactivity and presentation of resources. In total, 128 significant answers were obtained, 124 (97% of which were positive, assessed as excellent and satisfactory, considered as a flexible, dynamic, objective resources that is appropriate to the nursing learning process.CONCLUSION: the educational technology shows a clear and easily understandable language and the teaching method could be applied in other themes, contributing to the education and training of nursing professionals, positively affecting nursing teaching, stimulating the knowledge, autonomous and independent learning, aligned with the new professional education requirements.

Definition of a Learning Object from Perspectives of In-Service Teachers (Case of Duzce Province

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Kürşat ARSLAN

2016-08-01

Full Text Available Learning objects, as a relatively new technological concept, have drawn much attention from educators because these dijital resources are easily accessible, relatively easy to use due to their limited size and focus, interactive, and adaptable to many different educational contexts. Despite the fact that learning objects have the great potential to improve teaching and learning experiences by providing teachers reusable learning materials and reducing costs, the lack of a “working and clear” definition of these materials has restricted their effective and efficient use. This study aimed to explore elementary school teacher perceptions of their use of learning objects from a qualitative research paradigm in order to reveal the extent to which teachers understand concept of learning object and its instruction approach. The method of the study was based on descriptive phenomenology. Data were collected using multiple methods, including the semi-structured interview, field observation reports, and photos from nine in-service elementary school teachers from different departments in Duzce, Turkey. Methods of data analysis were based on Giorgi’s method of descriptive phenomenology including four stages of content analysis: data coding, developing themes, organizing code and themes, describing findings. Overall findings of the study indicate that teachers use learning objects in their lesson activities without explicit recognition; however they generally fail to understand the exact meaning of a learning object approach and its applications in the classroom. Participants understood different properties of learning objects. Almost all participants perceive objectivity as the most important characteristic of the learning object.  In addition, a majority of the teachers recognized the value of a learning object’s reusability. In-service teachers’ vague perceptions of the definition and usage of learning objects indicated that they used these

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Science.gov (United States)

Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

2014-01-01

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

Science.gov (United States)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

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Hayati .

2013-06-01

Full Text Available The objective in this research: (1 Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2 Determine the level of motivation to learn in affects physics student learning outcomes. (3 Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all students in class XI SMA Negeri 1 T.P Sunggal Semester I 2012/2013. The sample of this research was consisted of two classes with a sample of 70 peoples who are determined by purposive sampling, the IPA XI-2 as a class experiment using a model-based multimedia learning Training Inquiry as many as 35 peoples and XI IPA-3 as a control class using learning model Inquiry Training 35 peoples. Hypotheses were analyzed using the GLM at significant level of 0.05 using SPSS 17.0 for Windows. Based on data analysis and hypothesis testing conducted found that: (1 Training Inquiry-based multimedia learning model in improving student learning outcomes rather than learning model physics Inquiry Training. (2 The results of studying physics students who have high motivation to learn better than students who have a low learning motivation. (3 From this research there was an interaction between learning model inquiry-based multimedia training and motivation to study on learning outcomes of students.

Holistic Approach to Learning and Teaching Introductory Object-Oriented Programming

Science.gov (United States)

Thota, Neena; Whitfield, Richard

2010-01-01

This article describes a holistic approach to designing an introductory, object-oriented programming course. The design is grounded in constructivism and pedagogy of phenomenography. We use constructive alignment as the framework to align assessments, learning, and teaching with planned learning outcomes. We plan learning and teaching activities,…

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

Learn Objective-C on the Mac for OS X and iOS

CERN Document Server

Knaster, Scott; Malik, Waqar

2012-01-01

Learn to write apps for some of today's hottest technologies, including the iPhone and iPad (using iOS), as well as the Mac (using OS X). It starts with Objective-C, the base language on which the native iOS software development kit (SDK) and the OS X are based. Learn Objective-C on the Mac: For OS X and iOS, Second Edition updates a best selling book and is an extensive, newly updated guide to Objective-C. Objective-C is a powerful, object-oriented extension of C, making this update the perfect follow-up to Dave Mark's bestselling Learn C on the Mac. Whether you're an experienced C programmer

Virtual learning object and environment: a concept analysis.

Science.gov (United States)

Salvador, Pétala Tuani Candido de Oliveira; Bezerril, Manacés Dos Santos; Mariz, Camila Maria Santos; Fernandes, Maria Isabel Domingues; Martins, José Carlos Amado; Santos, Viviane Euzébia Pereira

2017-01-01

To analyze the concept of virtual learning object and environment according to Rodgers' evolutionary perspective. Descriptive study with a mixed approach, based on the stages proposed by Rodgers in his concept analysis method. Data collection occurred in August 2015 with the search of dissertations and theses in the Bank of Theses of the Coordination for the Improvement of Higher Education Personnel. Quantitative data were analyzed based on simple descriptive statistics and the concepts through lexicographic analysis with support of the IRAMUTEQ software. The sample was made up of 161 studies. The concept of "virtual learning environment" was presented in 99 (61.5%) studies, whereas the concept of "virtual learning object" was presented in only 15 (9.3%) studies. A virtual learning environment includes several and different types of virtual learning objects in a common pedagogical context. Analisar o conceito de objeto e de ambiente virtual de aprendizagem na perspectiva evolucionária de Rodgers. Estudo descritivo, de abordagem mista, realizado a partir das etapas propostas por Rodgers em seu modelo de análise conceitual. A coleta de dados ocorreu em agosto de 2015 com a busca de dissertações e teses no Banco de Teses e Dissertações da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. Os dados quantitativos foram analisados a partir de estatística descritiva simples e os conceitos pela análise lexicográfica com suporte do IRAMUTEQ. A amostra é constituída de 161 estudos. O conceito de "ambiente virtual de aprendizagem" foi apresentado em 99 (61,5%) estudos, enquanto o de "objeto virtual de aprendizagem" em apenas 15 (9,3%). Concluiu-se que um ambiente virtual de aprendizagem reúne vários e diferentes tipos de objetos virtuais de aprendizagem em um contexto pedagógico comum.

An Assistant for Loading Learning Object Metadata: An Ontology Based Approach

Science.gov (United States)

Casali, Ana; Deco, Claudia; Romano, Agustín; Tomé, Guillermo

2013-01-01

In the last years, the development of different Repositories of Learning Objects has been increased. Users can retrieve these resources for reuse and personalization through searches in web repositories. The importance of high quality metadata is key for a successful retrieval. Learning Objects are described with metadata usually in the standard…

[Digital learning object for diagnostic reasoning in nursing applied to the integumentary system].

Science.gov (United States)

da Costa, Cecília Passos Vaz; Luz, Maria Helena Barros Araújo

2015-12-01

To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

Using Epistemic Network Analysis to understand core topics as planned learning objectives

DEFF Research Database (Denmark)

Allsopp, Benjamin Brink; Dreyøe, Jonas; Misfeldt, Morten

Epistemic Network Analysis is a tool developed by the epistemic games group at the University of Wisconsin Madison for tracking the relations between concepts in students discourse (Shaffer 2017). In our current work we are applying this tool to learning objectives in teachers digital preparation....... The danish mathematics curriculum is organised in six competencies and three topics. In the recently implemented learning platforms teacher choose which of the mathematical competencies that serves as objective for a specific lesson or teaching sequence. Hence learning objectives for lessons and teaching...... sequences are defining a network of competencies, where two competencies are closely related of they often are part of the same learning objective or teaching sequence. We are currently using Epistemic Network Analysis to study these networks. In the poster we will include examples of different networks...

Comparing L2 Word Learning through a Tablet or Real Objects: What Benefits Learning Most?

NARCIS (Netherlands)

Vlaar, M.A.J.; Verhagen, J.; Oudgenoeg-Paz, O.; Leseman, P.P.M.

2017-01-01

In child-robot interactions focused on language learning, tablets are often used to structure the interaction between the robot and the child. However, it is not clear how tablets affect children’s learning gains. Real-life objects are thought to benefit children’s word learning, but it is not clear

Creating Usage Context-Based Object Similarities to Boost Recommender Systems in Technology Enhanced Learning

Science.gov (United States)

Niemann, Katja; Wolpers, Martin

2015-01-01

In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach relies on the usage-based relations between the objects themselves rather then on the content of the learning objects or on the relations between users and learning objects. We then take this new…

Teaching and Assessing Ethics as a Learning Objective: One School's Journey

Science.gov (United States)

Templin, Carl R.; Christensen, David

2009-01-01

This paper reports the results of a ten-year effort to establish ethics as a learning objective for all business students, to assess the effectiveness in achieving that learning objective and to incorporate ethical conduct as a part of the school's organizational culture. First, it addresses the importance of ethics instruction for all business…

The Development of the Virtual Learning Media of the Sacred Object Artwork

Science.gov (United States)

Nuanmeesri, Sumitra; Jamornmongkolpilai, Saran

2018-01-01

This research aimed to develop the virtual learning media of the sacred object artwork by applying the concept of the virtual technology in order to publicize knowledge on the cultural wisdom of the sacred object artwork. It was done by designing and developing the virtual learning media of the sacred object artwork for the virtual presentation.…

Active learning in the lecture theatre using 3D printed objects [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

David P. Smith

2016-06-01

Full Text Available The ability to conceptualize 3D shapes is central to understanding biological processes. The concept that the structure of a biological molecule leads to function is a core principle of the biochemical field. Visualisation of biological molecules often involves vocal explanations or the use of two dimensional slides and video presentations. A deeper understanding of these molecules can however be obtained by the handling of objects. 3D printed biological molecules can be used as active learning tools to stimulate engagement in large group lectures. These models can be used to build upon initial core knowledge which can be delivered in either a flipped form or a more didactic manner. Within the teaching session the students are able to learn by handling, rotating and viewing the objects to gain an appreciation, for example, of an enzyme’s active site or the difference between the major and minor groove of DNA. Models and other artefacts can be handled in small groups within a lecture theatre and act as a focal point to generate conversation. Through the approach presented here core knowledge is first established and then supplemented with high level problem solving through a "Think-Pair-Share" cooperative learning strategy. The teaching delivery was adjusted based around experiential learning activities by moving the object from mental cognition and into the physical environment. This approach led to students being able to better visualise biological molecules and a positive engagement in the lecture. The use of objects in teaching allows the lecturer to create interactive sessions that both challenge and enable the student.

Advanced technology for the reuse of learning objects in a course-management system

NARCIS (Netherlands)

Strijker, A.; Collis, Betty

2005-01-01

The creation, labelling, use, and re-use of learning objects is an important area of development involving learning technology. In the higher education context, instructors typically use a course management system (CMS) to organize and manage their own learning objects. The needs and practices of

Attribute conjunctions and the part configuration advantage in object category learning.

Science.gov (United States)

Saiki, J; Hummel, J E

1996-07-01

Five experiments demonstrated that in object category learning people are particularly sensitive to conjunctions of part shapes and relative locations. Participants learned categories defined by a part's shape and color (part-color conjunctions) or by a part's shape and its location relative to another part (part-location conjunctions). The statistical properties of the categories were identical across these conditions, as were the salience of color and relative location. Participants were better at classifying objects defined by part-location conjunctions than objects defined by part-color conjunctions. Subsequent experiments revealed that this effect was not due to the specific color manipulation or the role of location per se. These results suggest that the shape bias in object categorization is at least partly due to sensitivity to part-location conjunctions and suggest a new processing constraint on category learning.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Directory of Open Access Journals (Sweden)

Ming Xue

2014-02-01

Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

An OWL Ontology for Metadata of Interactive Learning Objects

Science.gov (United States)

Luz, Bruno N.; Santos, Rafael; Alves, Bruno; Areão, Andreza S.; Yokoyama, Marcos H.; Guimarães, Marcelo P.

2015-01-01

The main purpose of this paper is to present the importance of Interactive Learning Objects (ILO) to improve the teaching-learning process by assuring a constant interaction among teachers and students, which in turn, allows students to be constantly supported by the teacher. The paper describes the ontology that defines the ILO available on the…

Things to Say: Future Applications of Smart Objects in Learning

Science.gov (United States)

Preis, Kevin

2008-01-01

Smart object technology allows users to know something in real time about the physical objects in their presence. Each object, from cereal boxes to skyscrapers, becomes a source of information with which users can interact. Through a series of usage scenarios, the article explores the potential impact of smart objects on learning in formal and…

Effect of tDCS on task relevant and irrelevant perceptual learning of complex objects.

Science.gov (United States)

Van Meel, Chayenne; Daniels, Nicky; de Beeck, Hans Op; Baeck, Annelies

2016-01-01

During perceptual learning the visual representations in the brain are altered, but these changes' causal role has not yet been fully characterized. We used transcranial direct current stimulation (tDCS) to investigate the role of higher visual regions in lateral occipital cortex (LO) in perceptual learning with complex objects. We also investigated whether object learning is dependent on the relevance of the objects for the learning task. Participants were trained in two tasks: object recognition using a backward masking paradigm and an orientation judgment task. During both tasks, an object with a red line on top of it were presented in each trial. The crucial difference between both tasks was the relevance of the object: the object was relevant for the object recognition task, but not for the orientation judgment task. During training, half of the participants received anodal tDCS stimulation targeted at the lateral occipital cortex (LO). Afterwards, participants were tested on how well they recognized the trained objects, the irrelevant objects presented during the orientation judgment task and a set of completely new objects. Participants stimulated with tDCS during training showed larger improvements of performance compared to participants in the sham condition. No learning effect was found for the objects presented during the orientation judgment task. To conclude, this study suggests a causal role of LO in relevant object learning, but given the rather low spatial resolution of tDCS, more research on the specificity of this effect is needed. Further, mere exposure is not sufficient to train object recognition in our paradigm.

Group-Based Active Learning of Classification Models.

Science.gov (United States)

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

INST7150 - Advanced Topics in Learning Object Design and Reuse, Fall 2005

OpenAIRE

Wiley, David

2005-01-01

This course is designed to help you understand and apply advanced topics in the design, creation, and reuse of learning objects. The course is structured around a practical, hands-on project using learning objects, intermingled with readings and discussion on a variety of topics.

OBJECTIVES AND PROCESSES OF SECOND LANGUAGE LEARNING.

Science.gov (United States)

SIZEMORE, MAMIE

THE OBJECTIVES OF SECOND LANGUAGE TEACHING, AND SPECIFIC DIRECTIONS FOR PRESENTING AND DRILLING STRUCTURES BY THE USE OF CERTAIN GESTURES, WERE PRESENTED. RECOMMENDATIONS FOR CONCENTRATING EFFORTS ON THE ESSENTIALS OF LANGUAGE LEARNING REVOLVED AROUND AN EMPHASIS ON THE TEACHING OF THE LANGUAGE ITSELF RATHER THAN ABOUT ITS HISTORY, VOCABULARY,…

The perceptual effects of learning object categories that predict perceptual goals

Science.gov (United States)

Van Gulick, Ana E.; Gauthier, Isabel

2014-01-01

In classic category learning studies, subjects typically learn to assign items to one of two categories, with no further distinction between how items on each side of the category boundary should be treated. In real life, however, we often learn categories that dictate further processing goals, for instance with objects in only one category requiring further individuation. Using methods from category learning and perceptual expertise, we studied the perceptual consequences of experience with objects in tasks that rely on attention to different dimensions in different parts of the space. In two experiments, subjects first learned to categorize complex objects from a single morphspace into two categories based on one morph dimension, and then learned to perform a different task, either naming or a local feature judgment, for each of the two categories. A same-different discrimination test before and after each training measured sensitivity to feature dimensions of the space. After initial categorization, sensitivity increased along the category-diagnostic dimension. After task association, sensitivity increased more for the category that was named, especially along the non-diagnostic dimension. The results demonstrate that local attentional weights, associated with individual exemplars as a function of task requirements, can have lasting effects on perceptual representations. PMID:24820671

A Bootstrapping Model of Frequency and Context Effects in Word Learning.

Science.gov (United States)

Kachergis, George; Yu, Chen; Shiffrin, Richard M

2017-04-01

Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. Copyright © 2016 Cognitive Science Society, Inc.

Conformance Testing, the Elixer within the Chain for Learning Scenarios and Objects

NARCIS (Netherlands)

Nadolski, Rob; O'Neill, Owen; Vegt van der, Wim; Koper, Rob

2006-01-01

The chain for learning scenarios and learning objects includes five iterative links: (i) development, (ii) publication, (iii) making resources searchable and reusable and (iv) facilitating their arrangement (v) towards a runnable unit of learning. The use of e-learning specifications and

Visual object tracking by correlation filters and online learning

Science.gov (United States)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

Experimental Object-Oriented Modelling

DEFF Research Database (Denmark)

Hansen, Klaus Marius

through, e.g., technical prototyping and active user involvement. We introduce and examine “experimental object-oriented modelling” as the intersection of these practices. The contributions of this thesis are expected to be within three perspectives on models and modelling in experimental system...... development: Grounding We develop an empirically based conceptualization of modelling and use of models in system development projects characterized by a high degree of uncertainty in requirements and point to implications for tools and techniques for modelling in such a setting. Techniques We introduce......This thesis examines object-oriented modelling in experimental system development. Object-oriented modelling aims at representing concepts and phenomena of a problem domain in terms of classes and objects. Experimental system development seeks active experimentation in a system development project...

Special Aspects of Learning Objectives Design for Disciplines in Engineering Education

Directory of Open Access Journals (Sweden)

Yu. B. Tsvetkov

2015-01-01

Full Text Available The article is devoted to a problem of learning objectives design for disciplines in engineering education. It is shown that the system of well defined objectives can form a basis of discipline content analysis, acquisition control and improvement.The detailed defining of clear objectives and designing forms and content of objectives which allow to estimate their achievement are considered.For this purpose the objectives should consider the level of learners, to designate result which they will be able to show after training, conditions and how well they will be able to make it.Some examples of objective formulations are provided which allow to show in an explicit form the results reached by a learner.It is shown that cognitive process dimension can be divided into groups of initial level of thinking (to remember, understand, apply and thinking of high level (to analyze, estimate, create.Thus knowledge dimension include the factual, conceptual, procedural and metacognitive knowledges.On the basis of cognitive process dimension and knowledge dimension in engineering education it is offered to form system of learning objectives on the basis of their twodimensional classification - taxonomy.Objectives examples for engineering discipline are given. They consider conditions of their achievement and criteria of execution for various combinations of cognitive process dimension and levels of knowledge dimension.For some engineering disciplines examples of learning objectives are formulated including their achievement and criterion of execution of the corresponding actions.The given results can form a basis for design of learning objectives at realization of a competence approach in modern engineering education.Further work in this direction preplan the analysis and approbation of two-dimensional matrix applicability for objectives design on examples of various engineering disciplines.It is of profound importance to use matrixes of well defined

An Initial Approach for Learning Objects from Experience

Science.gov (United States)

2018-05-02

algorithm to delineate objects which are then fed to a simple feed-forward neural network without any other processes in the pipeline. Our neural network...These are the basic requirements for the pipeline and are discussed in more detail below. Additionally, we are interested in testing various parts...that continuously learning objects from experience requires mechanisms to do the following: 1) Focus attention on things and stuff of interest . 2

Recurrent processing during object recognition

Directory of Open Access Journals (Sweden)

Randall C. O'Reilly

2013-04-01

Full Text Available How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of of naturally-occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain's visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

Science.gov (United States)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

I learning object: la condivisione dei materiali didattici come naturale evoluzione del web

Directory of Open Access Journals (Sweden)

Corrado Petrucco

2004-01-01

Full Text Available Discussion of Learning Objects (LO and sharing of educational materials. In addition to the standards that exist today, some issues are dealt with the emergence of these new objects of learning.

The Proposed Model of Collaborative Virtual Learning Environment for Introductory Programming Course

Science.gov (United States)

Othman, Mahfudzah; Othman, Muhaini

2012-01-01

This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the "Think-Pair-Share". The main objective of this study is to design a model for an online learning system that facilitates the…

Improving Student Learning Outcomes Marketing Strategy Lesson By Applying SFAE Learning Model

Directory of Open Access Journals (Sweden)

Winda Nur Rohmawati

2017-11-01

Full Text Available Research objectives for improving student learning outcomes on the subjects of marketing strategy through the implementation of model learning SFAE. This type of research this is a class action research using a qualitative approach which consists of two cycles with the subject Marketing X grade SMK YPI Darussalam 2 Cerme Gresik Regency. This research consists of four stages: (1 the Planning Act, (2 the implementation of the action, (3 observations (observation, and (4 Reflection. The result of the research shows that cognitive and affective learning outcomes of students have increased significantly.

Information Recovery Algorithm for Ground Objects in Thin Cloud Images by Fusing Guide Filter and Transfer Learning

Directory of Open Access Journals (Sweden)

HU Gensheng

2018-03-01

Full Text Available Ground object information of remote sensing images covered with thin clouds is obscure. An information recovery algorithm for ground objects in thin cloud images is proposed by fusing guide filter and transfer learning. Firstly, multi-resolution decomposition of thin cloud target images and cloud-free guidance images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform. Then the decomposed low frequency subbands are processed by using support vector guided filter and transfer learning respectively. The decomposed high frequency subbands are enhanced by using modified Laine enhancement function. The low frequency subbands output by guided filter and those predicted by transfer learning model are fused by the method of selection and weighting based on regional energy. Finally, the enhanced high frequency subbands and the fused low frequency subbands are reconstructed by using inverse multi-directional nonsubsampled dual-tree complex wavelet transform to obtain the ground object information recovery images. Experimental results of Landsat-8 OLI multispectral images show that, support vector guided filter can effectively preserve the detail information of the target images, domain adaptive transfer learning can effectively extend the range of available multi-source and multi-temporal remote sensing images, and good effects for ground object information recover are obtained by fusing guide filter and transfer learning to remove thin cloud on the remote sensing images.

Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects

Directory of Open Access Journals (Sweden)

Di Feng

2018-02-01

Full Text Available Reusing the tactile knowledge of some previously-explored objects (prior objects helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT, and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10 % when it used only one training sample with the feature observations of prior objects. By further incorporating the predictions from the observation models of prior objects as auxiliary features, our method improved the discrimination accuracy by over 20 % . The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge transfer.

Algorithms for Learning Preferences for Sets of Objects

Science.gov (United States)

Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric

2010-01-01

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical

Students’ Views on Different Learning Objects Types

DEFF Research Database (Denmark)

Natsis, Antonios; Hormova, Hara; Mikropoulos, Tassos

2014-01-01

of different type: an educational game, a dynamic simulation and a digital concept map. The basic difference among these three LOs is the fact that both dynamic simulation and concept map are lacking game-like characteristics. The educational game has as a learning goal to familiarize students......The paper attempts to compare students’ views on three different Learning Objects (LOs), also known as Web-Based Learning Tools (WBLTs), which are used for educational purposes aiming at natural disaster readiness. Following an iterative development process, 100 LOs of various types are being...... they will be protected during the earthquake. The educational game comprises of 9 levels of ascending difficulty that have to be completed so as the game to be ended. The dynamic simulation aims to familiarize students with the causes of fog. In that context, they move temperature, wind and humidity bars and thus...

Fast automated segmentation of multiple objects via spatially weighted shape learning

Science.gov (United States)

Chandra, Shekhar S.; Dowling, Jason A.; Greer, Peter B.; Martin, Jarad; Wratten, Chris; Pichler, Peter; Fripp, Jurgen; Crozier, Stuart

2016-11-01

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice’s similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

THE PROPOSED MODEL OF COLLABORATIVE VIRTUAL LEARNING ENVIRONMENT FOR INTRODUCTORY PROGRAMMING COURSE

Directory of Open Access Journals (Sweden)

Mahfudzah OTHMAN

2012-01-01

Full Text Available This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the “Think-Pair-Share”. The main objective of this study is to design a model for an online learning system that facilitates the collaborative learning activities in a virtual environment such as online communications and pair or small group discussions. In order to model the virtual learning environment, the RUP methodology has been used where it involves the data collection phase and the analysis and design phase. Fifty respondents have been randomly selected to participate in the data collection phase to investigate the students’ interest and learning styles as well as their learning preferences. The results have shown the needs for the development of online small group discussions that can be used as an alternative learning style for programming courses. The proposed design of the virtual learning system named as the Online Collaborative Learning System or OCLS is being depicted using the object-oriented models which are the use-case model and class diagram in order to show the concise processes of virtual “Think-Pair-Share” collaborative activities. The “Think-Pair-Share” collaborative learning technique that is being used in this model has been chosen because of its simplicity and relatively low-risk. This paper also presents the proposed model of the system’s architecture that will become the guidelines for the physical development of OCLS using the web-based applications.

Object tracking by occlusion detection via structured sparse learning

KAUST Repository

Zhang, Tianzhu

2013-06-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

How do newcomers learn to use an object?

DEFF Research Database (Denmark)

Kjær, Malene

in the daily practice of assessing how a patient is doing. Learning how to operate it in situ is thus an important task.I will present an empirical example from clinical nursing education in a Danish hospital, where students learn to use specific medical objects (a sphygmomanometer) in the setting...... status and stance (Heritage, 2012) epistemic, cooperative and instrumental stance (Goodwin, 2007) is important, as is the understanding of situated embodied cognition in the workplace practice: the knowledge ‘understanding and use of objects’ that has been limited to the nurse, translates through...

Safe robot execution in model-based reinforcement learning

OpenAIRE

Martínez Martínez, David; Alenyà Ribas, Guillem; Torras, Carme

2015-01-01

Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e.g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans...

The Deflector Selector: A machine learning framework for prioritizing hazardous object deflection technology development

Science.gov (United States)

Nesvold, E. R.; Greenberg, A.; Erasmus, N.; van Heerden, E.; Galache, J. L.; Dahlstrom, E.; Marchis, F.

2018-05-01

Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

The Deflector Selector: A Machine Learning Framework for Prioritizing Hazardous Object Deflection Technology Development

Science.gov (United States)

Nesvold, Erika; Greenberg, Adam; Erasmus, Nicolas; Van Heerden, Elmarie; Galache, J. L.; Dahlstrom, Eric; Marchis, Franck

2018-01-01

Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We will present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We will describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

"The Spiral Model for the Development of Coordination": A Learning Model Based on Eshkol-Wachman Movement Notation (EWMN)

Science.gov (United States)

Al-Dor, Nira

2006-01-01

The objective of this study is to present "The Spiral Model for the Development of Coordination" (SMDC), a learning model that reflects the complexity and possibilities embodied in the learning of movement notation Eshkol-Wachman (EWMN), an Israeli invention. This model constituted the infrastructure for a comprehensive study that examined the…

Cloud Computing and Multi Agent System to improve Learning Object Paradigm

Directory of Open Access Journals (Sweden)

Ana B. Gil

2015-05-01

Full Text Available The paradigm of Learning Object provides Educators and Learners with the ability to access an extensive number of learning resources. To do so, this paradigm provides different technologies and tools, such as federated search platforms and storage repositories, in order to obtain information ubiquitously and on demand. However, the vast amount and variety of educational content, which is distributed among several repositories, and the existence of various and incompatible standards, technologies and interoperability layers among repositories, constitutes a real problem for the expansion of this paradigm. This study presents an agent-based architecture that uses the advantages provided by Cloud Computing platforms to deal with the open issues on the Learning Object paradigm.

APA's Learning Objectives for Research Methods and Statistics in Practice: A Multimethod Analysis

Science.gov (United States)

Tomcho, Thomas J.; Rice, Diana; Foels, Rob; Folmsbee, Leah; Vladescu, Jason; Lissman, Rachel; Matulewicz, Ryan; Bopp, Kara

2009-01-01

Research methods and statistics courses constitute a core undergraduate psychology requirement. We analyzed course syllabi and faculty self-reported coverage of both research methods and statistics course learning objectives to assess the concordance with APA's learning objectives (American Psychological Association, 2007). We obtained a sample of…

Static Object Detection Based on a Dual Background Model and a Finite-State Machine

Directory of Open Access Journals (Sweden)

Heras Evangelio Rubén

2011-01-01

Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

Personalized Learning Objects Recommendation Based on the Semantic-Aware Discovery and the Learner Preference Pattern

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Wang, Tzone I; Tsai, Kun Hua; Lee, Ming Che; Chiu, Ti Kai

2007-01-01

With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future,…

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

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Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

DEVELOPMENT OF SCIENCE PROCESS SKILLS STUDENTS WITH PROJECT BASED LEARNING MODEL- BASED TRAINING IN LEARNING PHYSICS

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Ratna Malawati

2016-06-01

Full Text Available This study aims to improve the physics Science Process Skills Students on cognitive and psychomotor aspects by using model based Project Based Learning training.The object of this study is the Project Based Learning model used in the learning process of Computationa Physics.The method used is classroom action research through two learning cycles, each cycle consisting of the stages of planning, implementation, observation and reflection. In the first cycle of treatment with their emphasis given training in the first phase up to third in the model Project Based Learning, while the second cycle is given additional treatment with emphasis discussion is collaboration in achieving the best results for each group of products. The results of data analysis showed increased ability to think Students on cognitive and Science Process Skills in the psychomotor.

Design Guide for Earth System Science Education: Common Student Learning Objectives and Special Pedagogical Approaches

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Baker, D.

2006-12-01

As part of the NASA-supported undergraduate Earth System Science Education (ESSE) program, fifty-seven institutions have developed and implemented a wide range of Earth system science (ESS) courses, pedagogies, and evaluation tools. The Teaching, Learning, and Evaluation section of USRA's online ESSE Design Guide showcases these ESS learning environments. This Design Guide section also provides resources for faculty who wish to develop ESS courses. It addresses important course design issues including prior student knowledge and interests, student learning objectives, learning resources, pedagogical approaches, and assessments tied to student learning objectives. The ESSE Design Guide provides links to over 130 ESS course syllabi at introductory, senior, and graduate levels. ESS courses over the past 15 years exhibit common student learning objectives and unique pedagogical approaches. From analysis of ESS course syllabi, seven common student learning objectives emerged: 1) demonstrate systems thinking, 2) develop an ESS knowledge base, 3) apply ESS to the human dimension, 4) expand and apply analytical skills, 5) improve critical thinking skills, 6) build professional/career skills, and 7) acquire an enjoyment and appreciation for science. To meet these objectives, ESSE often requires different ways of teaching than in traditional scientific disciplines. This presentation will highlight some especially successful pedagogical approaches for creating positive and engaging ESS learning environments.

Working memory contributes to the encoding of object location associations: Support for a 3-part model of object location memory.

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Gillis, M Meredith; Garcia, Sarah; Hampstead, Benjamin M

2016-09-15

A recent model by Postma and colleagues posits that the encoding of object location associations (OLAs) requires the coordination of several cognitive processes mediated by ventral (object perception) and dorsal (spatial perception) visual pathways as well as the hippocampus (feature binding) [1]. Within this model, frontoparietal network recruitment is believed to contribute to both the spatial processing and working memory task demands. The current study used functional magnetic resonance imaging (fMRI) to test each step of this model in 15 participants who encoded OLAs and performed standard n-back tasks. As expected, object processing resulted in activation of the ventral visual stream. Object in location processing resulted in activation of both the ventral and dorsal visual streams as well as a lateral frontoparietal network. This condition was also the only one to result in medial temporal lobe activation, supporting its role in associative learning. A conjunction analysis revealed areas of shared activation between the working memory and object in location phase within the lateral frontoparietal network, anterior insula, and basal ganglia; consistent with prior working memory literature. Overall, findings support Postma and colleague's model and provide clear evidence for the role of working memory during OLA encoding. Published by Elsevier B.V.

An insect-inspired model for visual binding I: learning objects and their characteristics.

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Northcutt, Brandon D; Dyhr, Jonathan P; Higgins, Charles M

2017-04-01

Visual binding is the process of associating the responses of visual interneurons in different visual submodalities all of which are responding to the same object in the visual field. Recently identified neuropils in the insect brain termed optic glomeruli reside just downstream of the optic lobes and have an internal organization that could support visual binding. Working from anatomical similarities between optic and olfactory glomeruli, we have developed a model of visual binding based on common temporal fluctuations among signals of independent visual submodalities. Here we describe and demonstrate a neural network model capable both of refining selectivity of visual information in a given visual submodality, and of associating visual signals produced by different objects in the visual field by developing inhibitory neural synaptic weights representing the visual scene. We also show that this model is consistent with initial physiological data from optic glomeruli. Further, we discuss how this neural network model may be implemented in optic glomeruli at a neuronal level.

It's all connected: Pathways in visual object recognition and early noun learning.

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Smith, Linda B

2013-11-01

A developmental pathway may be defined as the route, or chain of events, through which a new structure or function forms. For many human behaviors, including object name learning and visual object recognition, these pathways are often complex and multicausal and include unexpected dependencies. This article presents three principles of development that suggest the value of a developmental psychology that explicitly seeks to trace these pathways and uses empirical evidence on developmental dependencies among motor development, action on objects, visual object recognition, and object name learning in 12- to 24-month-old infants to make the case. The article concludes with a consideration of the theoretical implications of this approach. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Selective bilateral amygdala lesions in rhesus monkeys fail to disrupt object reversal learning.

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Izquierdo, Alicia; Murray, Elisabeth A

2007-01-31

Neuropsychological studies in nonhuman primates have led to the view that the amygdala plays an essential role in stimulus-reward association. The main evidence in support of this idea is that bilateral aspirative or radiofrequency lesions of the amygdala yield severe impairments on object reversal learning, a task that assesses the ability to shift choices of objects based on the presence or absence of food reward (i.e., reward contingency). The behavioral effects of different lesion techniques, however, can vary. The present study therefore evaluated the effects of selective, excitotoxic lesions of the amygdala in rhesus monkeys on object reversal learning. For comparison, we tested the same monkeys on a task known to be sensitive to amygdala damage, the reinforcer devaluation task. Contrary to previous results based on less selective lesion techniques, monkeys with complete excitotoxic amygdala lesions performed object reversal learning as quickly as controls. As predicted, however, the same operated monkeys were impaired in making object choices after devaluation of the associated food reinforcer. The results suggest two conclusions. First, the results demonstrate that the amygdala makes a selective contribution to stimulus-reward association; the amygdala is critical for guiding object choices after changes in reward value but not after changes in reward contingency. Second, the results implicate a critical contribution to object reversal learning of structures nearby the amygdala, perhaps the subjacent rhinal cortex.

Internal attention to features in visual short-term memory guides object learning.

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Fan, Judith E; Turk-Browne, Nicholas B

2013-11-01

Attending to objects in the world affects how we perceive and remember them. What are the consequences of attending to an object in mind? In particular, how does reporting the features of a recently seen object guide visual learning? In three experiments, observers were presented with abstract shapes in a particular color, orientation, and location. After viewing each object, observers were cued to report one feature from visual short-term memory (VSTM). In a subsequent test, observers were cued to report features of the same objects from visual long-term memory (VLTM). We tested whether reporting a feature from VSTM: (1) enhances VLTM for just that feature (practice-benefit hypothesis), (2) enhances VLTM for all features (object-based hypothesis), or (3) simultaneously enhances VLTM for that feature and suppresses VLTM for unreported features (feature-competition hypothesis). The results provided support for the feature-competition hypothesis, whereby the representation of an object in VLTM was biased towards features reported from VSTM and away from unreported features (Experiment 1). This bias could not be explained by the amount of sensory exposure or response learning (Experiment 2) and was amplified by the reporting of multiple features (Experiment 3). Taken together, these results suggest that selective internal attention induces competitive dynamics among features during visual learning, flexibly tuning object representations to align with prior mnemonic goals. Copyright © 2013 Elsevier B.V. All rights reserved.

Problem-Based Learning Associated by Action-Process-Object-Schema (APOS) Theory to Enhance Students' High Order Mathematical Thinking Ability

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Mudrikah, Achmad

2016-01-01

The research has shown a model of learning activities that can be used to stimulate reflective abstraction in students. Reflective abstraction as a method of constructing knowledge in the Action-Process-Object-Schema theory, and is expected to occur when students are in learning activities, will be able to encourage students to make the process of…

Research on Daily Objects Detection Based on Deep Neural Network

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Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Object-based implicit learning in visual search: perceptual segmentation constrains contextual cueing.

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Conci, Markus; Müller, Hermann J; von Mühlenen, Adrian

2013-07-09

In visual search, detection of a target is faster when it is presented within a spatial layout of repeatedly encountered nontarget items, indicating that contextual invariances can guide selective attention (contextual cueing; Chun & Jiang, 1998). However, perceptual regularities may interfere with contextual learning; for instance, no contextual facilitation occurs when four nontargets form a square-shaped grouping, even though the square location predicts the target location (Conci & von Mühlenen, 2009). Here, we further investigated potential causes for this interference-effect: We show that contextual cueing can reliably occur for targets located within the region of a segmented object, but not for targets presented outside of the object's boundaries. Four experiments demonstrate an object-based facilitation in contextual cueing, with a modulation of context-based learning by relatively subtle grouping cues including closure, symmetry, and spatial regularity. Moreover, the lack of contextual cueing for targets located outside the segmented region was due to an absence of (latent) learning of contextual layouts, rather than due to an attentional bias towards the grouped region. Taken together, these results indicate that perceptual segmentation provides a basic structure within which contextual scene regularities are acquired. This in turn argues that contextual learning is constrained by object-based selection.

Creating Learning Objects to Enhance the Educational Experiences of American Sign Language Learners: An Instructional Development Report

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Simone Conceição

2002-10-01

Full Text Available Little attention has been given to involving the deaf community in distance teaching and learning or in designing courses that relate to their language and culture. This article reports on the design and development of video-based learning objects created to enhance the educational experiences of American Sign Language (ASL hearing participants in a distance learning course and, following the course, the creation of several new applications for use of the learning objects. The learning objects were initially created for the web, as a course component for review and rehearsal. The value of the web application, as reported by course participants, led us to consider ways in which the learning objects could be used in a variety of delivery formats: CD-ROM, web-based knowledge repository, and handheld device. The process to create the learning objects, the new applications, and lessons learned are described.

Computational Modelling of the Neural Representation of Object Shape in the Primate Ventral Visual System

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Akihiro eEguchi

2015-08-01

Full Text Available Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and temporal continuity of the visual inputs during training to learn firing properties that are similar to neurons in V4 and TEO. Neurons in V4 encode the conformation of boundary contour elements at a particular position within an object regardless of the location of the object on the retina, while neurons in TEO integrate information from multiple boundary contour elements. This representation goes beyond mere object recognition, in which neurons simply respond to the presence of a whole object, but provides an essential foundation from which the brain is subsequently able to recognise the whole object.

Feedforward Object-Vision Models Only Tolerate Small Image Variations Compared to Human

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Masoud eGhodrati

2014-07-01

Full Text Available Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modelling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well when images with more complex variations of the same object are applied to them. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e. briefly presented masked stimuli with complex image variations, human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modelling. We show that this approach is not of significant help in solving the computational crux of object recognition (that is invariant object recognition when the identity-preserving image variations become more complex.

ATTITUDES OF STUDENTS TOWARDS LEARNING OBJECTS IN WEB-BASED LANGUAGE LEARNING

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Ahmet BASAL

2012-01-01

Full Text Available Language education is important in the rapidly changing world. Every year much effort has spent on preparing teaching materials for language education. Since positive attitudes of learners towards a teaching material enhance the effectiveness of that material, it is important to determine the attitudes of learners towards the material used. Learning objects (LOs are a new type of material on which many studies have been conducted in recent years. The aim of this study is to determine the attitudes of students towards LOs in web-based language learning. To this end, the study was conducted in English I Course at the Department of Computer Programming in Kırıkkale University in 2010-2011 Fall Semester. Seventy LOs appropriate for six-week long lecture program were integrated into the Learning Management System (LMS of Kırıkkale University. The study group consisted of 38 students. After the six weeks long implementation period of the study, an attitude scale was administered to the students. The findings indicated that students in web based language education have positive attitudes towards LOs.

Using IMS Learning Design to model collaborative learning activities

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Tattersall, Colin

2006-01-01

IMS Learning Design provides a counter to the trend towards designing for lone-learners reading from screens. It guides staff and educational developers to start not with content, but with learning activities and the achievement of learning objectives. It recognises that learning can happen without

Ego-Motion and Tracking for Continuous Object Learning: A Brief Survey

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2017-09-01

past research related to the tasks of ego-motion estimation and object tracking from the viewpoint of their role in continuous object learning...in visual object tracking, competitions are held each year to identify the most accurate and robust tracking implementations. Over recent competitions...information should they share) or vice versa? These are just some of the questions that must be addressed in future research toward continuous object

Models for predicting objective function weights in prostate cancer IMRT

International Nuclear Information System (INIS)

Boutilier, Justin J.; Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.

2015-01-01

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

Models for predicting objective function weights in prostate cancer IMRT

Energy Technology Data Exchange (ETDEWEB)

Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8 (Canada); Craig, Tim [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Sharpe, Michael B. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9 (Canada); Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada); Chan, Timothy C. Y. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada)

2015-04-15

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

Semantic Linking of Learning Object Repositories to DBpedia

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Lama, Manuel; Vidal, Juan C.; Otero-Garcia, Estefania; Bugarin, Alberto; Barro, Senen

2012-01-01

Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15…

Design, Implementation and Evaluation of a Learning Object that Supports the Mathematics Learning in Children with Autism Spectrum Disorders

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Roberto Munoz

2018-04-01

Full Text Available Information technologies have been widely used for entertainment and learning purposes by children with Autism Spectrum Disorders (ASD. Nonetheless, learning objects aiming at specific skills development in children with ASD require both a well bounded learning domain and a user-centered design process, considering skill levels of the users and the local geographical context and language. “Proyect@ Matemáticas” is a multi-touch based app designed for developing pre-calculus and functional mathematical skills in children with ASD, according to the Chilean regulations of learning goals for children with special educational necessities. This paper presents the User-centered design process conducted in order to develop the learning object, which included the evaluation by 15 experts in special educational needs, testing by 10 ASD-diagnosed children with different functional levels, and a multidisciplinary development team that also included a graphic designer diagnosed with ASD of high functionality. The development process yields to a validated learning object in terms of interactivity, design, engagement, and usability, from the point of view of the experts, and successful usage tests with ASD diagnosed children in terms of performance and achievement of learning outcomes. The application is currently available for download in the Google Play store for free, and currently has more than 15,000 downloads and an average rating of 4.2 out of 5 points.

The role of professional objects in technology-enhanced learning environments in higher education

NARCIS (Netherlands)

Zitter, I.I.; Bruijn, E. de; Simons, P.R.J.; Cate, Th.J. ten

2010-01-01

We study project-based, technology-enhanced learning environments in higher education, which should produce, by means of specific mechanisms, learning outcomes in terms of transferable knowledge and learning-, thinking-, collaboration- and regulation-skills. Our focus is on the role of objects from

Storytelling in the digital world: achieving higher-level learning objectives.

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Schwartz, Melissa R

2012-01-01

Nursing students are not passive media consumers but instead live in a technology ecosystem where digital is the language they speak. To prepare the next generation of nurses, educators must incorporate multiple technologies to improve higher-order learning. The author discusses the evolution and use of storytelling as part of the digital world and how digital stories can be aligned with Bloom's Taxonomy so that students achieve higher-level learning objectives.

The Sloan-C Pillars and Boundary Objects As a Framework for Evaluating Blended Learning

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Laumakis, Mark; Graham, Charles; Dziuban, Chuck

2009-01-01

The authors contend that blended learning represents a boundary object; a construct that brings together constituencies from a variety of backgrounds with each of these cohorts defining the object somewhat differently. The Sloan-C Pillars (learning effectiveness, access, cost effectiveness, student satisfaction, and faculty satisfaction) provide…

Learning object location predictors with boosting and grammar-guided feature extraction

Energy Technology Data Exchange (ETDEWEB)

Eads, Damian Ryan [Los Alamos National Laboratory; Rosten, Edward [UNIV OF CAMBRIDGE; Helmbold, David [UC/SANTA CRUZ

2009-01-01

The authors present BEAMER: a new spatially exploitative approach to learning object detectors which shows excellent results when applied to the task of detecting objects in greyscale aerial imagery in the presence of ambiguous and noisy data. There are four main contributions used to produce these results. First, they introduce a grammar-guided feature extraction system, enabling the exploration of a richer feature space while constraining the features to a useful subset. This is specified with a rule-based generative grammer crafted by a human expert. Second, they learn a classifier on this data using a newly proposed variant of AdaBoost which takes into account the spatially correlated nature of the data. Third, they perform another round of training to optimize the method of converting the pixel classifications generated by boosting into a high quality set of (x,y) locations. lastly, they carefully define three common problems in object detection and define two evaluation criteria that are tightly matched to these problems. Major strengths of this approach are: (1) a way of randomly searching a broad feature space, (2) its performance when evaluated on well-matched evaluation criteria, and (3) its use of the location prediction domain to learn object detectors as well as to generate detections that perform well on several tasks: object counting, tracking, and target detection. They demonstrate the efficacy of BEAMER with a comprehensive experimental evaluation on a challenging data set.

A biologically inspired neural network model to transformation invariant object recognition

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Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

The Kinematic Learning Model using Video and Interfaces Analysis

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Firdaus, T.; Setiawan, W.; Hamidah, I.

2017-09-01

An educator currently in demand to apply the learning to not be separated from the development of technology. Educators often experience difficulties when explaining kinematics material, this is because kinematics is one of the lessons that often relate the concept to real life. Kinematics is one of the courses of physics that explains the cause of motion of an object, Therefore it takes the thinking skills and analytical skills in understanding these symptoms. Technology is one that can bridge between conceptual relationship with real life. A framework of technology-based learning models has been developed using video and interfaces analysis on kinematics concept. By using this learning model, learners will be better able to understand the concept that is taught by the teacher. This learning model is able to improve the ability of creative thinking, analytical skills, and problem-solving skills on the concept of kinematics.

The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate

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Sutiani, Ani; Silitonga, Mei Y.

2017-08-01

This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

Proof of Economic Viability of Blended Learning Business Models

Science.gov (United States)

Druhmann, Carsten; Hohenberg, Gregor

2014-01-01

The discussion on economically sustainable business models with respect to information technology is lacking in many aspects of proven approaches. In the following contribution the economic viability is valued based on a procedural model for design and evaluation of e-learning business models in the form of a case study. As a case study object a…

Mobile Authoring of Open Educational Resources as Reusable Learning Objects

Directory of Open Access Journals (Sweden)

Dr Kinshuk

2013-06-01

Full Text Available E-learning technologies have allowed authoring and playback of standardized reusable learning objects (RLO for several years. Effective mobile learning requires similar functionality at both design time and runtime. Mobile devices can play RLO using applications like SMILE, mobile access to a learning management system (LMS, or other systems which deploy content to mobile learners (Castillo & Ayala, 2008; Chu, Hwang, & Tseng, 2010; Hsu & Chen, 2010; Nakabayashi, 2009; Zualkernan, Nikkhah, & Al-Sabah, 2009. However, implementations which author content in a mobile context do not typically permit reuse across multiple contexts due to a lack of standardization. Standards based (IMS and SCORM authoring implementations exist for non-mobile platforms (Gonzalez-Barbone & Anido-Rifon, 2008; Griffiths, Beauvoir, Liber, & Barrett-Baxendale, 2009; Téllez, 2010; Yang, Chiu, Tsai, & Wu, 2004. However, this paradigm precludes capturing learning where and when it occurs. Consequently, RLO authored for e-learning lack learner generated content, especially with timely, relevant, and location aware examples.

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

Directory of Open Access Journals (Sweden)

Akira Taniguchi

2017-12-01

Full Text Available In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color. This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

Using Learning Games to Meet Learning Objectives

DEFF Research Database (Denmark)

Henriksen, Thomas Duus

2013-01-01

This paper addresses the question on how learning games can be used to meet with the different levels in Bloom’s and the SOLO taxonomy, which are commonly used for evaluating the learning outcome of educational activities. The paper discusses the quality of game-based learning outcomes based on a...... on a case study of the learning game 6Styles....

Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.

Science.gov (United States)

Chen, Chi-Hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

2017-08-01

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co-occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. Copyright © 2016 Cognitive Science Society, Inc.

A Model for Learning Over Time: The Big Picture

Science.gov (United States)

Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

2002-01-01

Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry

Science.gov (United States)

sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.

2018-04-01

The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

Using visual lateralization to model learning and memory in zebrafish larvae.

Science.gov (United States)

Andersson, Madelene Åberg; Ek, Fredrik; Olsson, Roger

2015-03-02

Impaired learning and memory are common symptoms of neurodegenerative and neuropsychiatric diseases. Present, there are several behavioural test employed to assess cognitive functions in animal models, including the frequently used novel object recognition (NOR) test. However, although atypical functional brain lateralization has been associated with neuropsychiatric conditions, spanning from schizophrenia to autism, few animal models are available to study this phenomenon in learning and memory deficits. Here we present a visual lateralization NOR model (VLNOR) in zebrafish larvae as an assay that combines brain lateralization and NOR. In zebrafish larvae, learning and memory are generally assessed by habituation, sensitization, or conditioning paradigms, which are all representatives of nondeclarative memory. The VLNOR is the first model for zebrafish larvae that studies a memory similar to the declarative memory described for mammals. We demonstrate that VLNOR can be used to study memory formation, storage, and recall of novel objects, both short and long term, in 10-day-old zebrafish. Furthermore we show that the VLNOR model can be used to study chemical modulation of memory formation and maintenance using dizocilpine (MK-801), a frequently used non-competitive antagonist of the NMDA receptor, used to test putative antipsychotics in animal models.

A visual tracking method based on deep learning without online model updating

Science.gov (United States)

Tang, Cong; Wang, Yicheng; Feng, Yunsong; Zheng, Chao; Jin, Wei

2018-02-01

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

Validation of virtual learning object to support the teaching of nursing care systematization

Directory of Open Access Journals (Sweden)

Pétala Tuani Candido de Oliveira Salvador

Full Text Available ABSTRACT Objective: to describe the content validation process of a Virtual Learning Object to support the teaching of nursing care systematization to nursing professionals. Method: methodological study, with quantitative approach, developed according to the methodological reference of Pasquali's psychometry and conducted from March to July 2016, from two-stage Delphi procedure. Results: in the Delphi 1 stage, eight judges evaluated the Virtual Object; in Delphi 2 stage, seven judges evaluated it. The seven screens of the Virtual Object were analyzed as to the suitability of its contents. The Virtual Learning Object to support the teaching of nursing care systematization was considered valid in its content, with a Total Content Validity Coefficient of 0.96. Conclusion: it is expected that the Virtual Object can support the teaching of nursing care systematization in light of appropriate and effective pedagogical approaches.

Multi-objective group scheduling with learning effect in the cellular manufacturing system

Directory of Open Access Journals (Sweden)

Mohammad Taghi Taghavi-fard

2011-01-01

Full Text Available Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II and non-dominated rank genetic algorithm (NRGA. The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.

[Learning objectives achievement in ethics education for medical school students].

Science.gov (United States)

Chae, Sujin; Lim, Kiyoung

2015-06-01

This study aimed to examine the necessity for research ethics and learning objectives in ethics education at the undergraduate level. A total of 393 fourth-year students, selected from nine medical schools, participated in a survey about learning achievement and the necessity for it. It was found that the students had very few chances to receive systematic education in research ethics and that they assumed that research ethics education was provided during graduate school or residency programs. Moreover, the students showed a relatively high learning performance in life ethics, while learning achievement was low in research ethics. Medical school students revealed low interest in and expectations of research ethics in general; therefore, it is necessary to develop guidelines for research ethics in the present situation, in which medical education mainly focuses on life ethics.

The POL Model: Using a Social Constructivist Framework to Develop Blended and Online Learning

DEFF Research Database (Denmark)

Dalsgaard, Christian; Godsk, Mikkel

2007-01-01

The paper presents a model for developing blended and online learning based on a given curriculum and typical learning objectives for university courses. The model consists of a three-step-process in which the instructor formulates product-oriented tasks, develops and structures the learning...... materials and tools, outlines a schedule, and supports the students' learning activity in developing a product. The model is based on our experiences with transforming traditional lecture-based lessons into problem-based blended and online learning using a social constructivist approach and a standard...... virtual learning environment (VLE). Our initial experiments indicate that our model is useful to develop blended and online modules and, furthermore, it seems fruitful to use a social constructivist framework and orienting learning activities towards the development of products....

Development of national competency-based learning objectives "Medical Informatics" for undergraduate medical education.

Science.gov (United States)

Röhrig, R; Stausberg, J; Dugas, M

2013-01-01

The aim of this project is to develop a catalogue of competency-based learning objectives "Medical Informatics" for undergraduate medical education (abbreviated NKLM-MI in German). The development followed a multi-level annotation and consensus process. For each learning objective a reason why a physician needs this competence was required. In addition, each objective was categorized according to the competence context (A = covered by medical informatics, B = core subject of medical informatics, C = optional subject of medical informatics), the competence level (1 = referenced knowledge, 2 = applied knowledge, 3 = routine knowledge) and a CanMEDS competence role (medical expert, communicator, collaborator, manager, health advocate, professional, scholar). Overall 42 objectives in seven areas (medical documentation and information processing, medical classifications and terminologies, information systems in healthcare, health telematics and telemedicine, data protection and security, access to medical knowledge and medical signal-/image processing) were identified, defined and consented. With the NKLM-MI the competences in the field of medical informatics vital to a first year resident physician are identified, defined and operationalized. These competencies are consistent with the recommendations of the International Medical Informatics Association (IMIA). The NKLM-MI will be submitted to the National Competence-Based Learning Objectives for Undergraduate Medical Education. The next step is implementation of these objectives by the faculties.

Where's Waldo? How perceptual, cognitive, and emotional brain processes cooperate during learning to categorize and find desired objects in a cluttered scene

Directory of Open Access Journals (Sweden)

Stephen eGrossberg

2014-06-01

Full Text Available The Where’s Waldo problem concerns how individuals can rapidly learn to search a scene to detect, attend, recognize, and look at a valued target object in it. This article develops the ARTSCAN Search neural model to clarify how brain mechanisms across the What and Where cortical streams are coordinated to solve the Where's Waldo problem. The What stream learns positionally-invariant object representations, whereas the Where stream controls positionally-selective spatial and action representations. The model overcomes deficiencies of these computationally complementary properties through What and Where stream interactions. Where stream processes of spatial attention and predictive eye movement control modulate What stream processes whereby multiple view- and positionally-specific object categories are learned and associatively linked to view- and positionally-invariant object categories through bottom-up and attentive top-down interactions. Gain fields control the coordinate transformations that enable spatial attention and predictive eye movements to carry out this role. What stream cognitive-emotional learning processes enable the focusing of motivated attention upon the invariant object categories of desired objects. What stream cognitive names or motivational drives can prime a view- and positionally-invariant object category of a desired target object. A volitional signal can convert these primes into top-down activations that can, in turn, prime What stream view- and positionally-specific categories. When it also receives bottom-up activation from a target, such a positionally-specific category can cause an attentional shift in the Where stream to the positional representation of the target, and an eye movement can then be elicited to foveate it. These processes describe interactions among brain regions that include visual cortex, parietal cortex inferotemporal cortex, prefrontal cortex, amygdala, basal ganglia, and superior colliculus.

The Nemesis E-Learning 4-Sectors-Model - A Concept to Enhance the Reusability of E-Learning Products

Directory of Open Access Journals (Sweden)

Wilfried Hendricks

2007-06-01

Full Text Available The 4-Sectors-Model has been developed by the TU Berlin and is intended to facilitate providing customized e-learning products to different target learner groups, while keeping the same basic content. This is made possible by the independent development of user interface and content. The different components are assembled at the end to produce the final e-learning product. Software development is based on the Generative Learning Objects concept (UCeL. Further improvements based on results of the ongoing test phase will make the 4-Sectors-Model better adapted to fit user needs. Finally, this project is dedicated to establishing a high standard of didactic quality for the future development of e-learning software at the TU Berlin.

The Usage of E-Learning Model To Optimize Learning System In Higher Education by Using Dick and Carey Design Approach

Directory of Open Access Journals (Sweden)

Anak Agung Gde Satia Utama

2016-04-01

Full Text Available Nowadays many universities in the world apply technology enhanced learning in order to help learning activities. Due to the potentials technology enhanced learning offers, recent education using it and universities in particular are trying to apply it. One of the subjects of this research is The Accounting Department of Airlangga University in Surabaya. The idea of this research is to investigate the students about how they know deeply about e-learning system and learning objectives as a first step to conduct e-learning model. After the model completed, the next step is to prepare database learning. Entity Relationship Diagram (ERD can help to explain the model. The purpose of this research was done by using Dick and Carey Design Model. There are nine steps to conduct e-learning model. All steps can be categorized into three steps research: first is the introduction or empirical study, the next step is the design and the last is the feedback after the implementation. The methodology used in this research is using Qualitative Exploratory, by using questionnaire and interviews as data collection techniques. The analysis of the data shows organization requires information about e-learning content, user as a learning subject and information technology infrastructures. E-learning model as one of the alternative learning can help users to optimized learning.

Memory for Object Locations: Priority Effect and Sex Differences in Associative Spatial Learning

Science.gov (United States)

Cinan, Sevtap; Atalay, Deniz; Sisman, Simge; Basbug, Gokce; Dervent-Ozbek, Sevinc; Teoman, Dalga D.; Karagoz, Ayca; Karadeniz, A. Yezdan; Beykurt, Sinem; Suleyman, Hediye; Memis, H. Ozge; Yurtsever, Ozgur D.

2007-01-01

This paper reports two experiments conducted to examine priority effects and sex differences in object location memory. A new task of paired position-learning was designed, based on the A-B A-C paradigm, which was used in paired word learning. There were three different paired position-learning conditions: (1) positions of several different…

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

Science.gov (United States)

Bhat, Ajaz Ahmad; Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

2016-07-01

Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. © 2016 The Author(s).

Multi-agent system for Knowledge-based recommendation of Learning Objects

Directory of Open Access Journals (Sweden)

Paula Andrea RODRÍGUEZ MARÍN

2015-12-01

Full Text Available Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Autonomous learning of robust visual object detection and identification on a humanoid

NARCIS (Netherlands)

Leitner, J.; Chandrashekhariah, P.; Harding, S.; Frank, M.; Spina, G.; Förster, A.; Triesch, J.; Schmidhuber, J.

2012-01-01

In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for

Transforming clinical imaging and 3D data for virtual reality learning objects: HTML5 and mobile devices implementation.

Science.gov (United States)

Trelease, Robert B; Nieder, Gary L

2013-01-01

Web deployable anatomical simulations or "virtual reality learning objects" can easily be produced with QuickTime VR software, but their use for online and mobile learning is being limited by the declining support for web browser plug-ins for personal computers and unavailability on popular mobile devices like Apple iPad and Android tablets. This article describes complementary methods for creating comparable, multiplatform VR learning objects in the new HTML5 standard format, circumventing platform-specific limitations imposed by the QuickTime VR multimedia file format. Multiple types or "dimensions" of anatomical information can be embedded in such learning objects, supporting different kinds of online learning applications, including interactive atlases, examination questions, and complex, multi-structure presentations. Such HTML5 VR learning objects are usable on new mobile devices that do not support QuickTime VR, as well as on personal computers. Furthermore, HTML5 VR learning objects can be embedded in "ebook" document files, supporting the development of new types of electronic textbooks on mobile devices that are increasingly popular and self-adopted for mobile learning. © 2012 American Association of Anatomists.

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

Science.gov (United States)

Born, Jannis; Stringer, Simon M.

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

Science.gov (United States)

Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

2017-01-01

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning

An Interactive Learning Environment for Teaching the Imperative and Object-Oriented Programming Techniques in Various Learning Contexts

Science.gov (United States)

Xinogalos, Stelios

The acquisition of problem-solving and programming skills in the era of knowledge society seems to be particularly important. Due to the intrinsic difficulty of acquiring such skills various educational tools have been developed. Unfortunately, most of these tools are not utilized. In this paper we present the programming microworlds Karel and objectKarel that support the procedural-imperative and Object-Oriented Programming (OOP) techniques and can be used for supporting the teaching and learning of programming in various learning contexts and audiences. The paper focuses on presenting the pedagogical features that are common to both environments and mainly on presenting the potential uses of these environments.

L.I.M.E. A recommendation model for informal and formal learning, engaged

Directory of Open Access Journals (Sweden)

Daniel Burgos

2013-06-01

Full Text Available In current eLearning models and implementations (e.g. Learning Management Systems-LMS there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre-defined tests, and collateral resources like, i.e. discussion fora and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behaviour, user interaction, and personalised counselling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build an eLearning module that implements this conceptual model in a real application case, and present the preliminary analysis and positive results.

Creation of 'Ukrytie' objects computer model

International Nuclear Information System (INIS)

Mazur, A.B.; Kotlyarov, V.T.; Ermolenko, A.I.; Podbereznyj, S.S.; Postil, S.D.; Shaptala, D.V.

1999-01-01

A partial computer model of the 'Ukrytie' object was created with the use of geoinformation technologies. The computer model makes it possible to carry out information support of the works related to the 'Ukrytie' object stabilization and its conversion into ecologically safe system for analyzing, forecasting and controlling the processes occurring in the 'Ukrytie' object. Elements and structures of the 'Ukryttia' object were designed and input into the model

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Science.gov (United States)

Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

2011-09-01

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Directory of Open Access Journals (Sweden)

James N Ingram

2011-09-01

Full Text Available Motor learning has been extensively studied using dynamic (force-field perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar

Duo: A Human/Wearable Hybrid for Learning About Common Manipulate Objects

National Research Council Canada - National Science Library

Kemp, Charles C

2002-01-01

... with them. Duo is a human/wearable hybrid that is designed to learn about this important domain of human intelligence by interacting with natural manipulable objects in unconstrained environments...

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

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Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

2016-01-01

One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

Learning Photometric Invariance for Object Detection

NARCIS (Netherlands)

Álvarez, J.M.; Gevers, T.; López, A.M.

2010-01-01

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian

Study on process evaluation model of students' learning in practical course

Science.gov (United States)

Huang, Jie; Liang, Pei; Shen, Wei-min; Ye, Youxiang

2017-08-01

In practical course teaching based on project object method, the traditional evaluation methods include class attendance, assignments and exams fails to give incentives to undergraduate students to learn innovatively and autonomously. In this paper, the element such as creative innovation, teamwork, document and reporting were put into process evaluation methods, and a process evaluation model was set up. Educational practice shows that the evaluation model makes process evaluation of students' learning more comprehensive, accurate, and fairly.

Abstraction ability as an indicator of success for learning object-oriented programming?

DEFF Research Database (Denmark)

Bennedsen, Jens Benned; Caspersen, Michael Edelgaard

2006-01-01

ability is operationalized as stages of cognitive development (for which validated tests exist). Programming ability is operationalized as grade in the final assessment of a model-based objects-first CS1. The validity of the operationalizations is discussed. Surprisingly, our study shows......Computer science educators generally agree that abstract thinking is a crucial component for learning computer science in general and programming in particular. We report on a study to confirm the hypothesis that general abstraction ability has a positive impact on programming ability. Abstraction...... that there is no correlation between stage of cognitive development (abstraction ability) and final grade in CS1 (programming ability). Possible explanations are identified....

Cortical Dynamics of Contextually Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

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Huang, Tsung-Ren; Grossberg, Stephen

2010-01-01

How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient…

The Composite OLAP-Object Data Model

Energy Technology Data Exchange (ETDEWEB)

Pourabbas, Elaheh; Shoshani, Arie

2005-12-07

In this paper, we define an OLAP-Object model that combines the main characteristics of OLAP and Object data models in order to achieve their functionalities in a common framework. We classify three different object classes: primitive, regular and composite. Then, we define a query language which uses the path concept in order to facilitate data navigation and data manipulation. The main feature of the proposed language is an anchor. It allows us to fix dynamically an object class (primitive, regular or composite) along the paths over the OLAP-Object data model for expressing queries. The queries can be formulated on objects, composite objects and combination of both. The power of the proposed query language is investigated through multiple query examples. The semantic of different clauses and syntax of the proposed language are investigated.

THE LEARNING RESULT DIFFERENCE OF STUDENT TEACH BY USING ENHANCEMENT LEARNING MODEL OF STUDENT’S THINKING ABILITY WITH CONVENSIONAL MODEL FOR FORCE AND NEWTON LAWS MATERIAL

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Derlina .

2013-06-01

Full Text Available This research was done to observe the difference of learning achievement between student who have been teach by Enhancement Learning Model of Student’s Thinking Ability and Conventional Model. This research was done at SMP Negeri 2 Gebang. Type of this research is quasi experiment. Research population is every student of grade VIII semester 2 SMP Negeri 2 Gebang. Research sample was taken by random sampling around 2 classes as 34 students for experiment class and 34 students for control class. Learning achievement of test objective 20 of multiple choice was done as an instrument. The experiment result of pretest average is 37.94 for experiment class and 36.82 for control class. Treatment was done to each class, post test average score is 73.38 for experiment class and for student who have been teach by conventional learning is 67.05. Hypothetical testing is tcalculate > ttabe i.e 3.459 > 1.66 with significance standard α = 0.05 and dk = 66. It means that Ha was accepted, so it may conclude that there is a difference of learning achievement between Enhancement Learning Model of Student’s Thinking Ability with Conventional Learning Model for Force and Newton Laws on Grade VIII SMP Negeri 2 Gebang Annual Year 2011/2012.

Different Modes of Digital Learning Object Use in School Settings: Do We Design for Individual or Collaborative Learning?

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Akpinar, Yavuz

2014-01-01

The aim of the studies reported in this paper is to gain classroom based empirical evidence on the learning effectiveness of learning objects used in two types of study settings: Collaborative and individual. A total of 127 seventh and ninth grade students participated in the experiments. They were assigned into one of the study modes and worked…

The Development of the Assessment for Learning Model of Mathematics for Rajamangala University of Technology Rattanakosin

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Wannaree Pansiri

2016-12-01

Full Text Available The objectives of this research were 1 to develop the assessment for learning model of Mathematics for Rajamangala University 2 to study the effectivness of assessment for learning model of Mathematics for Rajamagala University of Technology Rattanakosin. The research target group consisted of 72 students from 3 classes and 3 General Mathematics teachers. The data was gathered from observation, worksheets, achievement test and skill of assessment for learning, questionnaire of the assessment for learning model of Mathematics. The statistics that used in this research were Frequency, Percentage, Mean, Standard Deviation, and Growth Score. The results of this research were 1. The assessment of learning model of Mathematics for Rajamangala University of Technology Rattanakosin consisted of 3 components ; 1. Pre-assessment which consisted of 4 activities ; a Preparation b Teacher development c Design and creation the assessment plan and instrument for assessment and d Creation of the learning experience plan 2. The component for assessment process consisted of 4 steps which were a Identifying the learning objectives and criteria b Identifying the learning experience plan and assessment follow the plan c Learning reflection and giving feedback and d Learner development based on information and improve instruction and 3. Giving feedback component. 2. The effective of assessment for learning model found that most students had good score in concentration, honest, responsibilities, group work, task presentation, worksheets, and doing exercises. The development knowledge of learning and knowledge and skill of assessment for learning of lecturers were fairly good. The opinion to the assessment for learning of learners and assessment for learning model of Mathematics of teachers found that was in a good level.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

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Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

Directory of Open Access Journals (Sweden)

Monica Villaverde

2015-11-01

Full Text Available The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

An Achievement Degree Analysis Approach to Identifying Learning Problems in Object-Oriented Programming

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Allinjawi, Arwa A.; Al-Nuaim, Hana A.; Krause, Paul

2014-01-01

Students often face difficulties while learning object-oriented programming (OOP) concepts. Many papers have presented various assessment methods for diagnosing learning problems to improve the teaching of programming in computer science (CS) higher education. The research presented in this article illustrates that although max-min composition is…

Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding.

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Foley, Nicholas C; Grossberg, Stephen; Mingolla, Ennio

2012-08-01

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how "attentional shrouds" are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of

Multi-documents summarization based on clustering of learning object using hierarchical clustering

Science.gov (United States)

Mustamiin, M.; Budi, I.; Santoso, H. B.

2018-03-01

The Open Educational Resources (OER) is a portal of teaching, learning and research resources that is available in public domain and freely accessible. Learning contents or Learning Objects (LO) are granular and can be reused for constructing new learning materials. LO ontology-based searching techniques can be used to search for LO in the Indonesia OER. In this research, LO from search results are used as an ingredient to create new learning materials according to the topic searched by users. Summarizing-based grouping of LO use Hierarchical Agglomerative Clustering (HAC) with the dependency context to the user’s query which has an average value F-Measure of 0.487, while summarizing by K-Means F-Measure only has an average value of 0.336.

Object-oriented biomedical system modelling--the language.

Science.gov (United States)

Hakman, M; Groth, T

1999-11-01

The paper describes a new object-oriented biomedical continuous system modelling language (OOBSML). It is fully object-oriented and supports model inheritance, encapsulation, and model component instantiation and behaviour polymorphism. Besides the traditional differential and algebraic equation expressions the language includes also formal expressions for documenting models and defining model quantity types and quantity units. It supports explicit definition of model input-, output- and state quantities, model components and component connections. The OOBSML model compiler produces self-contained, independent, executable model components that can be instantiated and used within other OOBSML models and/or stored within model and model component libraries. In this way complex models can be structured as multilevel, multi-component model hierarchies. Technically the model components produced by the OOBSML compiler are executable computer code objects based on distributed object and object request broker technology. This paper includes both the language tutorial and the formal language syntax and semantic description.

RuleML-Based Learning Object Interoperability on the Semantic Web

Science.gov (United States)

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

Experiences with Reusable E-Learning Objects: From Theory to Practice.

Science.gov (United States)

Muzio, Jeanette A.; Heins, Tanya; Mundell, Roger

2002-01-01

Explains reusable electronic learning objects (ELOs) that are stored in a database and discusses the practical application of creating and reusing ELOs at Royal Roads University (Canada). Highlights include ELOs and the instructional design of online courses; and examples of using templates to develop interactive ELOs. (Author/LRW)

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

A Model for Concurrent Objects

DEFF Research Database (Denmark)

Sørensen, Morten U.

1996-01-01

We present a model for concurrent objects where obejcts interact by taking part in common events that are closely matched to form call-response pairs, resulting in resulting in rendez-vous like communications. Objects are built from primitive objects by parallel composition, encapsulation...

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

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Jannis Born

Full Text Available A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior

Catalogue of Interactive Learning Objectives to improve an Integrated Medical and Dental Curriculum.

Science.gov (United States)

Mahmoodi, Benjamin; Sagheb, K; Sagheb, Ka; Schulz, P; Willershausen, B; Al-Nawas, B; Walter, C

2016-12-01

Online learning media are increasingly being incorporated into medical and dental education. However, the coordination between obligatory and facultative teaching domains still remains unsatisfying. The Catalogue of Interactive Learning Objectives of the University Clinic of Mainz (ILKUM), aims to offer knowledge transfer for students while being mindful of their individual qualifications. Its hierarchical structure is designed according to the Association for Dental Education in Europe (ADEE) levels of competence. The ILKUM was designed to establish a stronger interconnection between already existing and prospective learning strategies. All contents are linked to the current lectures as well as to e-learning modules, e.g., clinical case studies and OR videos. Students can conduct self-examinations regarding specific learning objectives. Since 2007, ILKUM has been developed and analyzed regarding its acceptance among dental students. These improved e-learning techniques foster time and location-independent access to study materials and allow an estimation of the knowledge achieved by students. Surveys of our students clearly show a large demand for upgrading ILKUM content (89%; n = 172) with integrated self-testing (89%; n = 174). In parallel to the advancement of our e-learning offering, a portion of internet-based learning is constantly rising among students. The broad acceptance and demand for the development of ILKUM show its potential. Moreover, ILKUM grants fast, topic-oriented querying of learning content without time and locale limitations as well as direct determination of the individually needed knowledge conditions. The long-term goal of the ILKUM project is to be a sustainable, important additional modality of teaching and training for dental and medical students.

The Implementation of Medical Informatics in the National Competence Based Catalogue of Learning Objectives for Undergraduate Medical Education (NKLM).

Science.gov (United States)

Behrends, Marianne; Steffens, Sandra; Marschollek, Michael

2017-01-01

The National Competence Based Catalogue of Learning Objectives for Undergraduate Medical Education (NKLM) describes medical skills and attitudes without being ordered by subjects or organs. Thus, the NKLM enables systematic curriculum mapping and supports curricular transparency. In this paper we describe where learning objectives related to Medical Informatics (MI) in Hannover coincide with other subjects and where they are taught exclusively in MI. An instance of the web-based MERLIN-database was used for the mapping process. In total 52 learning objectives overlapping with 38 other subjects could be allocated to MI. No overlap exists for six learning objectives describing explicitly topics of information technology or data management for scientific research. Most of the overlap was found for learning objectives relating to documentation and aspects of data privacy. The identification of numerous shared learning objectives with other subjects does not mean that other subjects teach the same content as MI. Identifying common learning objectives rather opens up the possibility for teaching cooperations which could lead to an important exchange and hopefully an improvement in medical education. Mapping of a whole medical curriculum offers the opportunity to identify common ground between MI and other medical subjects. Furthermore, in regard to MI, the interaction with other medical subjects can strengthen its role in medical education.

Quality Assurance in E-Learning: PDPP Evaluation Model and its Application

Directory of Open Access Journals (Sweden)

Weiyuan Zhang

2012-06-01

Full Text Available E-learning has become an increasingly important teaching and learning mode in educational institutions and corporate training. The evaluation of e-learning, however, is essential for the quality assurance of e-learning courses. This paper constructs a four-phase evaluation model for e-learning courses, which includes planning, development, process, and product evaluation, called the PDPP evaluation model. Planning evaluation includes market demand, feasibility, target student group, course objectives, and finance. Development evaluation includes instructional design, course material design, course Web site design, flexibility, student-student interaction, teacher/tutor support, technical support, and assessment. Process evaluation includes technical support, Web site utilization, learning interaction, learning evaluation, learning support, and flexibility. Product evaluation includes student satisfaction, teaching effectiveness, learning effectiveness, and sustainability. Using the PDPP model as a research framework, a purely e-learning course on Research Methods in Distance Education, developed by the School of Professional and Continuing Education at the University of Hong Kong (HKU SPACE and jointly offered with the School of Distance Learning for Medical Education of Peking University (SDLME, PKU, was used as a case study. Sixty students from mainland China, Hong Kong, Macau, and Malaysia were recruited for this course. According to summative evaluation through a student e-learning experience survey, the majority of students were very satisfied/satisfied on all e-learning dimensions of this course. The majority of students thought that the learning effectiveness of this course was equivalent, even better, than face-to-face learning because of cross-border collaborative learning, student-centred learning, sufficient learning support, and learning flexibility. This study shows that a high quality of teaching and learning might be assured by

Communication in Health Professions: A European consensus on inter- and multi-professional learning objectives in German.

Science.gov (United States)

Bachmann, Cadja; Kiessling, Claudia; Härtl, Anja; Haak, Rainer

2016-01-01

Communication is object of increasing attention in the health professions. Teaching communication competencies should already begin in undergraduate education or pre-registration training. The aim of this project was to translate the Health Professions Core Communication Curriculum (HPCCC), an English catalogue of learning objectives, into German to make its content widely accessible in the German-speaking countries. This catalogue lists 61 educational objectives and was agreed on by 121 international communication experts. A European reference framework for inter- and multi-professional curriculum development for communication in the health professions in German-speaking countries should be provided. The German version of the HPCCC was drafted by six academics and went through multiple revisions until consensus was reached. The learning objectives were paired with appropriate teaching and assessment tools drawn from the database of the teaching Committee of the European Association for Communication Health Care (tEACH). The HPCCC learning objectives are now available in German and can be applied for curriculum planning and development in the different German-speaking health professions, the educational objectives can also be used for inter-professional purposes. Examples for teaching methods and assessment tools are given for using and implementing the objectives. The German version of the HPCCC with learning objectives for communication in health professions can contribute significantly to inter- and multi-professional curriculum development in the health care professions in the German-speaking countries. Examples for teaching methods and assessment tools from the materials compiled by tEACH supplement the curricular content and provide suggestions for practical implementation of the learning objectives in teaching and assessment. The relevance of the German HPCCC to the processes of curriculum development for the various health professions and inter

Small Schools Student Learning Objectives, 9-12: Mathematics, Reading, Reading in the Content Areas, Language Arts.

Science.gov (United States)

Nelson, JoAnne, Ed.; Hartl, David, Ed.

Designed by Washington curriculum specialists and secondary teachers to assist teachers in small schools with the improvement of curriculum and instruction and to aid smaller districts lacking curriculum personnel to comply with Washington's Student Learning Objectives Law, this handbook contains learning objectives in the areas of language arts,…

Development Of Entrepreneur Learning Model Based On Problem Based Learning To Increase Competency Independence And Creativity Students Of Industrial Engineering

Directory of Open Access Journals (Sweden)

Leola Dewiyani

2017-10-01

Full Text Available Currently it is undeniable that the competition to get a job is very tight and of course universities have an important role in printing human resources that can compete globally not least with the Department of Industrial Engineering Faculty of Engineering Muhammadiyah University of Jakarta FT UMJ. Problems that occur is based on the analysis obtained from the track record of graduates researchers found that 60 percent of students of Industrial Engineering FT UMJ work not in accordance with the level of education owned so financially their income is still below the standard. This study aims to improve the competence of students of Industrial Engineering Department FT UMJ in entrepreneurship courses especially through the development of Problem Based Learning based learning model. Specific targets of this research were conducted with the aim to identify and analyze the need to implement learning model based on Problem Based Learning Entrepreneurship and to design and develop the model of entrepreneurship based on Problem Based Learning to improve the competence independence and creativity of Industrial Engineering students of FT UMJ in Entrepreneurship course. To achieve the above objectives this research uses research and development R amp D method. The product produced in this research is the detail of learning model of entrepreneurial model based on Problem Based Learning entrepreneurship model based on Problem Based Learning and international journals

A Meta-Relational Approach for the Definition and Management of Hybrid Learning Objects

Science.gov (United States)

Navarro, Antonio; Fernandez-Pampillon, Ana Ma.; Fernandez-Chamizo, Carmen; Fernandez-Valmayor, Alfredo

2013-01-01

Electronic learning objects (LOs) are commonly conceived of as digital units of information used for teaching and learning. To facilitate their classification for pedagogical planning and retrieval purposes, LOs are complemented with metadata (e.g., the author). These metadata are usually restricted by a set of predetermined tags to which the…

Database functionality for learning objects

NARCIS (Netherlands)

Sessink, O.D.T.; Beeftink, H.H.; Hartog, R.J.M.

2005-01-01

The development of student-activating digital learning material in six research projects revealed several shortcomings in the current learning management systems. Once the SCORM 2004 and the IMS Sharable State Persistence specifications are implemented in learning management systems, some of these

The effectiveness of flipped classroom learning model in secondary physics classroom setting

Science.gov (United States)

Prasetyo, B. D.; Suprapto, N.; Pudyastomo, R. N.

2018-03-01

The research aimed to describe the effectiveness of flipped classroom learning model on secondary physics classroom setting during Fall semester of 2017. The research object was Secondary 3 Physics group of Singapore School Kelapa Gading. This research was initiated by giving a pre-test, followed by treatment setting of the flipped classroom learning model. By the end of the learning process, the pupils were given a post-test and questionnaire to figure out pupils' response to the flipped classroom learning model. Based on the data analysis, 89% of pupils had passed the minimum criteria of standardization. The increment level in the students' mark was analysed by normalized n-gain formula, obtaining a normalized n-gain score of 0.4 which fulfil medium category range. Obtains from the questionnaire distributed to the students that 93% of students become more motivated to study physics and 89% of students were very happy to carry on hands-on activity based on the flipped classroom learning model. Those three aspects were used to generate a conclusion that applying flipped classroom learning model in Secondary Physics Classroom setting is effectively applicable.

Assuring the Quality of Agricultural Learning Repositories: Issues for the Learning Object Metadata Creation Process of the CGIAR

Science.gov (United States)

Zschocke, Thomas; Beniest, Jan

The Consultative Group on International Agricultural Re- search (CGIAR) has established a digital repository to share its teaching and learning resources along with descriptive educational information based on the IEEE Learning Object Metadata (LOM) standard. As a critical component of any digital repository, quality metadata are critical not only to enable users to find more easily the resources they require, but also for the operation and interoperability of the repository itself. Studies show that repositories have difficulties in obtaining good quality metadata from their contributors, especially when this process involves many different stakeholders as is the case with the CGIAR as an international organization. To address this issue the CGIAR began investigating the Open ECBCheck as well as the ISO/IEC 19796-1 standard to establish quality protocols for its training. The paper highlights the implications and challenges posed by strengthening the metadata creation workflow for disseminating learning objects of the CGIAR.

Evaluating the Use of Learning Objects for Improving Calculus Readiness

Science.gov (United States)

Kay, Robin; Kletskin, Ilona

2010-01-01

Pre-calculus concepts such as working with functions and solving equations are essential for students to explore limits, rates of change, and integrals. Yet many students have a weak understanding of these key concepts which impedes performance in their first year university Calculus course. A series of online learning objects was developed to…

Learning versus correct models: influence of model type on the learning of a free-weight squat lift.

Science.gov (United States)

McCullagh, P; Meyer, K N

1997-03-01

It has been assumed that demonstrating the correct movement is the best way to impart task-relevant information. However, empirical verification with simple laboratory skills has shown that using a learning model (showing an individual in the process of acquiring the skill to be learned) may accelerate skill acquisition and increase retention more than using a correct model. The purpose of the present study was to compare the effectiveness of viewing correct versus learning models on the acquisition of a sport skill (free-weight squat lift). Forty female participants were assigned to four learning conditions: physical practice receiving feedback, learning model with model feedback, correct model with model feedback, and learning model without model feedback. Results indicated that viewing either a correct or learning model was equally effective in learning correct form in the squat lift.

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences

Science.gov (United States)

2016-01-01

Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. PMID:27466440

Machine learning models in breast cancer survival prediction.

Science.gov (United States)

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

Rapid Object Detection Systems, Utilising Deep Learning and Unmanned Aerial Systems (uas) for Civil Engineering Applications

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Griffiths, D.; Boehm, J.

2018-05-01

With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2 segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel onestage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3 %, 83.1 % and 75.6 % of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.

Employing Machine-Learning Methods to Study Young Stellar Objects

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Moore, Nicholas

2018-01-01

Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

Social interaction facilitates word learning in preverbal infants: Word-object mapping and word segmentation.

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Hakuno, Yoko; Omori, Takahide; Yamamoto, Jun-Ichi; Minagawa, Yasuyo

2017-08-01

In natural settings, infants learn spoken language with the aid of a caregiver who explicitly provides social signals. Although previous studies have demonstrated that young infants are sensitive to these signals that facilitate language development, the impact of real-life interactions on early word segmentation and word-object mapping remains elusive. We tested whether infants aged 5-6 months and 9-10 months could segment a word from continuous speech and acquire a word-object relation in an ecologically valid setting. In Experiment 1, infants were exposed to a live tutor, while in Experiment 2, another group of infants were exposed to a televised tutor. Results indicate that both younger and older infants were capable of segmenting a word and learning a word-object association only when the stimuli were derived from a live tutor in a natural manner, suggesting that real-life interaction enhances the learning of spoken words in preverbal infants. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning to Act: Qualitative Learning of Deterministic Action Models

DEFF Research Database (Denmark)

Bolander, Thomas; Gierasimczuk, Nina

2017-01-01

In this article we study learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their dynamic epistemic logic representations as action...... in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, i.e. learning via update......, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and unconditional deterministic action models. We propose update learning mechanisms for the afore mentioned classes of actions and analyse...

MODELI: An Emotion-Based Software Engineering Methodology for the Development of Digital Learning Objects for the Preservation of the Mixtec Language

Directory of Open Access Journals (Sweden)

Olivia Allende-Hernández

2015-07-01

Full Text Available In this paper, a methodology termed MODELI (methodology for the design of educational digital objects for indigenous languages is presented for the development of digital learning objects (DLOs for the Mixtec language, which is an indigenous Mexican language. MODELI is based on the spiral model of software development and integrates three important aspects for the analysis and design of DLOs: pedagogical, affective-emotional and technological-functional. The premise of MODELI is that the emotional aspect with the inclusion of cultural factors has an important effect on the learning motivation of indigenous users when interacting with the DLO. Principles of the visual, auditory (or aural, read/write, kinesthetic (VARK model and Kansei engineering were considered for the inclusion of the pedagogical, emotional and technological-functional aspects within the spiral model for the development of MODELI. The methodology was validated with the development of a DLO for a previously unknown variant of the Mixtec language. Usability tests of the DLO built with MODELI evidenced an improvement on the learning motivation and the value of cultural identity of indigenous children. These results are important for the preservation of indigenous languages in Mexico, because most of them are partially documented, and there is social rejection of indigenous culture caused by discrimination of ethnic communities.

Learning to Appraise the Quality of Qualitative Research Articles: A Contextualized Learning Object for Constructing Knowledge

Science.gov (United States)

Chenail, Ronald J.

2011-01-01

Helping beginning qualitative researchers critically appraise qualitative research articles is a common learning objective for introductory methodology courses. To aid students in achieving competency in appraising the quality of qualitative research articles, a multi-part activity incorporating the Critical Appraisal Skills Programme's (CASP)…

#gottacatchemall: Exploring Pokemon Go in Search of Learning Enhancement Objects

Science.gov (United States)

Cacchione, Annamaria; Procter-Legg, Emma; Petersen, Sobah Abbas

2017-01-01

The Augmented Reality Game, Pokemon Go, took the world by storm in the summer of 2016. City landscapes were decorated with amusing, colourful objects called Pokemon, and the holiday activities were enhanced by catching these wonderful creatures. In light of this, it is inevitable for mobile language learning researchers to reflect on the impact of…

Using Active Learning for Speeding up Calibration in Simulation Models.

Science.gov (United States)

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

A workflow learning model to improve geovisual analytics utility.

Science.gov (United States)

Roth, Robert E; Maceachren, Alan M; McCabe, Craig A

2009-01-01

INTRODUCTION: This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. OBJECTIVES: The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. METHODOLOGY: The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on

EFFECT OF INQUIRY LEARNING MODEL AND MOTIVATION ON PHYSICS OUTCOMES LEARNING STUDENTS

Directory of Open Access Journals (Sweden)

Dahlia Megawati Pardede

2016-06-01

Full Text Available The purposes of the research are: (a to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a there are differences in physical students learning outcomes are taught by Inquiry Training models and conventional models. (b learning outcomes of students who are taught by Inquiry Learning Model Training better than student learning outcomes are taught with conventional model. (c there is a difference in student's learning outcomes that have high motivation and low motivation. (d Student learning outcomes that have a high motivation better than student learning outcomes than have a low motivation. (e there is interaction between learning and motivation to student learning outcomes. Learning outcomes of students who are taught by the model is influenced also by the motivation, while learning outcomes of students who are taught with conventional models are not affected by motivation.

The Role of Reusable Learning Objects in Occupational Therapy Entry-Level Education

Directory of Open Access Journals (Sweden)

Bryan M. Gee

2014-10-01

Full Text Available Out of early research, Cisco Systems (1999 have built an impressive foundation that advocates for reusable learning objects (RLOs. As the need for online methods for delivering both formal and informal educational content has increased, the prospect of greater influence through carefully constructed RLOs has grown. RLOs are any digital resource that can be used and reused to enhance online learning. RLOs typically are small, discrete, self-contained digital objects that may be sequenced, combined, and used within a variety of instructional activities. RLOs have been implemented in nursing, pharmacy, and physician assistant programs. However, there is a lack of literature regarding RLOs in occupational therapy education. An attitudinal survey was administered to occupational therapy students after they had used an RLO focused on goal writing. Student preferences toward RLO content, instructional design, and eLearning were generally positive. Nearly three-quarters of the students who responded to the survey indicated that the RLO presented was beneficial. All respondents noted that they would use the RLO for future occupational therapy courses. It is argued that incorporating RLOs offers a cost-effective, efficient learning tool, and also adds credibility to the given curriculum program as being innovative with instructing occupational-therapy related concepts.

Behavioral Objectives, the Cult of Efficiency, and Foreign Language Learning: Are They Compatible?

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Tumposky, Nancy Rennau

1984-01-01

Surveys the literature regarding the use of behavioral objectives in education and in foreign language instruction and examines the roots of the behavioral objectives movement in behaviorist psychology and the scientific management movement of the 1920s. Discusses implications for foreign and second language learning and provides suggestions for…

Real-time Pipeline for Object Modeling and Grasping Pose Selection via Superquadric Functions

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Giulia Vezzani

2017-11-01

Full Text Available This work provides a novel real-time pipeline for modeling and grasping of unknown objects with a humanoid robot. Such a problem is of great interest for the robotic community, since conventional approaches fail when the shape, dimension, or pose of the objects are missing. Our approach reconstructs in real-time a model for the object under consideration and represents the robot hand both with proper and mathematically usable models, i.e., superquadric functions. The volume graspable by the hand is represented by an ellipsoid and is defined a priori, because the shape of the hand is known in advance. The superquadric representing the object is obtained in real-time from partial vision information instead, e.g., one stereo view of the object under consideration, and provides an approximated 3D full model. The optimization problem we formulate for the grasping pose computation is solved online by using the Ipopt software package and, thus, does not require off-line computation or learning. Even though our approach is for a generic humanoid robot, we developed a complete software architecture for executing this approach on the iCub humanoid robot. Together with that, we also provide a tutorial on how to use this framework. We believe that our work, together with the available code, is of a strong utility for the iCub community for three main reasons: object modeling and grasping are relevant problems for the robotic community, our code can be easily applied on every iCub, and the modular structure of our framework easily allows extensions and communications with external code.

Video Cases in Teacher Education: A review study on intended and achieved learning objectives by video cases

NARCIS (Netherlands)

Geerts, Walter; Van der Werff, Anne; Hummel, Hans; Van Geert, Paul

2014-01-01

This literature review focuses on the use of video cases in the education of preservice teachers as a means of achieving higher order learning objectives that are necessary for gaining situated knowledge. An overview of both intended and achieved learning objectives in relevant studies involving

Object instance recognition using motion cues and instance specific appearance models

Science.gov (United States)

Schumann, Arne

2014-03-01

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

Creative Generation of 3D Objects with Deep Learning and Innovation Engines

DEFF Research Database (Denmark)

Lehman, Joel Anthony; Risi, Sebastian; Clune, Jeff

2016-01-01

Advances in supervised learning with deep neural networks have enabled robust classification in many real world domains. An interesting question is if such advances can also be leveraged effectively for computational creativity. One insight is that because evolutionary algorithms are free from st...... creativity. The results of this automated process are interesting and recognizable 3D-printable objects, demonstrating the creative potential for combining evolutionary computation and deep learning in this way....

Learning of perceptual grouping for object segmentation on RGB-D data.

Science.gov (United States)

Richtsfeld, Andreas; Mörwald, Thomas; Prankl, Johann; Zillich, Michael; Vincze, Markus

2014-01-01

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.

Associative vocabulary learning: development and testing of two paradigms for the (re-) acquisition of action- and object-related words.

Science.gov (United States)

Freundlieb, Nils; Ridder, Volker; Dobel, Christian; Enriquez-Geppert, Stefanie; Baumgaertner, Annette; Zwitserlood, Pienie; Gerloff, Christian; Hummel, Friedhelm C; Liuzzi, Gianpiero

2012-01-01

Despite a growing number of studies, the neurophysiology of adult vocabulary acquisition is still poorly understood. One reason is that paradigms that can easily be combined with neuroscientfic methods are rare. Here, we tested the efficiency of two paradigms for vocabulary (re-) acquisition, and compared the learning of novel words for actions and objects. Cortical networks involved in adult native-language word processing are widespread, with differences postulated between words for objects and actions. Words and what they stand for are supposed to be grounded in perceptual and sensorimotor brain circuits depending on their meaning. If there are specific brain representations for different word categories, we hypothesized behavioural differences in the learning of action-related and object-related words. Paradigm A, with the learning of novel words for body-related actions spread out over a number of days, revealed fast learning of these new action words, and stable retention up to 4 weeks after training. The single-session Paradigm B employed objects and actions. Performance during acquisition did not differ between action-related and object-related words (time*word category: p = 0.01), but the translation rate was clearly better for object-related (79%) than for action-related words (53%, p = 0.002). Both paradigms yielded robust associative learning of novel action-related words, as previously demonstrated for object-related words. Translation success differed for action- and object-related words, which may indicate different neural mechanisms. The paradigms tested here are well suited to investigate such differences with neuroscientific means. Given the stable retention and minimal requirements for conscious effort, these learning paradigms are promising for vocabulary re-learning in brain-lesioned people. In combination with neuroimaging, neuro-stimulation or pharmacological intervention, they may well advance the understanding of language learning

The Game Enhanced Learning Model

DEFF Research Database (Denmark)

Reng, Lars; Schoenau-Fog, Henrik

2016-01-01

will describe the levels of the model, which is based on our experience in teaching professional game development at university level. Furthermore, we have been using the model to inspire numerous educators to improve their students’ motivation and skills. The model presents various game-based learning...... activities, and depicts their required planning and expected outcome through eight levels. At its lower levels, the model contains the possibilities of using stand-alone analogue and digital games as teachers, utilizing games as a facilitator of learning activities, exploiting gamification and motivating......In this paper, we will introduce the Game Enhanced learning Model (GEM), which describes a range of gameoriented learning activities. The model is intended to give an overview of the possibilities of game-based learning in general and all the way up to purposive game productions. In the paper, we...

Practicing doctors' perceptions on new learning objectives for Vietnamese medical schools

NARCIS (Netherlands)

Hoat, L; Dung, DV; Wright, E.P.

2008-01-01

Background. As part of the process to develop more community-oriented medical teaching in Vietnam, eight medical schools prepared a set of standard learning objectives with attention to the needs of a doctor working with the community. Because they were prepared based on government documents and the

Designing Learning Object Repositories as Systems for Managing Educational Communities Knowledge

Science.gov (United States)

Sampson, Demetrios G.; Zervas, Panagiotis

2013-01-01

Over the past years, a number of international initiatives that recognize the importance of sharing and reusing digital educational resources among educational communities through the use of Learning Object Repositories (LORs) have emerged. Typically, these initiatives focus on collecting digital educational resources that are offered by their…

Object tracking using active appearance models

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille

2001-01-01

This paper demonstrates that (near) real-time object tracking can be accomplished by the deformable template model; the Active Appearance Model (AAM) using only low-cost consumer electronics such as a PC and a web-camera. Successful object tracking of perspective, rotational and translational...

iLOG: A Framework for Automatic Annotation of Learning Objects with Empirical Usage Metadata

Science.gov (United States)

Miller, L. D.; Soh, Leen-Kiat; Samal, Ashok; Nugent, Gwen

2012-01-01

Learning objects (LOs) are digital or non-digital entities used for learning, education or training commonly stored in repositories searchable by their associated metadata. Unfortunately, based on the current standards, such metadata is often missing or incorrectly entered making search difficult or impossible. In this paper, we investigate…

Curriculum development for a national cardiotocography education program: a Delphi survey to obtain consensus on learning objectives.

Science.gov (United States)

Thellesen, Line; Hedegaard, Morten; Bergholt, Thomas; Colov, Nina P; Hoegh, Stinne; Sorensen, Jette L

2015-08-01

To define learning objectives for a national cardiotocography (CTG) education program based on expert consensus. A three-round Delphi survey. One midwife and one obstetrician from each maternity unit in Denmark were appointed based on CTG teaching experience and clinical obstetric experience. Following national and international guidelines, the research group determined six topics as important when using CTG: fetal physiology, equipment, indication, interpretation, clinical management, and communication/responsibility. In the first Delphi round, participants listed one to five learning objectives within the predefined topics. Responses were analyzed by a directed approach to content analysis. Phrasing was modified in accordance with Bloom's taxonomy. In the second and third Delphi rounds, participants rated each objective on a five-point relevance scale. Consensus was predefined as objectives with a mean rating value of ≥ 3. A prioritized list of CTG learning objectives. A total of 42 midwives and obstetricians from 21 maternity units were invited to participate, of whom 26 completed all three Delphi rounds, representing 18 maternity units. The final prioritized list included 40 objectives. The highest ranked objectives emphasized CTG interpretation and clinical management. The lowest ranked objectives emphasized fetal physiology. Mean ratings of relevance ranged from 3.15 to 5.00. National consensus on CTG learning objectives was achieved using the Delphi methodology. This was an initial step in developing a valid CTG education program. A prioritized list of objectives will clarify which topics to emphasize in a CTG education program. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

Student Modeling and Machine Learning

OpenAIRE

Sison , Raymund; Shimura , Masamichi

1998-01-01

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

Evaluation of a digital learning object (DLO) to support the learning process in radiographic dental diagnosis.

Science.gov (United States)

Busanello, F H; da Silveira, P F; Liedke, G S; Arús, N A; Vizzotto, M B; Silveira, H E D; Silveira, H L D

2015-11-01

Studies have shown that inappropriate therapeutic strategies may be adopted if crown and root changes are misdiagnosed, potentially leading to undesirable consequences. Therefore, the aim of this study was to evaluate a digital learning object, developed to improve skills in diagnosing radiographic dental changes. The object was developed using the Visual Basic Application (VBA) software and evaluated by 62 undergraduate students (male: 24 and female: 38) taking an imaging diagnosis course. Participants were divided in two groups: test group, which used the object and control group, which attended conventional classes. After 3 weeks, students answered a 10-question test and took a practice test to diagnose 20 changes in periapical radiographs. The results show that test group performed better that control group in both tests, with statistically significant difference (P = 0.004 and 0.003, respectively). In overall, female students were better than male students. Specific aspects of object usability were assessed using a structured questionnaire based on the System Usability Scale (SUS), with a score of 90.5 and 81.6 by male and female students, respectively. The results obtained in this study suggest that students who used the DLO performed better than those who used conventional methods. This suggests that the DLO may be a useful teaching tool for dentistry undergraduates, on distance learning courses and as a complementary tool in face-to-face teaching. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

E-learning teaches attendings "how to" objectively assess pediatric urology trainees' surgery skills for orchiopexy.

Science.gov (United States)

Fernandez, Nicolas; Maizels, Max; Farhat, Walid; Smith, Edwin; Liu, Dennis; Chua, Michael; Bhanji, Yasin

2018-04-01

Established methods to train pediatric urology surgery by residency training programs require updating in response to administrative changes such as new, reduced trainee duty hours. Therefore, new objective methods must be developed to teach trainees. We approached this need by creating e-learning to teach attendings objective assessment of trainee skills using the Zwisch scale, an established assessment tool. The aim of this study was to identify whether or not e-learning is an appropriate platform for effective teaching of this assessment tool, by assessing inter-rater correlation of assessments made by the attendings after participation in the e-learning. Pediatric orchiopexy was used as the index case. An e-learning tool was created to teach attending surgeons objective assessment of trainees' surgical skills. First, e-learning content was created which showed the assessment method videotape of resident surgery done in the operating room. Next, attendings were enrolled to e-learn this method. Finally, the ability of enrollees to assess resident surgery skill performance was tested. Namely, test video was made showing a trainee performing inguinal orchiopexy. All enrollees viewed the same online videos. Assessments of surgical skills (Zwisch scale) were entered into an online survey. Data were analyzed by intercorrelation coefficient kappa analysis (strong correlation was ICC ≥ 0.7). A total of 11 attendings were enrolled. All accessed the online learning and then made assessments of surgical skills trainees showed on videotapes. The e-learning comprised three modules: 1. "Core concepts," in which users learned the assessment tool methods; 2. "Learn to assess," in which users learned how to assess by watching video clips, explaining the assessment method; and 3. "Test," in which users tested their skill at making assessments by watching video clips and then actively inputting their ratings of surgical and global skills as viewed in the video clips (Figure

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

Science.gov (United States)

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Learning Grasp Affordance Densities

DEFF Research Database (Denmark)

Detry, Renaud; Kraft, Dirk; Kroemer, Oliver

2011-01-01

and relies on kernel density estimation to provide a continuous model. Grasp densities are learned and refined from exploration, by letting a robot “play” with an object in a sequence of graspand-drop actions: The robot uses visual cues to generate a set of grasp hypotheses; it then executes......We address the issue of learning and representing object grasp affordance models. We model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability. The underlying function representation is nonparametric...... these and records their outcomes. When a satisfactory number of grasp data is available, an importance-sampling algorithm turns these into a grasp density. We evaluate our method in a largely autonomous learning experiment run on three objects of distinct shapes. The experiment shows how learning increases success...

Comparison of Chemistry Learning Outcomes with Inquiry Learning Model and Learning Cycle 5E in Material Solubility and Solubility Multiplication Results

Directory of Open Access Journals (Sweden)

Nur Indah Firdausi

2015-04-01

Full Text Available Perbandingan Hasil Belajar Kimia dengan Model Pembelajaran Inquiry dan Learning Cycle 5E pada Materi Kelarutan dan Hasil Kali Kelarutan   Abstract: This research is aimed to compare the effectiveness between inquiry and LC 5E in solubility equilibria and the solubility product for students with different prior knowledge. The effectiveness of both learning models is measured from students learning outcome. This quasi experimental research uses factorial2x2 with posttest only design. Research samples are chosen using cluster random sampling. They are two classes of XI IPA SMAN 1 Kepanjen in the 2012/2013 academic year which consist of 31 students in each class. Cognitive learning outcome is measured by test items consist of four objective items and nine subjective items. Technique of data analysis in this research is two way ANOVA. Research results show that: (1 cognitive learning outcome and higher cognitive learning outcome of students in inquiry class is higher than students in LC 5E class; (2 cognitive learning outcome and higher cognitive learning outcome of students who have upper prior knowledge is higher than students who have lower prior knowledge in both inquiry and LC 5E. Key Words: learning outcome, inquiry, learning cycle 5E, solubility equilibria and the solubility product   Abstrak: Penelitian ini bertujuan membandingkan keefektifan model inquiry dan LC 5E pada materi kelarutan dan hasil kali kelarutan untuk siswa dengan kemampuan awal berbeda. Keefektifan model pembelajaran dilihat dari hasil belajar kognitif siswa. Penelitian ini menggunakan rancangan eksperimen semu dengan desain faktorial 2x2. Subjek penelitian dipilih secara cluster random sampling yaitu dua kelas XI IPA SMAN 1 Kepanjen dengan jumlah masing-masing kelas sebanyak 31 siswa. Instrumen perlakuan yang digunakan adalah silabus dan RPP sedangkan instrumen pengukuran berupa soal tes terdiri dari empat soal objektif dan sembilan soal subjektif. Teknik analisis data

Toward a Dexter-based model for open hypermedia: Unifying embedded references and link objects

DEFF Research Database (Denmark)

Grønbæk, Kaj; Trigg, Randall Hagner

1996-01-01

Nominated for the Doug Engelbart best paper award. This paper discusses experiences and lessons learned from the design of an open hypermedia system, one that integrates applications and data not ''owned'' by the hypermedia. The Dexter Hypertext Reference Model was used as the basis for the design....... Though our experiences were generally positive, we found the model constraining in certain ways and underdeveloped in others. For instance, Dexter argues against dangling links, but we found several situations where permitting and supporting dangling links was advisable. In Dexter, the data objects...

Intuitive modeling of vaporish objects

International Nuclear Information System (INIS)

Sokolov, Dmitry; Gentil, Christian

2015-01-01

Attempts to model gases in computer graphics started in the late 1970s. Since that time, there have been many approaches developed. In this paper we present a non-physical method allowing to create vaporish objects like clouds or smoky characters. The idea is to create a few sketches describing the rough shape of the final vaporish object. These sketches will be used as condensation sets of Iterated Function Systems, providing intuitive control over the object. The advantages of the new method are: simplicity, good control of resulting shapes and ease of eventual object animation.

Gamification in online education: proposal for a participatory learning model

Directory of Open Access Journals (Sweden)

Fabiana Bigão Silva

2017-09-01

Full Text Available Empirical studies have suggested limitations on the form of application of gamification mechanics in the context of online education. These mechanics have been applied without reference to a theoretical model dedicated to this type of education. The objective of the paper is to propose a model for a gamified platform for online education that contributes to a more participatory learning, taking into account the different student profiles. Based on literature review about approaches to gamification systems design, a set of steps was followed in order to develop a generic model for a framework dedicated to online education. The model proposed is based on the Educational Gamification Design Principles proposed by Dicheva et al. (2015. The model may contribute to the promotion of participatory learning, taking into account the different student profiles. The results of such evaluation will be published in the future.

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

Learning Object Metadata in a Web-Based Learning Environment

NARCIS (Netherlands)

Avgeriou, Paris; Koutoumanos, Anastasios; Retalis, Symeon; Papaspyrou, Nikolaos

2000-01-01

The plethora and variance of learning resources embedded in modern web-based learning environments require a mechanism to enable their structured administration. This goal can be achieved by defining metadata on them and constructing a system that manages the metadata in the context of the learning

Grounding word learning in space.

Directory of Open Access Journals (Sweden)

Larissa K Samuelson

Full Text Available Humans and objects, and thus social interactions about objects, exist within space. Words direct listeners' attention to specific regions of space. Thus, a strong correspondence exists between where one looks, one's bodily orientation, and what one sees. This leads to further correspondence with what one remembers. Here, we present data suggesting that children use associations between space and objects and space and words to link words and objects--space binds labels to their referents. We tested this claim in four experiments, showing that the spatial consistency of where objects are presented affects children's word learning. Next, we demonstrate that a process model that grounds word learning in the known neural dynamics of spatial attention, spatial memory, and associative learning can capture the suite of results reported here. This model also predicts that space is special, a prediction supported in a fifth experiment that shows children do not use color as a cue to bind words and objects. In a final experiment, we ask whether spatial consistency affects word learning in naturalistic word learning contexts. Children of parents who spontaneously keep objects in a consistent spatial location during naming interactions learn words more effectively. Together, the model and data show that space is a powerful tool that can effectively ground word learning in social contexts.

A Model for Predicting Learning Flow and Achievement in Corporate e-Learning

Science.gov (United States)

Joo, Young Ju; Lim, Kyu Yon; Kim, Su Mi

2012-01-01

The primary objective of this study was to investigate the determinants of learning flow and achievement in corporate online training. Self-efficacy, intrinsic value, and test anxiety were selected as learners' motivational factors, while perceived usefulness and ease of use were also selected as learning environmental factors. Learning flow was…

A System for Automatic Detection of Partially Occluded Objects from Real-World Images

National Research Council Canada - National Science Library

Neskovic, Predrag; Wu, Liang; Cooper, Leon N

2006-01-01

In this work we consider the Bayesian Integrate And Shift (BIAS) model for learning object categories and test its performance on learning and recognizing different object categories from real-world images...

Precise Object Tracking under Deformation

International Nuclear Information System (INIS)

Saad, M.H.

2010-01-01

The precise object tracking is an essential issue in several serious applications such as; robot vision, automated surveillance (civil and military), inspection, biomedical image analysis, video coding, motion segmentation, human-machine interface, visualization, medical imaging, traffic systems, satellite imaging etc. This framework focuses on the precise object tracking under deformation such as scaling, rotation, noise, blurring and change of illumination. This research is a trail to solve these serious problems in visual object tracking by which the quality of the overall system will be improved. Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model learned by the OLS. This framework presents a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors. The presented research work is applied to real video stream and achieved high precession results. xiiiThe precise object tracking is an essential issue in several serious applications such as; robot vision, automated surveillance (civil and military), inspection, biomedical image analysis, video coding, motion segmentation, human-machine interface, visualization, medical imaging, traffic systems, satellite imaging etc. This framework focuses on the precise object tracking under deformation such as scaling, rotation, noise, blurring and change of illumination. This research is a trail to solve these serious problems in visual object tracking by which the quality of the overall system will be improved. Developing a three dimensional (3D) geometrical model to determine the current pose of an object and predict its future location based on FIR model learned by the OLS. This framework presents a robust ranging technique to track a visual target instead of the traditional expensive ranging sensors. The presented research work is applied to real video stream and achieved high

Embedded or linked learning objects: Implications for content development, course design and classroom use

Directory of Open Access Journals (Sweden)

Gail Kopp

2007-06-01

Full Text Available This research explores the idea of embedding and linking to existing content in learning object repositories and investigates teacher-designer use of learning objects within one high school mathematics course in an online school. This qualitative case study supports and extends the learning object literature, and brings forward context-specific examples of issues around repository design, autonomy and self-containment, technical support and granularity. Moreover, these findings have implications for building learning objects and repositories that could better support teachers in their instructional design and pedagogical decision-making. Résumé : La présente recherche étudie la possibilité d’effectuer un emboîtement et d’établir des liens avec le contenu existant dans les référentiels sur les objets d’apprentissage et explore l’utilisation par les enseignants-concepteurs des objets d’apprentissage au sein d’un cours de mathématique du secondaire donné dans une école en ligne. Cette étude de cas qualitative appuie et vise la littérature sur les objets d’apprentissage et met en avant plan des exemples de questions touchant la conception de référentiels, l’autonomie et l’indépendance, le soutien technique et la granularité propres au contexte. De plus, ces conclusions ont des répercussions sur l’élaboration d’objets et de référentiels d’apprentissage qui pourraient mieux appuyer les enseignants dans le cadre de leur conception pédagogique et de leur prise de décision touchant l’enseignement.

Assessing Program Learning Objectives to Improve Undergraduate Physics Education

Science.gov (United States)

Menke, Carrie

2014-03-01

Our physics undergraduate program has five program learning objectives (PLOs) focusing on (1) physical principles, (2) mathematical expertise, (3) experimental technique, (4) communication and teamwork, and (5) research proficiency. One PLO is assessed each year, with the results guiding modifications in our curriculum and future assessment practices; we have just completed our first cycle of assessing all PLOs. Our approach strives to maximize the ease and applicability of our assessment practices while maintaining faculty's flexibility in course design and delivery. Objectives are mapped onto our core curriculum with identified coursework collected as direct evidence. We've utilized mostly descriptive rubrics, applying them at the course and program levels as well as sharing them with the students. This has resulted in more efficient assessment that is also applicable to reaccreditation efforts, higher inter-rater reliability than with other rubric types, and higher quality capstone projects. We've also found that the varied quality of student writing can interfere with our assessment of other objectives. This poster outlines our processes, resources, and how we have used PLO assessment to strengthen our undergraduate program.

Learning About Climate and Atmospheric Models Through Machine Learning

Science.gov (United States)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Metaphoric Modeling of Foreign Language Teaching and Learning, with Special Reference to Teaching Philosophy Statements

Science.gov (United States)

Alghbban, Mohammed I.; Ben Salamh, Sami; Maalej, Zouheir

2017-01-01

The current article investigates teachers' metaphoric modeling of foreign language teaching and learning at the College of Languages and Translation, King Saud University. It makes use of teaching philosophy statements as a corpus. Our objective is to analyze the underlying conceptualizations of teaching/learning, the teachers' perception of the…

Aligning Learning and Talent Development Performance Outcomes with Organizational Objectives: A Proposed Model

Science.gov (United States)

Ware, Iris

2017-01-01

The value proposition for learning and talent development (LTD) is often challenged due to human resources' inability to demonstrate meaningful outcomes in relation to organizational needs and return-on-investment. The primary role of human resources (HR) and the learning and talent development (LTD) function is to produce meaningful outcomes to…

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Credit assignment between body and object probed by an object transportation task.

Science.gov (United States)

Kong, Gaiqing; Zhou, Zhihao; Wang, Qining; Kording, Konrad; Wei, Kunlin

2017-10-17

It has been proposed that learning from movement errors involves a credit assignment problem: did I misestimate properties of the object or those of my body? For example, an overestimate of arm strength and an underestimate of the weight of a coffee cup can both lead to coffee spills. Though previous studies have found signs of simultaneous learning of the object and of the body during object manipulation, there is little behavioral evidence about their quantitative relation. Here we employed a novel weight-transportation task, in which participants lift the first cup filled with liquid while assessing their learning from errors. Specifically, we examined their transfer of learning when switching to a contralateral hand, the second identical cup, or switching both hands and cups. By comparing these transfer behaviors, we found that 25% of the learning was attributed to the object (simply because of the use of the same cup) and 58% of the learning was attributed to the body (simply because of the use of the same hand). The nervous system thus seems to partition the learning of object manipulation between the object and the body.

Libraries in Second Life: New Approaches to Education, Information Sharing, Learning Object Implementation, User Interactions and Collaborations

Directory of Open Access Journals (Sweden)

Susan Smith Nash

2009-10-01

Full Text Available Three-dimensional virtual worlds such as Second Life continue to expand the way they provide information, learning activities, and educational applications. This paper explores the types of learning activities that take place in Second Life and discusses how learning takes place, with a view toward developing effective instructional strategies. As learning objects are being launched in Second Life, new approaches to collaboration, interactivity, and cognition are being developed. Many learning-centered islands appeal to individuals who benefit from interaction with peers and instructors, and who can access learning objects such as information repositories, simulations, and interactive animations. The key advantages that Second Life offers include engaging and meaningful interaction with fellow learners, media-rich learning environments with embedded video, graphics, and interactive quizzes and assessments, an engaging environment for simulations such as virtual labs, and culturally inclusive immersive environments. However, because of the steep learning curve, technical difficulties, and cultural diversity, learners may become frustrated in Second Life. Since Second Life is social learning environment that emphasizes the creation of a self, effective learning requires step-by-step empowerment of that new, constructed self.

Construction and validation of a virtual learning object on intestinal elimination stoma

Directory of Open Access Journals (Sweden)

Cecílio Soares Rodrigues Braga

Full Text Available Objective.To construct and validate a virtual learning object (VLO on intestinal elimination stoma. Methods. Applied, descriptive and quantitative study. In 2014, eight stoma therapists and eight experts in computer science took part of the research. The VLO included four steps: i planning, ii construction of VLO and changes of content; iii development of dynamic, and iv conclusion and analysis. The VLO was inserted into the Moodle virtual learning environment. The ergonomic and pedagogical validation of the VLO was performed. Results. The experts appreciated the VLO satisfactorily, and scored it between good and full agreement. Conclusion. The VLO on intestinal elimination stoma is a tool that can be implemented at undergraduate programs in nursing and continuing education programs for nurses in clinical practice, contributing significantly to improve the theoretical skills necessary for the care of ostomized people safely, with quality and enabling self-care.

Distribution majorization of corner points by reinforcement learning for moving object detection

Science.gov (United States)

Wu, Hao; Yu, Hao; Zhou, Dongxiang; Cheng, Yongqiang

2018-04-01

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

Science.gov (United States)

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

Aspects on Teaching/Learning with Object Oriented Programming for Entry Level Courses of Engineering.

Science.gov (United States)

de Oliveira, Clara Amelia; Conte, Marcos Fernando; Riso, Bernardo Goncalves

This work presents a proposal for Teaching/Learning, on Object Oriented Programming for Entry Level Courses of Engineering and Computer Science, on University. The philosophy of Object Oriented Programming comes as a new pattern of solution for problems, where flexibility and reusability appears over the simple data structure and sequential…

Learning from erroneous models using SCYDynamics

NARCIS (Netherlands)

Mulder, Y.G.; Bollen, Lars; de Jong, Anthonius J.M.

2014-01-01

Dynamic phenomena are common in science education. Students can learn about such system dynamic processes through model based learning activities. This paper describes a study on the effects of a learning from erroneous models approach using the learning environment SCYDynamics. The study compared

Evaluation Models for E-Learning Platform in Riyadh City Universities (RCU with Applied of Geographical Information System (GIS

Directory of Open Access Journals (Sweden)

Abdulaziz I. Alharrah

2014-12-01

Full Text Available E-learning that integrates digital knowledge content, network and information technology has become an emerging learning method. As the e-learning platform approach is becoming an important tool to allow the flexibility and quality requested by such a kind of learning process. There is a new kind of problem faced by organizations consisting in the selection of the most suitable e-learning platform. This paper proposes evaluation model for E-Learning platform in Riyadh City universities (RCU with Applied Geographic Information System (GIS. The E-Learning platform solution selection is a multiple criteria decision-making problem that needs to be addressed objectively taking into consideration the relative weights of the criteria for any organization. We formulate the quoted multi criteria problem as a decision hierarchy to be solved using GIS. AGIS-based evaluation index system and web-based evaluating platform were established. In this paper we will show the general evaluation strategy and some obtained results using our model to evaluate some existing commercial platforms.The results of evaluation model are outlined as follows: Total weights of the proposed framework in management feature is 20.25/25, in collaborative feature is 9.2/10, in adaption learning path is 6.8/10 and in interactive learning object is 5/5. The total weights of all features are 41.25/50. In this study an evaluation model was applied on Riyadh City universities like KSU, IMAMU, NAUSS, YU and FU. Then, the results were compared with each other. The total weighs of KSU was 41. While the total weights of FU, IMAMU, YU and NAUSS was 40, 37, 36 and 32, respectively. Evaluation process shows that the proposed framework satisfied the objectives with applied GIS.

Modeling a terminology-based electronic nursing record system: an object-oriented approach.

Science.gov (United States)

Park, Hyeoun-Ae; Cho, InSook; Byeun, NamSoo

2007-10-01

The aim of this study was to present our perspectives on healthcare information analysis at a conceptual level and the lessons learned from our experience with the development of a terminology-based enterprise electronic nursing record system - which was one of components in an EMR system at a tertiary teaching hospital in Korea - using an object-oriented system analysis and design concept. To ensure a systematic approach and effective collaboration, the department of nursing constituted a system modeling team comprising a project manager, systems analysts, user representatives, an object-oriented methodology expert, and healthcare informaticists (including the authors). A rational unified process (RUP) and the Unified Modeling Language were used as a development process and for modeling notation, respectively. From the scenario and RUP approach, user requirements were formulated into use case sets and the sequence of activities in the scenario was depicted in an activity diagram. The structure of the system was presented in a class diagram. This approach allowed us to identify clearly the structural and behavioral states and important factors of a terminology-based ENR system (e.g., business concerns and system design concerns) according to the viewpoints of both domain and technical experts.

Visual Perceptual Learning and Models.

Science.gov (United States)

Dosher, Barbara; Lu, Zhong-Lin

2017-09-15

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

Constraints on Perceptual Learning: Objects and Dimensions.

Science.gov (United States)

Bedford, Felice L.

1995-01-01

Addresses two questions that may be unique to perceptual learning: What are the circumstances that produce learning? and What is the content of learning? Suggests a critical principle for each question. Provides a discussion of perceptual learning theory, how learning occurs, and what gets learned. Includes a 121-item bibliography. (DR)

Ferromanganese Furnace Modelling Using Object-Oriented Principles

Energy Technology Data Exchange (ETDEWEB)

Wasboe, S.O.

1996-12-31

This doctoral thesis defines an object-oriented framework for aiding unit process modelling and applies it to model high-carbon ferromanganese furnaces. A framework is proposed for aiding modelling of the internal topology and the phenomena taking place inside unit processes. Complex unit processes may consist of a number of zones where different phenomena take place. A topology is therefore defined for the unit process itself, which shows the relations between the zones. Inside each zone there is a set of chemical species and phenomena, such as reactions, phase transitions, heat transfer etc. A formalized graphical methodology is developed as a tool for modelling these zones and their interaction. The symbols defined in the graphical framework are associated with objects and classes. The rules for linking the objects are described using OMT (Object Modeling Technique) diagrams and formal language formulations. The basic classes that are defined are implemented using the C++ programming language. The ferromanganese process is a complex unit process. A general description of the process equipment is given, and a detailed discussion of the process itself and a system theoretical overview of it. The object-oriented framework is then used to develop a dynamic model based on mass and energy balances. The model is validated by measurements from an industrial furnace. 101 refs., 119 figs., 20 tabs.

Study of the Influence of Social Relationships among Students on Knowledge Building Using a Moderately Constructivist Learning Model

Science.gov (United States)

Alonso, Fernando; Manrique, Daniel; Martínez, Loïc; Viñes, José M.

2015-01-01

The main objective of higher education institutions is to educate students to high standards to proficiently perform their role in society. Elsewhere we presented empirical evidence illustrating that the use of a blended learning approach to the learning process that applies a moderate constructivist e-learning instructional model improves…

An object model for beamline descriptions

International Nuclear Information System (INIS)

Hill, B.W.; Martono, H.; Gillespie, J.S.

1997-01-01

Translation of beamline model descriptions between different accelerator codes presents a unique challenge due to the different representations used for various elements and subsystems. These differences range from simple units conversions to more complex translations involving multiple beamline components. A representation of basic accelerator components is being developed in order to define a meta-structure from which beamline models, in different codes, can be described and to facilitate the translation of models between these codes. Sublines of basic components will be used to represent more complex beamline descriptions and bridge the gap between codes which may represent a beamline element as a single entity, and those which use multiple elements to describe the same physical device. A C++ object model for supporting this beamline description and a grammar for describing beamlines in terms of these components is being developed. The object model will support a common graphic user interface and translation filters for representing native beamline descriptions for a variety of accelerator codes. An overview of our work on the object model for beamline descriptions is presented here. copyright 1997 American Institute of Physics

Individual Learning Accounts and Other Models of Financing Lifelong Learning

Science.gov (United States)

Schuetze, Hans G.

2007-01-01

To answer the question "Financing what?" this article distinguishes several models of lifelong learning as well as a variety of lifelong learning activities. Several financing methods are briefly reviewed, however the principal focus is on Individual Learning Accounts (ILAs) which were seen by some analysts as a promising model for…

Teaching Economics: A Cooperative Learning Model.

Science.gov (United States)

Caropreso, Edward J.; Haggerty, Mark

2000-01-01

Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a…

An object-oriented approach to energy-economic modeling

Energy Technology Data Exchange (ETDEWEB)

Wise, M.A.; Fox, J.A.; Sands, R.D.

1993-12-01

In this paper, the authors discuss the experiences in creating an object-oriented economic model of the U.S. energy and agriculture markets. After a discussion of some central concepts, they provide an overview of the model, focusing on the methodology of designing an object-oriented class hierarchy specification based on standard microeconomic production functions. The evolution of the model from the class definition stage to programming it in C++, a standard object-oriented programming language, will be detailed. The authors then discuss the main differences between writing the object-oriented program versus a procedure-oriented program of the same model. Finally, they conclude with a discussion of the advantages and limitations of the object-oriented approach based on the experience in building energy-economic models with procedure-oriented approaches and languages.

Prenatal treatment prevents learning deficit in Down syndrome model.