Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

How Object, Situation and Personality Shape Human Attitude in Learning: An Activity Perspective and a Multilevel Modeling Approach

ERIC Educational Resources Information Center

Sun, Jun

2009-01-01

Based on Activity Theory, this article examines attitude formation in human learning as shaped by the experiences of individual learners with various learning objects in particular learning contexts. It hypothesizes that a learner's object-related perceptions, personality traits and situational perceptions may have different relationships with the…

Invariant visual object recognition: a model, with lighting invariance.

PubMed

Rolls, Edmund T; Stringer, Simon M

2006-01-01

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model 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 Transformation learning. 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 in this paper we show 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 model 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.

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

ERIC Educational Resources Information Center

Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

2015-01-01

This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

Grading for Understanding--Standards-Based Grading

ERIC Educational Resources Information Center

Zimmerman, Todd

2017-01-01

Standards-based grading (SBG), sometimes called learning objectives-based assessment (LOBA), is an assessment model that relies on students demonstrating mastery of learning objectives (sometimes referred to as standards). The goal of this grading system is to focus students on mastering learning objectives rather than on accumulating points. I…

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention are Coordinated Using Surface-Based Attentional Shrouds

ERIC Educational Resources Information Center

Fazl, Arash; Grossberg, Stephen; Mingolla, Ennio

2009-01-01

How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are attention and eye movements intelligently coordinated to facilitate object learning? A neural model provides a unified…

Self-paced model learning for robust visual tracking

NASA Astrophysics Data System (ADS)

Huang, Wenhui; Gu, Jason; Ma, Xin; Li, Yibin

2017-01-01

In visual tracking, learning a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and the frequency of model updating, which contains many details that could affect the tracking results. Self-paced learning (SPL) has recently been attracting considerable interest in the fields of machine learning and computer vision. SPL is inspired by the learning principle underlying the cognitive process of humans, whose learning process is generally from easier samples to more complex aspects of a task. We propose a tracking method that integrates the learning paradigm of SPL into visual tracking, so reliable samples can be automatically selected for model learning. In contrast to many existing model learning strategies in visual tracking, we discover the missing link between sample selection and model learning, which are combined into a single objective function in our approach. Sample weights and model parameters can be learned by minimizing this single objective function. Additionally, to solve the real-valued learning weight of samples, an error-tolerant self-paced function that considers the characteristics of visual tracking is proposed. We demonstrate the robustness and efficiency of our tracker on a recent tracking benchmark data set with 50 video sequences.

Learning and Control Model of the Arm for Loading

NASA Astrophysics Data System (ADS)

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.

An Intelligent Semantic E-Learning Framework Using Context-Aware Semantic Web Technologies

ERIC Educational Resources Information Center

Huang, Weihong; Webster, David; Wood, Dawn; Ishaya, Tanko

2006-01-01

Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused…

Four Single-Page Learning Models.

ERIC Educational Resources Information Center

Hlynka, Denis

1979-01-01

Identifies four models of single-page learning systems that can streamline lengthy, complex prose: Information Mapping, Focal Press Model, Behavioral Objectives Model, and School Mathematics Model. (CMV)

Toward Self-Referential Autonomous Learning of Object and Situation Models.

PubMed

Damerow, Florian; Knoblauch, Andreas; Körner, Ursula; Eggert, Julian; Körner, Edgar

2016-01-01

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.

Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

ERIC Educational Resources Information Center

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

The Usefulness of Learning Objects in Industry Oriented Learning Environments

ERIC Educational Resources Information Center

Fernando, Shantha; Sol, Henk; Dahanayake, Ajantha

2012-01-01

A model is presented to evaluate the usefulness of learning objects for industry oriented learning environments that emphasise training university graduates for job opportunities in a competitive industry oriented economy. Knowledge workers of the industry seek continuous professional development to keep their skills and knowledge up to date. Many…

Statistical Mechanics of Node-perturbation Learning with Noisy Baseline

NASA Astrophysics Data System (ADS)

Hara, Kazuyuki; Katahira, Kentaro; Okada, Masato

2017-02-01

Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. It estimates the gradient of an objective function by using the change in the object function in response to the perturbation. The value of the objective function for an unperturbed output is called a baseline. Cho et al. proposed node-perturbation learning with a noisy baseline. In this paper, we report on building the statistical mechanics of Cho's model and on deriving coupled differential equations of order parameters that depict learning dynamics. We also show how to derive the generalization error by solving the differential equations of order parameters. On the basis of the results, we show that Cho's results are also apply in general cases and show some general performances of Cho's model.

Tracking of multiple targets using online learning for reference model adaptation.

PubMed

Pernkopf, Franz

2008-12-01

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

ERIC Educational Resources Information Center

Yu, Shengquan; Yang, Xianmin; Cheng, Gang

2013-01-01

The key to implementing ubiquitous learning is the construction and organization of learning resources. While current research on ubiquitous learning has primarily focused on concept models, supportive environments and small-scale empirical research, exploring ways to organize learning resources to make them available anywhere on-demand is also…

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

PubMed

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 in VisNet.

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

PubMed Central

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 in VisNet. PMID:28562618

The Development of Invariant Object Recognition Requires Visual Experience with Temporally Smooth Objects

ERIC Educational Resources Information Center

Wood, Justin N.; Wood, Samantha M. W.

2018-01-01

How do newborns learn to recognize objects? According to temporal learning models in computational neuroscience, the brain constructs object representations by extracting smoothly changing features from the environment. To date, however, it is unknown whether newborns depend on smoothly changing features to build invariant object representations.…

The core learning objectives education model: an approach to the teaching of core concepts in the clinical clerkship.

PubMed

Rapp, David E; Lyon, Mark B; Orvieto, Marcelo A; Zagaja, Gregory P

2005-10-01

The classical approach to the undergraduate medical clerkship has several limitations, including variability of clinical exposure and method of examination. As a result, the clerkship experience does not ensure exposure to and reinforcement of the fundamental concepts of a given specialty. This article reviews the classic approach to clerkship education within the undergraduate medical education. Specific attention is placed on clinical exposure and clerkship examination. We describe the introduction of the Core Learning Objective (CLO) educational model at the University of Chicago Section of Urology. This model is designed to provide an efficient exposure to and evaluation of core clerkship learning objectives. The CLO model has been successfully initiated, focusing on both technical and clinical skill sets. The proposed model has been introduced with positive initial results and should allow for an efficient approach to the teaching and evaluation of core objectives in clerkship education.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Application of ''Earl's Assessment "as", Assessment "for", and Assessment "of" Learning Model'' with Orthopaedic Assessment Clinical Competence

ERIC Educational Resources Information Center

Lafave, Mark R.; Katz, Larry; Vaughn, Norman

2013-01-01

Context: In order to study the efficacy of assessment methods, a theoretical framework of Earl's model of assessment was introduced. Objective: (1) Introduce the predictive learning assessment model (PLAM) as an application of Earl's model of learning; (2) test Earl's model of learning through the use of the Standardized Orthopedic Assessment Tool…

Learning Engines - A Functional Object Model for Developing Learning Resources for the WWW.

ERIC Educational Resources Information Center

Fritze, Paul; Ip, Albert

The Learning Engines (LE) model, developed at the University of Melbourne (Australia), supports the integration of rich learning activities into the World Wide Web. The model is concerned with the practical design, educational value, and reusability of software components. The model is focused on the academic teacher who is in the best position to…

Using Digital Learning Objects to Introduce Students to the Nature of Models and the Nature of Matter

ERIC Educational Resources Information Center

Gustafson, Brenda; Mahaffy, Peter; Martin, Brian

2011-01-01

This article reports a subset of findings from a larger study centered on designing a series of six digital learning objects to help Grade 5 (age 10-12) students begin to consider the nature of models (understood as the physical or mental representation of objects, phenomena, or processes), the particle nature of matter, and the behavior of…

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

A computational exploration of complementary learning mechanisms in the primate ventral visual pathway.

PubMed

Spoerer, Courtney J; Eguchi, Akihiro; Stringer, Simon M

2016-02-01

In order to develop transformation invariant representations of objects, the visual system must make use of constraints placed upon object transformation by the environment. For example, objects transform continuously from one point to another in both space and time. These two constraints have been exploited separately in order to develop translation and view invariance in a hierarchical multilayer model of the primate ventral visual pathway in the form of continuous transformation learning and temporal trace learning. We show for the first time that these two learning rules can work cooperatively in the model. Using these two learning rules together can support the development of invariance in cells and help maintain object selectivity when stimuli are presented over a large number of locations or when trained separately over a large number of viewing angles. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Model United Nations and Deep Learning: Theoretical and Professional Learning

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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…

Using speakers' referential intentions to model early cross-situational word learning.

PubMed

Frank, Michael C; Goodman, Noah D; Tenenbaum, Joshua B

2009-05-01

Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

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

ERIC Educational Resources Information Center

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…

A Methodology for Developing Learning Objects for Web Course Delivery

ERIC Educational Resources Information Center

Stauffer, Karen; Lin, Fuhua; Koole, Marguerite

2008-01-01

This article presents a methodology for developing learning objects for web-based courses using the IMS Learning Design (IMS LD) specification. We first investigated the IMS LD specification, determining how to use it with online courses and the student delivery model, and then applied this to a Unit of Learning (UOL) for online computer science…

Modellus: Learning Physics with Mathematical Modelling

NASA Astrophysics Data System (ADS)

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--differential equations--are the most important mathematical objects used for modelling Natural phenomena. In traditional approaches, they are introduced only at advanced level, because it takes a long time for students to be introduced to the fundamental principles of Calculus. With the new proposed approach, rates of change can be introduced also at early stages on learning if teachers stress semi-quantitative reasoning and use adequate computer tools. In this thesis, there is also presented Modellus, a computer tool for modelling and experimentation. This computer tool has a user interface that allows students to start doing meaningful conceptual and empirical experiments without the need to learn new syntax, as is usual with established tools. The different steps in the process of constructing and exploring models can be done with Modellus, both from physical points of view and from mathematical points of view. Modellus activities show how mathematics and physics have a unity that is very difficult to see with traditional approaches. Mathematical models are treated as concrete-abstract objects: concrete in the sense that they can be manipulated directly with a computer and abstract in the sense that they are representations of relations between variables. Data gathered from two case studies, one with secondary school students and another with first year undergraduate students support the main ideas of the thesis. Also data gathered from teachers (from college and secondary schools), mainly through an email structured questionnaire, shows that teachers agree on the potential of modelling in the learning of physics (and mathematics) and of the most important aspects of the proposed framework to integrate modelling as an essential component of the curriculum. Schools, as all institutions, change at a very slow rate. There are a multitude of reasons for this. And traditional curricula, where the emphasis is on rote learning of facts, can only be changed if schools have access to new and powerful views of learning and to new tools, that support meaningful conceptual learning and are as common and easy to use as pencil and paper.

Adaptive Urban Stormwater Management Using a Two-stage Stochastic Optimization Model

NASA Astrophysics Data System (ADS)

Hung, F.; Hobbs, B. F.; McGarity, A. E.

2014-12-01

In many older cities, stormwater results in combined sewer overflows (CSOs) and consequent water quality impairments. Because of the expense of traditional approaches for controlling CSOs, cities are considering the use of green infrastructure (GI) to reduce runoff and pollutants. Examples of GI include tree trenches, rain gardens, green roofs, and rain barrels. However, the cost and effectiveness of GI are uncertain, especially at the watershed scale. We present a two-stage stochastic extension of the Stormwater Investment Strategy Evaluation (StormWISE) model (A. McGarity, JWRPM, 2012, 111-24) to explicitly model and optimize these uncertainties in an adaptive management framework. A two-stage model represents the immediate commitment of resources ("here & now") followed by later investment and adaptation decisions ("wait & see"). A case study is presented for Philadelphia, which intends to extensively deploy GI over the next two decades (PWD, "Green City, Clean Water - Implementation and Adaptive Management Plan," 2011). After first-stage decisions are made, the model updates the stochastic objective and constraints (learning). We model two types of "learning" about GI cost and performance. One assumes that learning occurs over time, is automatic, and does not depend on what has been done in stage one (basic model). The other considers learning resulting from active experimentation and learning-by-doing (advanced model). Both require expert probability elicitations, and learning from research and monitoring is modelled by Bayesian updating (as in S. Jacobi et al., JWRPM, 2013, 534-43). The model allocates limited financial resources to GI investments over time to achieve multiple objectives with a given reliability. Objectives include minimizing construction and O&M costs; achieving nutrient, sediment, and runoff volume targets; and community concerns, such as aesthetics, CO2 emissions, heat islands, and recreational values. CVaR (Conditional Value at Risk) and chance constraints are placed on the objectives to achieve desired confidence levels. By varying the budgets, reliability constraints, and priorities among other objectives, we generate a range of GI deployment strategies that represent tradeoffs among objectives as well as the confidence in achieving them.

Visual learning in drosophila: application on a roving robot and comparisons

NASA Astrophysics Data System (ADS)

Arena, P.; De Fiore, S.; Patané, L.; Termini, P. S.; Strauss, R.

2011-05-01

Visual learning is an important aspect of fly life. Flies are able to extract visual cues from objects, like colors, vertical and horizontal distributedness, and others, that can be used for learning to associate a meaning to specific features (i.e. a reward or a punishment). Interesting biological experiments show trained stationary flying flies avoiding flying towards specific visual objects, appearing on the surrounding environment. Wild-type flies effectively learn to avoid those objects but this is not the case for the learning mutant rutabaga defective in the cyclic AMP dependent pathway for plasticity. A bio-inspired architecture has been proposed to model the fly behavior and experiments on roving robots were performed. Statistical comparisons have been considered and mutant-like effect on the model has been also investigated.

An object-based visual attention model for robotic applications.

PubMed

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.

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization.

PubMed

Rajaei, Karim; Khaligh-Razavi, Seyed-Mahdi; Ghodrati, Masoud; Ebrahimpour, Reza; Shiri Ahmad Abadi, Mohammad Ebrahim

2012-01-01

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Models, Matter and Truth in Doing and Learning Science

ERIC Educational Resources Information Center

Hardman, Mark

2017-01-01

Doing science involves the development and evaluation of models. These models are not objective truths but can be understood as explanations, which scientists use to explore and reason about an aspect of the world. Learning science involves students expressing and engaging with models in the classroom. However, this learning should not be seen as…

Perseverative Interference with Object-in-Place Scene Learning in Rhesus Monkeys with Bilateral Ablation of Ventrolateral Prefrontal Cortex

ERIC Educational Resources Information Center

Baxter, Mark G.; Browning, Philip G. F.; Mitchell, Anna S.

2008-01-01

Surgical disconnection of the frontal cortex and inferotemporal cortex severely impairs many aspects of visual learning and memory, including learning of new object-in-place scene memory problems, a monkey model of episodic memory. As part of a study of specialization within prefrontal cortex in visual learning and memory, we tested monkeys with…

Top-down attention based on object representation and incremental memory for knowledge building and inference.

PubMed

Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

2013-10-01

Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rectangular Array Model Supporting Students' Spatial Structuring in Learning Multiplication

ERIC Educational Resources Information Center

Shanty, Nenden Octavarulia; Wijaya, Surya

2012-01-01

We examine how rectangular array model can support students' spatial structuring in learning multiplication. To begin, we define what we mean by spatial structuring as the mental operation of constructing an organization or form for an object or set of objects. For that reason, the eggs problem was chosen as the starting point in which the…

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

ERIC Educational Resources Information Center

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…

Learning the Gestalt rule of collinearity from object motion.

PubMed

Prodöhl, Carsten; Würtz, Rolf P; von der Malsburg, Christoph

2003-08-01

The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.

A Bootstrapping Model of Frequency and Context Effects in Word Learning

ERIC Educational Resources Information Center

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

2017-01-01

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…

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

ERIC Educational Resources Information Center

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…

Learning to recognize objects on the fly: a neurally based dynamic field approach.

PubMed

Faubel, Christian; Schöner, Gregor

2008-05-01

Autonomous robots interacting with human users need to build and continuously update scene representations. This entails the problem of rapidly learning to recognize new objects under user guidance. Based on analogies with human visual working memory, we propose a dynamical field architecture, in which localized peaks of activation represent objects over a small number of simple feature dimensions. Learning consists of laying down memory traces of such peaks. We implement the dynamical field model on a service robot and demonstrate how it learns 30 objects from a very small number of views (about 5 per object are sufficient). We also illustrate how properties of feature binding emerge from this framework.

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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.

Using the Knowledge, Process, Practice (KPP) model for driving the design and development of online postgraduate medical education.

PubMed

Shaw, Tim; Barnet, Stewart; Mcgregor, Deborah; Avery, Jennifer

2015-01-01

Online learning is a primary delivery method for continuing health education programs. It is critical that programs have curricula objectives linked to educational models that support learning. Using a proven educational modelling process ensures that curricula objectives are met and a solid basis for learning and assessment is achieved. To develop an educational design model that produces an educationally sound program development plan for use by anyone involved in online course development. We have described the development of a generic educational model designed for continuing health education programs. The Knowledge, Process, Practice (KPP) model is founded on recognised educational theory and online education practice. This paper presents a step-by-step guide on using this model for program development that encases reliable learning and evaluation. The model supports a three-step approach, KPP, based on learning outcomes and supporting appropriate assessment activities. It provides a program structure for online or blended learning that is explicit, educationally defensible, and supports multiple assessment points for health professionals. The KPP model is based on best practice educational design using a structure that can be adapted for a variety of online or flexibly delivered postgraduate medical education programs.

The Development of Professional Learning Community in Primary Schools

ERIC Educational Resources Information Center

Sompong, Samoot; Erawan, Prawit; Dharm-tad-sa-na-non, Sudharm

2015-01-01

The objectives of this research are: (1) To study the current situation and need for developing professional learning community in primary schools; (2) To develop the model for developing professional learning community, and (3) To study the findings of development for professional learning community based on developed model related to knowledge,…

Assessment of the core learning objectives curriculum for the urology clerkship.

PubMed

Rapp, David E; Gong, Edward M; Reynolds, W Stuart; Lucioni, Alvaro; Zagaja, Gregory P

2007-11-01

The traditional approach to the surgical clerkship has limitations, including variability of clinical exposure. To optimize student education we developed and introduced the core learning objectives curriculum, which is designed to allow students freedom to direct their learning and focus on core concepts. We performed a prospective, randomized, controlled study to compare the efficacy of core learning objectives vs traditional curricula through objective and subjective measures. Medical students were randomly assigned to the core learning objectives or traditional curricula during the 2-week urology clerkship. Faculty was blinded to student assignment. Upon rotation completion all students were given a 20-question multiple choice examination covering basic urology concepts. In addition, students completed a questionnaire addressing subjective clerkship satisfaction, comprising 15 questions. Between June 2005 and January 2007, 10 core learning objectives students and 10 traditional students completed the urology clerkship. The average +/- SEM multiple choice examination score was 12.1 +/- 0.87 and 9.8 +/- 0.59 for students assigned to the core learning objectives and traditional curricula, respectively (p <0.05). Subjective scores were higher in the core learning objectives cohort, although this result did not attain statistical significance (124.9 +/- 3.72 vs 114.3 +/- 4.96, p = 0.1). Core learning objectives students reported higher satisfaction in all 15 assessed subjective end points. Our experience suggests that the core learning objectives model may be an effective educational tool to help students achieve a broad and directed exposure to the core urological concepts.

Designing an Educational Game with Ten Steps to Complex Learning

ERIC Educational Resources Information Center

Enfield, Jacob

2012-01-01

Few instructional design (ID) models exist which are specific for developing educational games. Moreover, those extant ID models have not been rigorously evaluated. No ID models were found which focus on educational games with complex learning objectives. "Ten Steps to Complex Learning" (TSCL) is based on the four component instructional…

Impaired associative learning in schizophrenia: behavioral and computational studies

PubMed Central

Diwadkar, Vaibhav A.; Flaugher, Brad; Jones, Trevor; Zalányi, László; Ujfalussy, Balázs; Keshavan, Matcheri S.

2008-01-01

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia. PMID:19003486

Model of Learning Organizational Development of Primary School Network under the Office of Basic Education Commission

ERIC Educational Resources Information Center

Sai-rat, Wipa; Tesaputa, Kowat; Sriampai, Anan

2015-01-01

The objectives of this study were 1) to study the current state of and problems with the Learning Organization of the Primary School Network, 2) to develop a Learning Organization Model for the Primary School Network, and 3) to study the findings of analyses conducted using the developed Learning Organization Model to determine how to develop the…

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

NASA Astrophysics Data System (ADS)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

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

PubMed

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.

Applying Objective Diagnostic Criteria to Students in a College Support Program for Learning Disabilities

ERIC Educational Resources Information Center

Sparks, Richard L.; Lovett, Benjamin J.

2013-01-01

This study examined whether a large group of postsecondary students participating in a support program for students classified as having learning disabilities (LD) met criteria for five objective diagnostic models for LD: IQ-achievement discrepancy (1.0 SD, 1.5 SD, and greater than 2.0 SD) models, a "Diagnostic and Statistical Manual of…

From Doing to Learning: Changed focus during a pre-school learning study project on organic decomposition

NASA Astrophysics Data System (ADS)

Ljung-Djärf, Agneta; Magnusson, Andreas; Peterson, Sam

2014-03-01

We explored the use of the learning study (LS) model in developing Swedish pre-school science learning. This was done by analysing a 3-cycle LS project implemented to help a group of pre-school teachers (n = 5) understand their science educational practice, by collaboratively and systematically challenging it. Data consisted of video recordings of 1 screening (n = 7), 1 initial planning meeting, 3 analysis meetings, 3 interventions, and 78 individual test interviews with the children (n = 26). The study demonstrated that the teachers were initially uncomfortable with using scientific concepts and with maintaining the children's focus on the object of learning without framing it with play. During the project, we noted a shift in focus towards the object of learning and how to get the children to discern it. As teachers' awareness changed, enhanced learning was noted among the children. The study suggests that the LS model can promote pre-school science learning as follows: by building on, re-evaluating, and expanding children's experiences; and by helping the teachers focus on and contrast critical aspects of an object of learning, and to reflect on the use of play, imagination, and concepts and on directing the children's focus when doing so. Our research showed that the LS model holds promise to advance pre-school science learning by offering a theoretical tool useable to shift the focus from doing to learning while teaching science using learning activities.

Deep Learning through Reusable Learning Objects in an MBA Program

ERIC Educational Resources Information Center

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…

Development of Conceptual Learning and Development Assessment Series II: Cutting Tool. Technical Report No. 431. Reprinted December 1977.

ERIC Educational Resources Information Center

Klausmeier, Herbert J.; And Others

The Conceptual Learning and Development (CLD) Model suggests four successive levels of concept learning: (1) concrete--recognizing an object which has been encountered previously; (2) identity--recognizing a known object when it appears in a different spatial, time, or sensory perspective; (3) classificatory--generalizing that two items are alike…

Utah Guidance and Toolkit for Student Learning Objectives: Instructions and Materials. Utah SLOs. Updated

ERIC Educational Resources Information Center

Utah State Office of Education, 2014

2014-01-01

This document is intended to help teachers understand and create Student Learning Objectives (SLOs). This resource is a practical guide intended to provide clarity to a complex but worthwhile task. This resource may also be used by administrators for professional learning. As Utah moves toward providing a "Model for Measuring Educator…

Methodology for Evaluating Quality and Reusability of Learning Objects

ERIC Educational Resources Information Center

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…

Incidental Learning Speeds Visual Search by Lowering Response Thresholds, Not by Improving Efficiency: Evidence from Eye Movements

ERIC Educational Resources Information Center

Hout, Michael C.; Goldinger, Stephen D.

2012-01-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no…

Learning-based stochastic object models for characterizing anatomical variations

NASA Astrophysics Data System (ADS)

Dolly, Steven R.; Lou, Yang; Anastasio, Mark A.; Li, Hua

2018-03-01

It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection.

PubMed

Ouyang, Wanli; Zeng, Xingyu; Wang, Xiaogang; Qiu, Shi; Luo, Ping; Tian, Yonglong; Li, Hongsheng; Yang, Shuo; Wang, Zhe; Li, Hongyang; Loy, Chen Change; Wang, Kun; Yan, Junjie; Tang, Xiaoou

2016-07-07

In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [16], which was the state-of-the-art, from 31% to 50.3% on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1%. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.

Implementation of formal learning objectives during a physical medicine and rehabilitation sports medicine rotation.

PubMed

Smith, Jay; Laskowski, Edward R; Newcomer-Aney, Karen L; Thompson, Jeffrey M; Schaefer, Michael P; Morfe, Erasmus G

2005-04-01

To develop and implement formal learning objectives during a physical medicine and rehabilitation sports medicine rotation and characterize resident experiences with the objectives over a 16-mo period. Prospective, including learning objective development, implementation, and postrotation survey. A total of 69 learning objectives were developed by physical medicine and rehabilitation staff physician consensus, including 39 core objectives. Eighteen residents completed 4-wk sports medicine rotations from January 2003 through April 2004. Residents completed an average of 31 total objectives (45%; range, 3-52), of which 24 (62%; range, 3-35) were core. Residents completed the highest percentage of knee (60%), shoulder (57%), and ankle-foot (57%) objectives and reported that objectives related to these areas were most effective to facilitate learning. In general, residents reported that objective content was good and that the objectives delineated important concepts to learn during the rotation. Seventeen of 18 residents indicated that the objectives should be permanently implemented into the sports rotation and that similar objectives should be developed for other rotations. Based on our experience and the recommendations of residents, the average resident should be able to complete approximately 30 objectives during a typical 4-wk rotation. Successful implementation of specific, consensus-derived learning objectives is possible within the context of a busy clinical practice. Our initial physician staff and resident experience with the objectives suggests that this model may be useful as a supplementary educational tool in physical medicine and rehabilitation residency programs.

Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

PubMed Central

Emberson, Lauren L.; Rubinstein, Dani

2016-01-01

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779

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

PubMed Central

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. PMID:22723777

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

PubMed

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.

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

PubMed Central

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

2015-01-01

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 crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. PMID:22425615

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

PubMed

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 crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects. Copyright © 2012 Elsevier Inc. All rights reserved.

Filling in the Gaps of Clerkship with a Comprehensive Clinical Skills Curriculum

ERIC Educational Resources Information Center

Veale, Pamela; Carson, Julie; Coderre, Sylvain; Woloschuk, Wayne; Wright, Bruce; McLaughlin, Kevin

2014-01-01

Although the clinical clerkship model is based upon sound pedagogy, including theories of social learning and situated learning, studies evaluating clinical performance of residents suggests that this model may not fully meet the learning needs of students. Here our objective was to design a curriculum to bridge the learning gaps of the existing…

The Effectiveness of Three Experiential Teaching Approaches on Student Science Learning in Fifth-Grade Public School Classrooms.

ERIC Educational Resources Information Center

Powell, Kristin; Wells, Marcella

2002-01-01

Compares the effects of three experiential science lessons in meeting the objectives of the Colorado model content science standards. Uses Kolb's (1984) experiential learning model as a framework for understanding the process by which students engage in learning when participating in experiential learning activities. Uses classroom exams and…

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

ERIC Educational Resources Information Center

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…

Top-Down Visual Saliency via Joint CRF and Dictionary Learning.

PubMed

Yang, Jimei; Yang, Ming-Hsuan

2017-03-01

Top-down visual saliency is an important module of visual attention. In this work, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a visual dictionary. The proposed model incorporates a layered structure from top to bottom: CRF, sparse coding and image patches. With sparse coding as an intermediate layer, CRF is learned in a feature-adaptive manner; meanwhile with CRF as the output layer, the dictionary is learned under structured supervision. For efficient and effective joint learning, we develop a max-margin approach via a stochastic gradient descent algorithm. Experimental results on the Graz-02 and PASCAL VOC datasets show that our model performs favorably against state-of-the-art top-down saliency methods for target object localization. In addition, the dictionary update significantly improves the performance of our model. We demonstrate the merits of the proposed top-down saliency model by applying it to prioritizing object proposals for detection and predicting human fixations.

Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

ERIC Educational Resources Information Center

Rastegarmoghadam, Mahin; Ziarati, Koorush

2017-01-01

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…

A Rational Model of the Effects of Distributional Information on Feature Learning

ERIC Educational Resources Information Center

Austerweil, Joseph L.; Griffiths, Thomas L.

2011-01-01

Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of…

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

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.

An Analysis of Implementation Issues for the Searchable Content Object Reference Model (SCORM) in Navy Education and Training

DTIC Science & Technology

2003-09-01

content objects to be used and reused within civilian and military education and training Learning Management Systems (LMS) across the World Wide Web...to be used and reused within civilian and military education and training Learning Management Systems (LMS) across the World Wide Web. vi...1998, SUBJECT: ENHANCING LEARNING AND EDUCATION THROUGH TECHNOLOGY

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

PubMed

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.

Autonomous mental development with selective attention, object perception, and knowledge representation

NASA Astrophysics Data System (ADS)

Ban, Sang-Woo; Lee, Minho

2008-04-01

Knowledge-based clustering and autonomous mental development remains a high priority research topic, among which the learning techniques of neural networks are used to achieve optimal performance. In this paper, we present a new framework that can automatically generate a relevance map from sensory data that can represent knowledge regarding objects and infer new knowledge about novel objects. The proposed model is based on understating of the visual what pathway in our brain. A stereo saliency map model can selectively decide salient object areas by additionally considering local symmetry feature. The incremental object perception model makes clusters for the construction of an ontology map in the color and form domains in order to perceive an arbitrary object, which is implemented by the growing fuzzy topology adaptive resonant theory (GFTART) network. Log-polar transformed color and form features for a selected object are used as inputs of the GFTART. The clustered information is relevant to describe specific objects, and the proposed model can automatically infer an unknown object by using the learned information. Experimental results with real data have demonstrated the validity of this approach.

Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

NASA Astrophysics Data System (ADS)

Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

2018-03-01

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Web Instruction with the LBO Model.

ERIC Educational Resources Information Center

Agarwal, Rajshree; Day, A. Edward

2000-01-01

Presents a Web site that utilizes the Learning-by-Objective (LBO) model that integrates Internet tools for knowledge transmission, communication, and assessment of learning. Explains that the LBO model has been used in creating micro and macroeconomic course Web sites with WebCT software. (CMK)

The Magic Bullet: A Tool for Assessing and Evaluating Learning Potential in Games

ERIC Educational Resources Information Center

Becker, Katrin

2011-01-01

This paper outlines a simple and effective model that can be used to evaluate and design educational digital games. It also facilitates the formulation of strategies for using existing games in learning contexts. The model categorizes game goals and learning objectives into one or more of four possible categories. An overview of the model is…

The Development of Discovery-Inquiry Learning Model to Reduce the Science Misconceptions of Junior High School Students

ERIC Educational Resources Information Center

Tompo, Basman; Ahmad, Arifin; Muris, Muris

2016-01-01

The main objective of this research was to develop discovery inquiry (DI) learning model to reduce the misconceptions of Science student level of secondary school that is valid, practical, and effective. This research was an R&D (research and development). The trials of discovery inquiry (DI) learning model were carried out in two different…

Learning to Predict and Control the Physics of Our Movements

PubMed Central

2017-01-01

When we hold an object in our hand, the mass of the object alters the physics of our arm, changing the relationship between motor commands that our brain sends to our arm muscles and the resulting motion of our hand. If the object is unfamiliar to us, our first movement will exhibit an error, producing a trajectory that is different from the one we had intended. This experience of error initiates learning in our brain, making it so that on the very next attempt our motor commands partially compensate for the unfamiliar physics, resulting in smaller errors. With further practice, the compensation becomes more complete, and our brain forms a model that predicts the physics of the object. This model is a motor memory that frees us from having to relearn the physics the next time that we encounter the object. The mechanism by which the brain transforms sensory prediction errors into corrective motor commands is the basis for how we learn the physics of objects with which we interact. The cerebellum and the motor cortex appear to be critical for our ability to learn physics, allowing us to use tools that extend our capabilities, making us masters of our environment. PMID:28202784

Object Toolkit Version 4.3 User’s Manual

DTIC Science & Technology

2016-12-31

unlimited. (OPS-17-12855 dtd 19 Jan 2017) 13. SUPPLEMENTARY NOTES 14. ABSTRACT Object Toolkit is a finite - element model builder specifically designed for...INTRODUCTION 1 What Is Object Toolkit? Object Toolkit is a finite - element model builder specifically designed for creating representations of spacecraft...Nascap-2k and EPIC, the user is not required to purchase or learn expensive finite element generators to create system models. Second, Object Toolkit

Guiding Age 10-11 Students to Notice the Salient Features of Physical Change Models in Chemistry Digital Learning Objects

ERIC Educational Resources Information Center

Gustafson, Brenda; Mahaffy, Peter; Martin, Brian

2015-01-01

This paper focuses on one Grade 5 class (9 females; 9 males) who worked in student-pairs to view five digital learning object (DLO) lessons created by the authors and meant to introduce students to the nature of models, the particle nature of matter, and physical change. Specifically, the paper focuses on whether DLO design elements could assist…

Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

Tracking-Learning-Detection.

PubMed

Kalal, Zdenek; Mikolajczyk, Krystian; Matas, Jiri

2012-07-01

This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of "experts": (1) P-expert estimates missed detections, and (2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.

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

ERIC Educational Resources Information Center

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…

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

PubMed Central

Clerkin, Elizabeth M.; Hart, Elizabeth; Rehg, James M.; Yu, Chen

2017-01-01

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’. PMID:27872373

Transforming Clinical Imaging Data for Virtual Reality Learning Objects

ERIC Educational Resources Information Center

Trelease, Robert B.; Rosset, Antoine

2008-01-01

Advances in anatomical informatics, three-dimensional (3D) modeling, and virtual reality (VR) methods have made computer-based structural visualization a practical tool for education. In this article, the authors describe streamlined methods for producing VR "learning objects," standardized interactive software modules for anatomical sciences…

An Investigation of User Perceptions and Attitudes towards Learning Objects

ERIC Educational Resources Information Center

Lau, Siong-Hoe; Woods, Peter C.

2008-01-01

This study empirically evaluates the technology acceptance model drawn from Information Systems (IS) literature to investigate how user beliefs and attitudes influence learning-object use among higher education learners by evaluating the relationships between perceived usefulness, perceived ease of use, attitude, behavioural intentions and actual…

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

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 evidentialmore » 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.« less

Learning to distinguish similar objects

NASA Astrophysics Data System (ADS)

Seibert, Michael; Waxman, Allen M.; Gove, Alan N.

1995-04-01

This paper describes how the similarities and differences among similar objects can be discovered during learning to facilitate recognition. The application domain is single views of flying model aircraft captured in silhouette by a CCD camera. The approach was motivated by human psychovisual and monkey neurophysiological data. The implementation uses neural net processing mechanisms to build a hierarchy that relates similar objects to superordinate classes, while simultaneously discovering the salient differences between objects within a class. Learning and recognition experiments both with and without the class similarity and difference learning show the effectiveness of the approach on this visual data. To test the approach, the hierarchical approach was compared to a non-hierarchical approach, and was found to improve the average percentage of correctly classified views from 77% to 84%.

On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning.

PubMed

Grossberg, Stephen; Markowitz, Jeffrey; Cao, Yongqiang

2011-12-01

Visual object recognition is an essential accomplishment of advanced brains. Object recognition needs to be tolerant, or invariant, with respect to changes in object position, size, and view. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise in stages due to processes across multiple cortical areas. These processes include the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features with different levels of vigilance. The model predicts that data from the tradeoff and image morph tasks emerge from different levels of vigilance in the animals performing them. This result illustrates how different vigilance requirements of a task may change the course of category learning, notably the critical features that are attended and incorporated into learned category prototypes. The model outlines a path for developing an animal model of how defective vigilance control can lead to symptoms of various mental disorders, such as autism and amnesia. Copyright © 2011 Elsevier Ltd. All rights reserved.

Development of Learning Management Model Based on Constructivist Theory and Reasoning Strategies for Enhancing the Critical Thinking of Secondary Students

ERIC Educational Resources Information Center

Chaipichit, Dudduan; Jantharajit, Nirat; Chookhampaeng, Sumalee

2015-01-01

The objectives of this research were to study issues around the management of science learning, problems that are encountered, and to develop a learning management model to address those problems. The development of that model and the findings of its study were based on Constructivist Theory and literature on reasoning strategies for enhancing…

Object Lesson: Discovering and Learning to Recognize Objects

DTIC Science & Technology

2002-01-01

4 x 4 grid represents the possible appearance of an edge, quantized to just two luminance levels. The dark line centered in the grid is the average...11):33-38, 1995. [16] Maja J. Mataric . A distributed model for mobile robot environment-learning and navigation. Technical Report AIlR- 1228

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

PubMed

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.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Effects of Instructional Design with Mental Model Analysis on Learning.

ERIC Educational Resources Information Center

Hong, Eunsook

This paper presents a model for systematic instructional design that includes mental model analysis together with the procedures used in developing computer-based instructional materials in the area of statistical hypothesis testing. The instructional design model is based on the premise that the objective for learning is to achieve expert-like…

Quality Assurance Model for Digital Adult Education Materials

ERIC Educational Resources Information Center

Dimou, Helen; Kameas, Achilles

2016-01-01

Purpose: This paper aims to present a model for the quality assurance of digital educational material that is appropriate for adult education. The proposed model adopts the software quality standard ISO/IEC 9126 and takes into account adult learning theories, Bloom's taxonomy of learning objectives and two instructional design models: Kolb's model…

Electronic Education System Model-2

ERIC Educational Resources Information Center

Güllü, Fatih; Kuusik, Rein; Laanpere, Mart

2015-01-01

In this study we presented new EES Model-2 extended from EES model for more productive implementation in e-learning process design and modelling in higher education. The most updates were related to uppermost instructional layer. We updated learning processes object of the layer for adaptation of educational process for young and old people,…

Object recognition in images via a factor graph model

NASA Astrophysics Data System (ADS)

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.

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

Learning, Judgment, and the Rooted Particular

ERIC Educational Resources Information Center

McCabe, David

2012-01-01

This article begins by acknowledging the general worry that scholarship in the humanities lacks the rigor and objectivity of other scholarly fields. In considering the validity of that criticism, I distinguish two models of learning: the covering law model exemplified by the natural sciences, and the model of rooted particularity that…

A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

ERIC Educational Resources Information Center

Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

2011-01-01

This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

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

ERIC Educational Resources Information Center

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…

A new generation of intelligent trainable tools for analyzing large scientific image databases

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Smyth, Padhraic; Atkinson, David J.

1994-01-01

The focus of this paper is on the detection of natural, as opposed to human-made, objects. The distinction is important because, in the context of image analysis, natural objects tend to possess much greater variability in appearance than human-made objects. Hence, we shall focus primarily on the use of algorithms that 'learn by example' as the basis for image exploration. The 'learn by example' approach is potentially more generally applicable compared to model-based vision methods since domain scientists find it relatively easier to provide examples of what they are searching for versus describing a model.

Exploration of Machine Learning Approaches to Predict Pavement Performance

DOT National Transportation Integrated Search

2018-03-23

Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

Integrating visual learning within a model-based ATR system

NASA Astrophysics Data System (ADS)

Carlotto, Mark; Nebrich, Mark

2017-05-01

Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of visual information for detecting, classifying, and identifying manmade objects in aerial imagery. We describe the integration of a visual learning component into the Image Data Conditioner (IDC) for target/clutter and other visual classification tasks. The component is based on an implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual learning in an ATR context requires the ability to recognize objects independent of location, scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate target locations. A bootstrap learning method effectively extends the operation of the classifier beyond the training set and provides a measure of confidence. We show how the classifier can be used to learn other features that are difficult to compute from imagery such as target direction, and to assess the performance of the visual learning process itself.

A Module for Adaptive Course Configuration and Assessment in Moodle

NASA Astrophysics Data System (ADS)

Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia

Personalization and Adaptation are among the main challenges in the field of e-learning, where currently just few Learning Management Systems, mostly experimental ones, support such features. In this work we present an architecture that allows Moodle to interact with the Lecomps system, an adaptive learning system developed earlier by our research group, that has been working in a stand-alone modality so far. In particular, the Lecomps responsibilities are circumscribed to the sole production of personalized learning objects sequences and to the management of the student model, leaving to Moodle all the rest of the activities for course delivery. The Lecomps system supports the "dynamic" adaptation of learning objects sequences, basing on the student model, i.e., learner's Cognitive State and Learning Style. Basically, this work integrates two main Lecomps tasks into Moodle, to be directly managed by it: Authentication and Quizzes.

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

ERIC Educational Resources Information Center

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…

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

PubMed Central

Chang, Hung-Cheng; Grossberg, Stephen; Cao, Yongqiang

2014-01-01

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 (PFC), amygdala, basal ganglia (BG), and superior colliculus (SC). PMID:24987339

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

ERIC Educational Resources Information Center

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…

Understanding How Service Learning Pedagogy Impacts Student Learning Objectives

ERIC Educational Resources Information Center

Wang, Liz; Calvano, Lisa

2018-01-01

Service learning (SL) is gaining popularity in business schools as a way to supplement traditional pedagogies. Research indicates that SL improves particular learning outcomes, but little is known about how this happens. Using Kolb's theory of experiential learning, the authors develop and test a conceptual model that explains how SL activates the…

Autonomous physics-based color learning under daylight

NASA Astrophysics Data System (ADS)

Berube Lauziere, Yves; Gingras, Denis J.; Ferrie, Frank P.

1999-09-01

An autonomous approach for learning the colors of specific objects assumed to have known body spectral reflectances is developed for daylight illumination conditions. The main issue is to be able to find these objects autonomously in a set of training images captured under a wide variety of daylight illumination conditions, and to extract their colors to determine color space regions that are representative of the objects' colors and their variations. The work begins by modeling color formation under daylight using the color formation equations and the semi-empirical model of Judd, MacAdam and Wyszecki (CIE daylight model) for representing the typical spectral distributions of daylight. This results in color space regions that serve as prior information in the initial phase of learning which consists in detecting small reliable clusters of pixels having the appropriate colors. These clusters are then expanded by a region growing technique using broader color space regions than those predicted by the model. This is to detect objects in a way that is able to account for color variations which the model cannot due to its limitations. Validation on the detected objects is performed to filter out those that are not of interest and to eliminate unreliable pixel color values extracted from the remaining ones. Detection results using the color space regions determined from color values obtained by this procedure are discussed.

Proof of Economic Viability of Blended Learning Business Models

ERIC Educational Resources Information Center

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…

Learning the Norm of Internality: NetNorm, a Connectionist Model

ERIC Educational Resources Information Center

Thierry, Bollon; Adeline, Paignon; Pascal, Pansu

2011-01-01

The objective of the present article is to show that connectionist simulations can be used to model some of the socio-cognitive processes underlying the learning of the norm of internality. For our simulations, we developed a connectionist model which we called NetNorm (based on Dual-Network formalism). This model is capable of simulating the…

Toward a unified model of face and object recognition in the human visual system

PubMed Central

Wallis, Guy

2013-01-01

Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

The Corporate University Model for Continuous Learning, Training and Development.

ERIC Educational Resources Information Center

El-Tannir, Akram A.

2002-01-01

Corporate universities typically convey corporate culture and provide systematic curriculum aimed at achieving strategic objectives. Virtual access and company-specific content combine to provide opportunities for continuous and active learning, a model that is becoming pervasive. (Contains 17 references.) (SK)

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

PubMed

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).

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Competency to Study and Learn in Stressful Contests: Fundamentals of the "e-Coping with Academic Stress"™ Utility

ERIC Educational Resources Information Center

de la Fuente, Jesús; López, Mireia; Zapata, Lucía; Martínez-Vicente, Jose Manuel; Vera, Manuel Mariano; Solinas, Giulliana; Fadda, Salvatore

2014-01-01

There has been growing research interest in achievement emotions in university teaching-learning processes in recent years. While their importance has been firmly established, there continues to be a need for assessment and intervention models. The objective of this report is to present the "Competency Model for Studying, Learning and…

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.

PubMed

Cantwell, George; Riesenhuber, Maximilian; Roeder, Jessica L; Ashby, F Gregory

2017-05-01

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Essen Learning Model--A Step towards a Representation of Learning Objectives.

ERIC Educational Resources Information Center

Bick, Markus; Pawlowski, Jan M.; Veith, Patrick

The importance of the Extensible Markup Language (XML) technology family in the field of Computer Assisted Learning (CAL) can not be denied. The Instructional Management Systems Project (IMS), for example, provides a learning resource XML binding specification. Considering this specification and other implementations using XML to represent…

Le Climat d'Apprentissage; Analyse Conceptuelle=Learning Climate: A Conceptual Analysis.

ERIC Educational Resources Information Center

Michaud, Pierre; And Others

1989-01-01

Analyzes and defines the concept of "learning climate." Discusses the conceptual models of Biddle and Brookover. Considers the use of observation techniques and surveys to measure learning climate. Reviews research on the relationship between learning climate and the attainment of course and institutional objectives. (DMM)

Computational modeling of the neural representation of object shape in the primate ventral visual system

PubMed Central

Eguchi, Akihiro; Mender, Bedeho M. W.; Evans, Benjamin D.; Humphreys, Glyn W.; Stringer, Simon M.

2015-01-01

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 recognize the whole object. PMID:26300766

Unanticipated Learning Outcomes Associated with Commitment to Change in Continuing Medical Education

ERIC Educational Resources Information Center

Dolcourt, Jack L.; Zuckerman, Grace

2003-01-01

Introduction: Educator-derived, predetermined instructional objectives are integral to the traditional instructional model and form the linkage between instructional design and postinstruction evaluation. The traditional model does not consider unanticipated learning outcomes. We explored the contribution of learner-identified desired outcomes…

How to build a course in mathematical-biological modeling: content and processes for knowledge and skill.

PubMed

Hoskinson, Anne-Marie

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments.

How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

PubMed Central

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical–biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments. PMID:20810966

A Model for the Design of Puzzle-Based Games Including Virtual and Physical Objects

ERIC Educational Resources Information Center

Melero, Javier; Hernandez-Leo, Davinia

2014-01-01

Multiple evidences in the Technology-Enhanced Learning domain indicate that Game-Based Learning can lead to positive effects in students' performance and motivation. Educational games can be completely virtual or can combine the use of physical objects or spaces in the real world. However, the potential effectiveness of these approaches…

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

ERIC Educational Resources Information Center

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and…

Automatic Sound Generation for Spherical Objects Hitting Straight Beams Based on Physical Models.

ERIC Educational Resources Information Center

Rauterberg, M.; And Others

Sounds are the result of one or several interactions between one or several objects at a certain place and in a certain environment; the attributes of every interaction influence the generated sound. The following factors influence users in human/computer interaction: the organization of the learning environment, the content of the learning tasks,…

Theta oscillations promote temporal sequence learning.

PubMed

Crivelli-Decker, Jordan; Hsieh, Liang-Tien; Clarke, Alex; Ranganath, Charan

2018-05-17

Many theoretical models suggest that neural oscillations play a role in learning or retrieval of temporal sequences, but the extent to which oscillations support sequence representation remains unclear. To address this question, we used scalp electroencephalography (EEG) to examine oscillatory activity over learning of different object sequences. Participants made semantic decisions on each object as they were presented in a continuous stream. For three "Consistent" sequences, the order of the objects was always fixed. Activity during Consistent sequences was compared to "Random" sequences that consisted of the same objects presented in a different order on each repetition. Over the course of learning, participants made faster semantic decisions to objects in Consistent, as compared to objects in Random sequences. Thus, participants were able to use sequence knowledge to predict upcoming items in Consistent sequences. EEG analyses revealed decreased oscillatory power in the theta (4-7 Hz) band at frontal sites following decisions about objects in Consistent sequences, as compared with objects in Random sequences. The theta power difference between Consistent and Random only emerged in the second half of the task, as participants were more effectively able to predict items in Consistent sequences. Moreover, we found increases in parieto-occipital alpha (10-13 Hz) and beta (14-28 Hz) power during the pre-response period for objects in Consistent sequences, relative to objects in Random sequences. Linear mixed effects modeling revealed that single trial theta oscillations were related to reaction time for future objects in a sequence, whereas beta and alpha oscillations were only predictive of reaction time on the current trial. These results indicate that theta and alpha/beta activity preferentially relate to future and current events, respectively. More generally our findings highlight the importance of band-specific neural oscillations in the learning of temporal order information. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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.

Educational Strategies for Learning to Learn from Role Models.

ERIC Educational Resources Information Center

Williams, Martha

The way that socialization, via role modeling, can be enhanced in professional education is discussed, and 10 class assignments are used to illustrate teaching methods for enhancing role modeling, based on a course on women in administration at The University of Texas at Austin. Among the objectives of the course assignments are the following: to…

Model Early Childhood Learning Program, Baltimore, Maryland. Model Programs, Title III--Elementary and Secondary Education Act.

ERIC Educational Resources Information Center

National Center for Educational Communication (DHEW/NIE), Washington, DC.

The purpose of the Model Early Childhood Learning Program of Baltimore, Md., City Schools is to provide experiences for disadvantaged children which will constitute the prerequisite developmental history needed to undertake first grade concepts and skills. The project's stated objectives are: (1) to improve the measured aptitude or readiness for…

Pedagogy and the Intuitive Appeal of Learning Styles in Post-Compulsory Education in England

ERIC Educational Resources Information Center

Nixon, Lawrence; Gregson, Maggie; Spedding, Trish

2007-01-01

Despite the rigorous and robust evaluation of learning styles theories, models and inventories, little objective evidence in support of their effectiveness has been found. The lack of unambiguous evidence in support of these models and practices leaves the continued popularity of these models and instruments as a puzzle. Two related accounts of…

The Development of Monograph with 3-Dimentional Illustrations Titled “Augmented Chemistry: Hydrocarbon” as Learning Enrichment Materials

NASA Astrophysics Data System (ADS)

Ernawati, D.; Ikhsan, J.

2017-02-01

The development of 3D technology provides more advantages in education sectors. In chemistry, the 3D technology makes chemistry objects look more tangible. This research developed a monograph titled “Augmented Chemistry: Hydrocarbon” as learning enrichment materials. The development model consisted of 5 steps, which were the adaptation of the ADDIE model. The 3D objects of chemistry were built using the computer applications of Chem Sketch, and Google Sketch Up with AR Plugin. The 3D objects were displayed by relevant markers on the texts of the monograph from which the visualizations of the 3D objects appeared when they were captured by digital camera of laptop or smartphone, and were possibly viewed with free-rotation. Not only were 3D chemistry objects included in the monograph, but also graphics, videos, audios, and animations, which facilitated more fun learning for readers of the monograph. After the reviews by the experts of subject matter, of media, of instruction, and by peers, the monograph was revised, and then rated by chemistry teachers. The analysis of the data showed that the monograph titled “Augmented Chemistry: Hydrocarbon” was in the criteria of very good for the enrichment materials of Chemistry learning.

The Role of Unconscious Information Processing in the Acquisition and Learning of Instructional Messages

ERIC Educational Resources Information Center

Kuldas, Seffetullah; Bakar, Zainudin Abu; Ismail, Hairul Nizam

2012-01-01

This review investigates how the unconscious information processing can create satisfactory learning outcomes, and can be used to ameliorate the challenges of teaching students to regulate their learning processes. The search for the ideal model of human information processing as regards achievement of teaching and learning objectives is a…

Using AMLO to Improve the Quality of Teacher Education Outcomes

ERIC Educational Resources Information Center

Al-Shammari, Zaid

2012-01-01

This study aims to find ways to improve learning outcomes in teacher education courses by using an Analysis Model for Learning Outcomes (AMLO). It addresses the improvement of the quality of teacher education by analyzing learning outcomes and implementing curriculum modifications related to specific learning objectives and their effects on…

The Ghost Condition: Imitation Versus Emulation in Young Children's Observational Learning.

ERIC Educational Resources Information Center

Thompson, Doreen E.; Russell, James

2004-01-01

Although observational learning by children may occur through imitating a modeler's actions, it can also occur through learning about an object's dynamic affordances- a process that M. Tomasello (1996) calls "emulation." The relative contributions of imitation and emulation within observational learning were examined in a study with 14- to…

Improving orbit prediction accuracy through supervised machine learning

NASA Astrophysics Data System (ADS)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Using visual lateralization to model learning and memory in zebrafish larvae

PubMed Central

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

2015-01-01

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. PMID:25727677

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

NASA Astrophysics Data System (ADS)

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.

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

PubMed Central

Grossberg, Stephen; Srinivasan, Karthik; Yazdanbakhsh, Arash

2015-01-01

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations. PMID:25642198

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements.

PubMed

Grossberg, Stephen; Srinivasan, Karthik; Yazdanbakhsh, Arash

2014-01-01

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.

Developing Model Assesement for Learning (AFL) to Improve Quality and Evaluation in Pragmatic Course in IAIN Surakarta

ERIC Educational Resources Information Center

Retnaningsih, Woro; Djatmiko; Sumarlam

2017-01-01

The research objective is to develop a model of Assessment for Learning (AFL) in Pragmatic course in IAIN Surakarta. The research problems are as follows: How did the lecturer develop a model of AFL? What was the form of assessment information used as the model of AFL? How was the results of the implementation of the model of assessment. The…

Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change.

PubMed

Leibold, Christian; Monsalve-Mercado, Mauro M

2016-08-01

Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easily change their location in space and yet we can memorize object locations. But how should a neural circuit ideally be set up to integrate two input streams (object location and identity) in case the overall synaptic changes should be minimized during ongoing learning? This letter provides a theoretical framework on how the two input pathways should ideally be specified. Generally the model predicts that the information-rich pathway should be plastic and encoded sparsely, whereas the pathway conveying less information should be encoded densely and undergo learning only if a neuronal representation of a novel object has to be established. As an example, we consider hippocampal area CA1, which combines place and object information. The model thereby provides a normative account of hippocampal rate remapping, that is, modulations of place field activity by changes of local cues. It may as well be applicable to other brain areas (such as neocortical layer V) that learn combinatorial codes from multiple input streams.

On valuing information in adaptive-management models.

PubMed

Moore, Alana L; McCarthy, Michael A

2010-08-01

Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large.

The Development of Learning Model Based on Problem Solving to Construct High-Order Thinking Skill on the Learning Mathematics of 11th Grade in SMA/MA

ERIC Educational Resources Information Center

Syahputra, Edi; Surya, Edy

2017-01-01

This paper is a summary study of team Postgraduate on 11th grade. The objective of this study is to develop a learning model based on problem solving which can construct high-order thinking on the learning mathematics in SMA/MA. The subject of dissemination consists of Students of 11th grade in SMA/MA in 3 kabupaten/kota in North Sumatera, namely:…

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: evidence from eye movements.

PubMed

Hout, Michael C; Goldinger, Stephen D

2012-02-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated nontarget objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent.

A SCORM Odyssey.

ERIC Educational Resources Information Center

Shackelford, Bill

2002-01-01

Discusses the Shareable Content Object Reference Model (SCORM), which integrates electronic learning standards to provide a common ground for course development. Describes the Advanced Distributed Learning Co-Laboratory at the University of Wisconsin- Madison campus. (JOW)

Continuum of Medical Education in Obstetrics and Gynecology.

ERIC Educational Resources Information Center

Dohner, Charles W.; Hunter, Charles A., Jr.

1980-01-01

Over the past eight years the obstetric and gynecology specialty has applied a system model of instructional planning to the continuum of medical education. The systems model of needs identification, preassessment, instructional objectives, instructional materials, learning experiences; and evaluation techniques directly related to objectives was…

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

PubMed

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.

Leadership Perspectives on Operationalizing the Learning Health Care System in an Integrated Delivery System.

PubMed

Psek, Wayne; Davis, F Daniel; Gerrity, Gloria; Stametz, Rebecca; Bailey-Davis, Lisa; Henninger, Debra; Sellers, Dorothy; Darer, Jonathan

2016-01-01

Healthcare leaders need operational strategies that support organizational learning for continued improvement and value generation. The learning health system (LHS) model may provide leaders with such strategies; however, little is known about leaders' perspectives on the value and application of system-wide operationalization of the LHS model. The objective of this project was to solicit and analyze senior health system leaders' perspectives on the LHS and learning activities in an integrated delivery system. A series of interviews were conducted with 41 system leaders from a broad range of clinical and administrative areas across an integrated delivery system. Leaders' responses were categorized into themes. Ten major themes emerged from our conversations with leaders. While leaders generally expressed support for the concept of the LHS and enhanced system-wide learning, their concerns and suggestions for operationalization where strongly aligned with their functional area and strategic goals. Our findings suggests that leaders tend to adopt a very pragmatic approach to learning. Leaders expressed a dichotomy between the operational imperative to execute operational objectives efficiently and the need for rigorous evaluation. Alignment of learning activities with system-wide strategic and operational priorities is important to gain leadership support and resources. Practical approaches to addressing opportunities and challenges identified in the themes are discussed. Continuous learning is an ongoing, multi-disciplinary function of a health care delivery system. Findings from this and other research may be used to inform and prioritize system-wide learning objectives and strategies which support reliable, high value care delivery.

Domain learning naming game for color categorization.

PubMed

Li, Doujie; Fan, Zhongyan; Tang, Wallace K S

2017-01-01

Naming game simulates the evolution of vocabulary in a population of agents. Through pairwise interactions in the games, agents acquire a set of vocabulary in their memory for object naming. The existing model confines to a one-to-one mapping between a name and an object. Focus is usually put onto name consensus in the population rather than knowledge learning in agents, and hence simple learning model is usually adopted. However, the cognition system of human being is much more complex and knowledge is usually presented in a complicated form. Therefore, in this work, we extend the agent learning model and design a new game to incorporate domain learning, which is essential for more complicated form of knowledge. In particular, we demonstrate the evolution of color categorization and naming in a population of agents. We incorporate the human perceptive model into the agents and introduce two new concepts, namely subjective perception and subliminal stimulation, in domain learning. Simulation results show that, even without any supervision or pre-requisition, a consensus of a color naming system can be reached in a population solely via the interactions. Our work confirms the importance of society interactions in color categorization, which is a long debate topic in human cognition. Moreover, our work also demonstrates the possibility of cognitive system development in autonomous intelligent agents.

Domain learning naming game for color categorization

PubMed Central

2017-01-01

Naming game simulates the evolution of vocabulary in a population of agents. Through pairwise interactions in the games, agents acquire a set of vocabulary in their memory for object naming. The existing model confines to a one-to-one mapping between a name and an object. Focus is usually put onto name consensus in the population rather than knowledge learning in agents, and hence simple learning model is usually adopted. However, the cognition system of human being is much more complex and knowledge is usually presented in a complicated form. Therefore, in this work, we extend the agent learning model and design a new game to incorporate domain learning, which is essential for more complicated form of knowledge. In particular, we demonstrate the evolution of color categorization and naming in a population of agents. We incorporate the human perceptive model into the agents and introduce two new concepts, namely subjective perception and subliminal stimulation, in domain learning. Simulation results show that, even without any supervision or pre-requisition, a consensus of a color naming system can be reached in a population solely via the interactions. Our work confirms the importance of society interactions in color categorization, which is a long debate topic in human cognition. Moreover, our work also demonstrates the possibility of cognitive system development in autonomous intelligent agents. PMID:29136661

Exploring the changing learning environment of the gross anatomy lab.

PubMed

Hopkins, Robin; Regehr, Glenn; Wilson, Timothy D

2011-07-01

The objective of this study was to assess the impact of virtual models and prosected specimens in the context of the gross anatomy lab. In 2009, student volunteers from an undergraduate anatomy class were randomly assigned to study groups in one of three learning conditions. All groups studied the muscles of mastication and completed identical learning objectives during a 45-minute lab. All groups were provided with two reference atlases. Groups were distinguished by the type of primary tools they were provided: gross prosections, three-dimensional stereoscopic computer model, or both resources. The facilitator kept observational field notes. A prepost multiple-choice knowledge test was administered to evaluate students' learning. No significant effect of the laboratory models was demonstrated between groups on the prepost assessment of knowledge. Recurring observations included students' tendency to revert to individual memorization prior to the posttest, rotation of models to match views in the provided atlas, and dissemination of groups into smaller working units. The use of virtual lab resources seemed to influence the social context and learning environment of the anatomy lab. As computer-based learning methods are implemented and studied, they must be evaluated beyond their impact on knowledge gain to consider the effect technology has on students' social development.

A model of blended learning in a preclinical course in prosthetic dentistry.

PubMed

Reissmann, Daniel R; Sierwald, Ira; Berger, Florian; Heydecke, Guido

2015-02-01

The aim of this study was to evaluate the use of blending learning that added online tools to traditional learning methods in a preclinical course in prosthetic dentistry at one dental school in Germany. The e-learning modules were comprised of three main components: fundamental principles, additional information, and learning objective tests. Video recordings of practical demonstrations were prepared and cut into sequences meant to achieve single learning goals. The films were accompanied by background information and, after digital processing, were made available online. Additionally, learning objective tests and learning contents were integrated. Evaluations of 71 of 89 students (response rate: 80%) in the course with the integrated e-learning content were available for the study. Compared with evaluation results of the previous years, a substantial and statistically significant increase in satisfaction with learning content (from 30% and 34% to 86%, p<0.001) and learning effect (from 65% and 63% to 83%, p<0.05) was observed. Satisfaction ratings stayed on a high level in three subsequent courses with the modules. Qualitative evaluation revealed mostly positive responses, with not a single negative comment regarding the blended learning concept. The results showed that the e-learning tool was appreciated by the students and suggest that learning objective tests can be successfully implemented in blended learning.

Conceptualising Integration in CLIL and Multilingual Education

ERIC Educational Resources Information Center

Nikula, Tarja, Ed.; Dafouz, Emma, Ed.; Moore, Pat, Ed.; Smit, Ute, Ed.

2016-01-01

Content and Language Integrated Learning (CLIL) is a form of education that combines language and content learning objectives, a shared concern with other models of bilingual education. While CLIL research has often addressed learning outcomes, this volume focuses on how integration can be conceptualised and investigated. Using different…

Three-Dimensional Printing of a Scalable Molecular Model and Orbital Kit for Organic Chemistry Teaching and Learning

ERIC Educational Resources Information Center

Penny, Matthew R.; Cao, Zi Jing; Patel, Bhaven; dos Santos, Bruno Sil; Asquith, Christopher R. M.; Szulc, Blanka R.; Rao, Zenobia X.; Muwaffak, Zaid; Malkinson, John P.; Hilton, Stephen T.

2017-01-01

Three-dimensional (3D) chemical models are a well-established learning tool used to enhance the understanding of chemical structures by converting two-dimensional paper or screen outputs into realistic three-dimensional objects. While commercial atom model kits are readily available, there is a surprising lack of large molecular and orbital models…

Virtual parameter-estimation experiments in Bioprocess-Engineering education.

PubMed

Sessink, Olivier D T; Beeftink, Hendrik H; Hartog, Rob J M; Tramper, Johannes

2006-05-01

Cell growth kinetics and reactor concepts constitute essential knowledge for Bioprocess-Engineering students. Traditional learning of these concepts is supported by lectures, tutorials, and practicals: ICT offers opportunities for improvement. A virtual-experiment environment was developed that supports both model-related and experimenting-related learning objectives. Students have to design experiments to estimate model parameters: they choose initial conditions and 'measure' output variables. The results contain experimental error, which is an important constraint for experimental design. Students learn from these results and use the new knowledge to re-design their experiment. Within a couple of hours, students design and run many experiments that would take weeks in reality. Usage was evaluated in two courses with questionnaires and in the final exam. The faculties involved in the two courses are convinced that the experiment environment supports essential learning objectives well.

The Development of SCORM-Conformant Learning Content Based on the Learning Cycle Using Participatory Design

ERIC Educational Resources Information Center

Su, C. Y.; Chiu, C. H.; Wang, T. I.

2010-01-01

This study incorporates the 5E learning cycle strategy to design and develop Sharable Content Object Reference Model-conformant materials for elementary science education. The 5E learning cycle that supports the constructivist approach has been widely applied in science education. The strategy consists of five phases: engagement, exploration,…

An Ontology-Based Framework for Bridging Learning Design and Learning Content

ERIC Educational Resources Information Center

Knight, Colin, Gasevic, Dragan; Richards, Griff

2006-01-01

The paper describes an ontology-based framework for bridging learning design and learning object content. In present solutions, researchers have proposed conceptual models and developed tools for both of those subjects, but without detailed discussions of how they can be used together. In this paper we advocate the use of ontologies to explicitly…

Teachers' Reasoning: Classroom Visual Representational Practices in the Context of Introductory Chemical Bonding

ERIC Educational Resources Information Center

Patron, Emelie; Wikman, Susanne; Edfors, Inger; Johansson-Cederblad, Brita; Linder, Cedric

2017-01-01

Visual representations are essential for communication and meaning-making in chemistry, and thus the representational practices play a vital role in the teaching and learning of chemistry. One powerful contemporary model of classroom learning, the variation theory of learning, posits that the way an object of learning gets handled is another vital…

Collaborative mining and transfer learning for relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Eslami, Mohammed

2015-06-01

Many of the real-world problems, - including human knowledge, communication, biological, and cyber network analysis, - deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

Longitudinal Models of Reading Achievement of Students with Learning Disabilities and without Disabilities

ERIC Educational Resources Information Center

Sullivan, Amanda L.; Kohli, Nidhi; Farnsworth, Elyse M.; Sadeh, Shanna; Jones, Leila

2017-01-01

Objective: Accurate estimation of developmental trajectories can inform instruction and intervention. We compared the fit of linear, quadratic, and piecewise mixed-effects models of reading development among students with learning disabilities relative to their typically developing peers. Method: We drew an analytic sample of 1,990 students from…

On Practising in Physical Education: Outline for a Pedagogical Model

ERIC Educational Resources Information Center

Aggerholm, K.; Standal, O.; Barker, D. M.; Larsson, H.

2018-01-01

Background: Models-based approaches to physical education have in recent years developed as a way for teachers and students to concentrate on a manageable number of learning objectives, and align pedagogical approaches with learning subject matter and context. This paper draws on Hannah Arendt's account of "vita activa" to map existing…

Teaching Supply Chain Management Complexities: A SCOR Model Based Classroom Simulation

ERIC Educational Resources Information Center

Webb, G. Scott; Thomas, Stephanie P.; Liao-Troth, Sara

2014-01-01

The SCOR (Supply Chain Operations Reference) Model Supply Chain Classroom Simulation is an in-class experiential learning activity that helps students develop a holistic understanding of the processes and challenges of supply chain management. The simulation has broader learning objectives than other supply chain related activities such as the…

Cognitive Modeling of Learning Abilities: A Status Report of LAMP.

ERIC Educational Resources Information Center

Kyllonen, Patrick C.; Christal, Raymond E.

Research activities underway as part of the Air Force's Learning Abilities Measurement Program (LAMP) are described. A major objective of the program is to devise new models of the nature and organization of human abilities, that could be applied to improve personnel selection and classification systems. The activities of the project have been…

Portfolio as a Means of Promoting Autonomous Learning in Teacher Education: A Quasi-Experimental Study

ERIC Educational Resources Information Center

Meeus, Wil; Van Petegem, Peter; Meijer, Joost

2008-01-01

Background: The predominant dissertation model used in teacher education courses in Flanders is the "literature study with practical processing". Despite the practical supplement, this traditional model does not fit sufficiently well with autonomous learning as the objective of modern teacher education dissertations. This study reports on the…

A Model Program of Comprehensive Educational Services for Students With Learning Problems.

ERIC Educational Resources Information Center

Union Township Board of Education, NJ.

Programs are described for learning-disabled or mantally-handicapped elementary and secondary students in regular and special classes in Union, New Jersey, and approximately 58 instructional episodes involving student made objects for understanding technology are presented. In part one, components of the model program such as the multi-learning…

The Role of Motivation, Cognition, and Conscientiousness for Academic Achievement

ERIC Educational Resources Information Center

Imhof, Margarete; Spaeth-Hilbert, Tatjana

2013-01-01

Based on a cognitive motivational process model of learning, the impact of studying behavior on learning outcome is investigated. First-year students (N = 488) participated in the study. Two research questions were addressed: (1) Can cognitive-motivational variables and objective study behavior predict individual learning? (2) Which factors drive…

Ontological Modeling of Educational Resources: A Proposed Implementation for Greek Schools

ERIC Educational Resources Information Center

Poulakakis, Yannis; Vassilakis, Kostas; Kalogiannakis, Michail; Panagiotakis, Spyros

2017-01-01

In eLearning context searching for suitable educational material is still a challenging issue. During the last two decades, various digital repositories, such as Learning Object Repositories, institutional repositories and latterly Open Educational Resources, have been developed to accommodate collections of learning material that can be used for…

Implementation and Deployment of the IMS Learning Design Specification

ERIC Educational Resources Information Center

Paquette, Gilbert; Marino, Olga; De la Teja, Ileana; Lundgren­-Cayrol, Karin; Lonard, Michel; Contamines, Julien

2005-01-01

Knowledge management in organizations, the learning objects paradigm, the advent of a new web generation, and the "Semantic Web" are major actual trends that reveal a potential for a renewed distance learning pedagogy. First and foremost is the use of educational modelling languages and instructional engineering methods to help decide…

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

Creating an Organic Knowledge-Building Environment within an Asynchronous Distributed Learning Context.

ERIC Educational Resources Information Center

Moller, Leslie; Prestera, Gustavo E.; Harvey, Douglas; Downs-Keller, Margaret; McCausland, Jo-Ann

2002-01-01

Discusses organic architecture and suggests that learning environments should be designed and constructed using an organic approach, so that learning is not viewed as a distinct human activity but incorporated into everyday performance. Highlights include an organic knowledge-building model; information objects; scaffolding; discourse action…

An investigation of difficulties experienced by students developing unified modelling language (UML) class and sequence diagrams

NASA Astrophysics Data System (ADS)

Sien, Ven Yu

2011-12-01

Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they essentially do not know "what" to model. This article investigates the difficulties and misconceptions undergraduate students have with analysing systems using unified modelling language analysis class and sequence diagrams. These models were chosen because they represent important static and dynamic aspects of the software system under development. The results of this study will help students produce effective OO models, and facilitate software engineering lecturers design learning materials and approaches for introductory OOAD courses.

A Path Less Chosen: An Assessment of the School of Advanced Military Studies

DTIC Science & Technology

2014-05-22

the theory learned in course one.40 This course used theory , history, doctrine (both US and Soviet), and practical exercises to study the basic...relationships between learning domains, levels of learning and learning objectives, and the experiential learning model.96 In short, there is a major emphasis...discussion. There are multiple theories of education related to the use of discussion in learning . The most frequently cited or referred to amongst

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

PubMed

Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

PubMed Central

Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

The Impact of Secondary School Students' Preconceptions on the Evolution of Their Mental Models of the Greenhouse Effect and Global Warming

ERIC Educational Resources Information Center

Reinfried, Sibylle; Tempelmann, Sebastian

2014-01-01

This paper provides a video-based learning process study that investigates the kinds of mental models of the atmospheric greenhouse effect 13-year-old learners have and how these mental models change with a learning environment, which is optimised in regard to instructional psychology. The objective of this explorative study was to observe and…

Learning Performance with Interactive Simulations in Medical Education: Lessons Learned from Results of Learning Complex Physiological Models with the HAEMOdynamics SIMulator

ERIC Educational Resources Information Center

Holzinger, Andreas; Kickmeier-Rust, Michael D.; Wassertheurer, Sigi; Hessinger, Michael

2009-01-01

Objective: Since simulations are often accepted uncritically, with excessive emphasis being placed on technological sophistication at the expense of underlying psychological and educational theories, we evaluated the learning performance of simulation software, in order to gain insight into the proper use of simulations for application in medical…

Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

ERIC Educational Resources Information Center

Dwiyogo, Wasis D.

2018-01-01

The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

Integration and Deployment of Educational Games in e-Learning Environments: The Learning Object Model Meets Educational Gaming

ERIC Educational Resources Information Center

Torrente, Javier; Moreno-Ger, Pablo; Martinez-Ortiz, Ivan; Fernandez-Manjon, Baltasar

2009-01-01

Game-based learning is becoming popular in the academic discussion of Learning Technologies. However, even though the educational potential of games has been thoroughly discussed in the literature, the integration of the games into educational processes and how to efficiently deliver the games to the students are still open questions. This paper…

Learning to rank using user clicks and visual features for image retrieval.

PubMed

Yu, Jun; Tao, Dacheng; Wang, Meng; Rui, Yong

2015-04-01

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.

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

PubMed Central

Taniguchi, Akira; Taniguchi, Tadahiro; Cangelosi, Angelo

2017-01-01

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. PMID:29311888

Computational model for perception of objects and motions.

PubMed

Yang, WenLu; Zhang, LiQing; Ma, LiBo

2008-06-01

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.

Leadership Perspectives on Operationalizing the Learning Health Care System in an Integrated Delivery System

PubMed Central

Psek, Wayne; Davis, F. Daniel; Gerrity, Gloria; Stametz, Rebecca; Bailey-Davis, Lisa; Henninger, Debra; Sellers, Dorothy; Darer, Jonathan

2016-01-01

Introduction: Healthcare leaders need operational strategies that support organizational learning for continued improvement and value generation. The learning health system (LHS) model may provide leaders with such strategies; however, little is known about leaders’ perspectives on the value and application of system-wide operationalization of the LHS model. The objective of this project was to solicit and analyze senior health system leaders’ perspectives on the LHS and learning activities in an integrated delivery system. Methods: A series of interviews were conducted with 41 system leaders from a broad range of clinical and administrative areas across an integrated delivery system. Leaders’ responses were categorized into themes. Findings: Ten major themes emerged from our conversations with leaders. While leaders generally expressed support for the concept of the LHS and enhanced system-wide learning, their concerns and suggestions for operationalization where strongly aligned with their functional area and strategic goals. Discussion: Our findings suggests that leaders tend to adopt a very pragmatic approach to learning. Leaders expressed a dichotomy between the operational imperative to execute operational objectives efficiently and the need for rigorous evaluation. Alignment of learning activities with system-wide strategic and operational priorities is important to gain leadership support and resources. Practical approaches to addressing opportunities and challenges identified in the themes are discussed. Conclusion: Continuous learning is an ongoing, multi-disciplinary function of a health care delivery system. Findings from this and other research may be used to inform and prioritize system-wide learning objectives and strategies which support reliable, high value care delivery. PMID:27683668

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

PubMed

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.

Active learning in the lecture theatre using 3D printed objects

PubMed Central

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. PMID:27366318

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Non-accidental properties, metric invariance, and encoding by neurons in a model of ventral stream visual object recognition, VisNet.

PubMed

Rolls, Edmund T; Mills, W Patrick C

2018-05-01

When objects transform into different views, some properties are maintained, such as whether the edges are convex or concave, and these non-accidental properties are likely to be important in view-invariant object recognition. The metric properties, such as the degree of curvature, may change with different views, and are less likely to be useful in object recognition. It is shown that in a model of invariant visual object recognition in the ventral visual stream, VisNet, non-accidental properties are encoded much more than metric properties by neurons. Moreover, it is shown how with the temporal trace rule training in VisNet, non-accidental properties of objects become encoded by neurons, and how metric properties are treated invariantly. We also show how VisNet can generalize between different objects if they have the same non-accidental property, because the metric properties are likely to overlap. VisNet is a 4-layer unsupervised model of visual object recognition trained by competitive learning that utilizes a temporal trace learning rule to implement the learning of invariance using views that occur close together in time. A second crucial property of this model of object recognition is, when neurons in the level corresponding to the inferior temporal visual cortex respond selectively to objects, whether neurons in the intermediate layers can respond to combinations of features that may be parts of two or more objects. In an investigation using the four sides of a square presented in every possible combination, it was shown that even though different layer 4 neurons are tuned to encode each feature or feature combination orthogonally, neurons in the intermediate layers can respond to features or feature combinations present is several objects. This property is an important part of the way in which high capacity can be achieved in the four-layer ventral visual cortical pathway. These findings concerning non-accidental properties and the use of neurons in intermediate layers of the hierarchy help to emphasise fundamental underlying principles of the computations that may be implemented in the ventral cortical visual stream used in object recognition. Copyright © 2018 Elsevier Inc. All rights reserved.

Blind Students' Learning of Probability through the Use of a Tactile Model

ERIC Educational Resources Information Center

Vita, Aida Carvalho; Kataoka, Verônica Yumi

2014-01-01

The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence "Jefferson's Random Walk", in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D…

Self-Regulation, Coregulation, and Socially Shared Regulation: Exploring Perspectives of Social in Self-Regulated Learning Theory

ERIC Educational Resources Information Center

Hadwin, Allyson; Oshige, Mika

2011-01-01

Background/Context: Models of self-regulated learning (SRL) have increasingly acknowledged aspects of social context influence in its process; however, great diversity exists in the theoretical positioning of "social" in these models. Purpose/Objective/Research Question/Focus of Study: The purpose of this review article is to introduce and…

A Hierarchical and Contextual Model for Learning and Recognizing Highly Variant Visual Categories

DTIC Science & Technology

2010-01-01

neighboring pattern primitives, to create our model. We also present a minimax entropy framework for automatically learning which contextual constraints are...Grammars . . . . . . . . . . . . . . . . . . 19 3.2 Markov Random Fields . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Creating a Contextual...Compositional Boosting. . . . . 119 7.8 Top-down hallucinations of missing objects. . . . . . . . . . . . . . . 121 7.9 The bottom-up to top-down

Identifying Affordances of 3D Printed Tangible Models for Understanding Core Biological Concepts

ERIC Educational Resources Information Center

Davenport, Jodi L.; Silberglitt, Matt; Boxerman, Jonathan; Olson, Arthur

2014-01-01

3D models derived from actual molecular structures have the potential to transform student learning in biology. We share findings related to our research questions: 1) what types of interactions with a protein folding kit promote specific learning objectives?, and 2) what features of the instructional environment (e.g., peer interactions, teacher…

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: Evidence from eye movements

PubMed Central

Hout, Michael C.; Goldinger, Stephen D.

2011-01-01

When observers search for a target object, they incidentally learn the identities and locations of “background” objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays (Hout & Goldinger, 2010). Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory; Wolfe, 2007). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated non-target objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent. PMID:21574743

Instance-Based Ontology Matching for Open and Distance Learning Materials

ERIC Educational Resources Information Center

Cerón-Figueroa, Sergio; López-Yáñez, Itzamá; Villuendas-Rey, Yenny; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Yáñez-Márquez, Cornelio

2017-01-01

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is…

The Evolution of SCORM to Tin Can API: Implications for Instructional Design

ERIC Educational Resources Information Center

Lindert, Lisa; Su, Bude

2016-01-01

Integrating and documenting formal and informal learning experiences is challenging using the current Shareable Content Object Reference Model (SCORM) eLearning standard, which limits the media and data that are obtained from eLearning. In response to SCORM's limitations, corporate, military, and academic institutions have collaborated to develop…

A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning

ERIC Educational Resources Information Center

Wang, Te-Hua; Chang, Flora Chia-I

2007-01-01

The sharable content object reference model (SCORM) includes a representation of distance learning contents and a behavior definition of how users should interact with the contents. Generally, SCORMcompliant systems were based on multimedia and Web technologies on PCs. We further build a pervasive learning environment, which allows users to read…

A Stakeholder Approach to Implementing E-Learning in a University

ERIC Educational Resources Information Center

Cook, John; Holley, Debbie; Andrew, David

2007-01-01

This paper describes the most recent phase in a mature e-learning project, in the area of reusable learning objects, that has attempted to bring about technological and cultural change. Following an overview of the project and organisational context, an institutional change model is described that helps managers and stakeholders to identify…

Environmental Education throughout FE. 2: A Model and Unit of Environmental Learning Outcomes. FEDA Paper.

ERIC Educational Resources Information Center

Lawrence, Allan; Parkin, Christopher

1995-01-01

This report summarizes the outcomes of the "Colleges Going Green" project that sought to develop a widely applicable core of environmental learning outcomes (curriculum objectives) and illustrative learning assignments and to review activity in colleges and provide guidance on introducing environmental policy. This report also presents a…

3D interactive augmented reality-enhanced digital learning systems for mobile devices

NASA Astrophysics Data System (ADS)

Feng, Kai-Ten; Tseng, Po-Hsuan; Chiu, Pei-Shuan; Yang, Jia-Lin; Chiu, Chun-Jie

2013-03-01

With enhanced processing capability of mobile platforms, augmented reality (AR) has been considered a promising technology for achieving enhanced user experiences (UX). Augmented reality is to impose virtual information, e.g., videos and images, onto a live-view digital display. UX on real-world environment via the display can be e ectively enhanced with the adoption of interactive AR technology. Enhancement on UX can be bene cial for digital learning systems. There are existing research works based on AR targeting for the design of e-learning systems. However, none of these work focuses on providing three-dimensional (3-D) object modeling for en- hanced UX based on interactive AR techniques. In this paper, the 3-D interactive augmented reality-enhanced learning (IARL) systems will be proposed to provide enhanced UX for digital learning. The proposed IARL systems consist of two major components, including the markerless pattern recognition (MPR) for 3-D models and velocity-based object tracking (VOT) algorithms. Realistic implementation of proposed IARL system is conducted on Android-based mobile platforms. UX on digital learning can be greatly improved with the adoption of proposed IARL systems.

Incrementally learning objects by touch: online discriminative and generative models for tactile-based recognition.

PubMed

Soh, Harold; Demiris, Yiannis

2014-01-01

Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.

Hands-on Simulation versus Traditional Video-learning in Teaching Microsurgery Technique

PubMed Central

SAKAMOTO, Yusuke; OKAMOTO, Sho; SHIMIZU, Kenzo; ARAKI, Yoshio; HIRAKAWA, Akihiro; WAKABAYASHI, Toshihiko

2017-01-01

Bench model hands-on learning may be more effective than traditional didactic practice in some surgical fields. However, this has not been reported for microsurgery. Our study objective was to demonstrate the efficacy of bench model hands-on learning in acquiring microsuturing skills. The secondary objective was to evaluate the aptitude for microsurgery based on personality assessment. Eighty-six medical students comprising 62 men and 24 women were randomly assigned to either 20 min of hands-on learning with a bench model simulator or 20 min of video-learning using an instructional video. They then practiced microsuturing for 40 min. Each student then made three knots, and the time to complete the task was recorded. The final products were scored by two independent graders in a blind fashion. All participants then took a personality test, and their microsuture test scores and the time to complete the task were compared. The time to complete the task was significantly shorter in the simulator group than in the video-learning group. The final product scores tended to be higher with simulator-learning than with video-learning, but the difference was not significant. Students with high “extraversion” scores on the personality inventory took a shorter time to complete the suturing test. Simulator-learning was more effective for microsurgery training than video instruction, especially in understanding the procedure. There was a weak association between personality traits and microsurgery skill. PMID:28381653

Good Features to Correlate for Visual Tracking

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Alatan, A. Aydin

2018-05-01

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.

Saul: Towards Declarative Learning Based Programming

PubMed Central

Kordjamshidi, Parisa; Roth, Dan; Wu, Hao

2015-01-01

We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction. PMID:26635465

Saul: Towards Declarative Learning Based Programming.

PubMed

Kordjamshidi, Parisa; Roth, Dan; Wu, Hao

2015-07-01

We present Saul , a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints. We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Food Service Trades. Instructional System Development Model for Vermont Area Vocational Centers.

ERIC Educational Resources Information Center

1975

The model curriculum guide in food service occupations consists of 26 units of study presented in outline form and intended for use at the secondary level. The outline presents a concept statement, behavioral objective, learning activities, teacher resource needs, suggested evaluation techniques, lesson objectives, a lesson/unit plan, and…

Antecedences to Continued Intentions of Adopting E-Learning System in Blended Learning Instruction: A Contingency Framework Based on Models of Information System Success and Task-Technology Fit

ERIC Educational Resources Information Center

Lin, Wen-Shan; Wang, Chun-Hsien

2012-01-01

The objective of this study is to propose a research framework that investigates the relation between perceived fit and system factors that can motivate learners in continuing utilizing an e-learning system in blended learning instruction. As learners have the face-to-face learning opportunity in interacting with lecturers, the study aims at…

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

PubMed

Mansoor, Awais; Cerrolaza, Juan J; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-11

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM 1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal shape deep learning: applications to pediatric lung field segmentation

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovany; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, local- ization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0:927 using only the four highest modes of variation (compared to 0:888 with classical ASM1 (p-value=0:01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation

PubMed Central

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-01-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects. PMID:28592911

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

PubMed Central

Leibo, Joel Z.; Liao, Qianli; Freiwald, Winrich A.; Anselmi, Fabio; Poggio, Tomaso

2017-01-01

SUMMARY The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations like depth-rotations [1, 2]. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3, 4, 5, 6]. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here we demonstrate that one specific biologically-plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli like faces at intermediate levels of the architecture and show why it does so. Thus the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. PMID:27916522

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

PubMed

Shimansky, Yury P; Kang, Tao; He, Jiping

2004-02-01

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments.

PubMed

Ramon Soria, Pablo; Arrue, Begoña C; Ollero, Anibal

2017-01-07

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors.

Professional Development of Faculty: How Do We Know It Is Effective?

NASA Astrophysics Data System (ADS)

Derting, T. L.; Ebert-May, D.; Hodder, J.

2011-12-01

Professional development (PD) of faculty has been an integral component of curriculum reform efforts in STEM. Traditionally, PD occurs through workshops that last from hours to several days. Regardless of the particular model of PD used during a workshop, its effectiveness is usually assessed through self-report surveys of faculty satisfaction, perceived learning, and reports of applications in faculty classrooms. My presentation focuses on ways of assessing the effectiveness of models of PD, with an emphasis on the need for objective measures of change in faculty teaching. The data that I present raise two significant questions about faculty PD. Are traditional approaches to faculty PD effective in changing classroom teaching practices and improving student learning? What evidence is needed to determine the effectiveness of different models of PD? Self-report data have been useful in identifying variables that can influence the extent to which faculty implement new teaching strategies. These variables include faculty beliefs about student learning, self-efficacy, level of dissatisfaction with student learning, departmental rewards for teaching and learning, time limitations, and peer interactions. Self-report data do not, however, provide a complete or necessarily accurate assessment of the impacts of PD on classroom practices and student learning. Objective assessment of teaching and learning is also necessary, yet seldom conducted. Two approaches to such assessment will be presented, one based on student performance and the other based on observations of faculty teaching. In multiple sections of a student-centered, inquiry-based course, learning gains were higher for students taught by faculty who were trained in student-centered teaching compared with faculty with no such training. In two national projects that focused on faculty PD, self-report data indicated that faculty increased their use of student-centered teaching following PD. Objective assessment measures, however, showed that most faculty actually used teacher-centered methods with only minor use of student-centered teaching practices. Moreover, variables that have been associated with change in teaching practices, or the lack thereof, contributed little to explaining observed classroom teaching practice after PD. For example, faculty with less teaching experience engaged in more student-centered teaching compared with faculty with more years of teaching experience. Also, departmental and peer support for faculty use of non-lecture approaches to teaching had no significant relationship with the classroom practices used by faculty. These and other data suggest that assumptions about the effectiveness of traditional models of PDs need to be validated using objective, as well as subjective measures. The data also indicate a need for new models of PD for STEM faculty.

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

The use of CORE model by metacognitive skill approach in developing characters junior high school students

NASA Astrophysics Data System (ADS)

Fisher, Dahlia; Yaniawati, Poppy; Kusumah, Yaya Sukjaya

2017-08-01

This study aims to analyze the character of students who obtain CORE learning model using metacognitive approach. The method in this study is qualitative research and quantitative research design (Mixed Method Design) with concurrent embedded strategy. The research was conducted on two groups: an experimental group and the control group. An experimental group consists of students who had CORE model learning using metacognitive approach while the control group consists of students taught by conventional learning. The study was conducted the object this research is the seventh grader students in one the public junior high schools in Bandung. Based on this research, it is known that the characters of the students in the CORE model learning through metacognitive approach is: honest, hard work, curious, conscientious, creative and communicative. Overall it can be concluded that CORE model learning is good for developing characters of a junior high school student.

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

NASA Astrophysics Data System (ADS)

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.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

PubMed Central

Grossberg, Stephen

2009-01-01

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing. PMID:19528003

Evaluation of an Instructional Model to Teach Clinically Relevant Medicinal Chemistry in a Campus and a Distance Pathway

PubMed Central

Galt, Kimberly A.

2008-01-01

Objectives To evaluate an instructional model for teaching clinically relevant medicinal chemistry. Methods An instructional model that uses Bloom's cognitive and Krathwohl's affective taxonomy, published and tested concepts in teaching medicinal chemistry, and active learning strategies, was introduced in the medicinal chemistry courses for second-professional year (P2) doctor of pharmacy (PharmD) students (campus and distance) in the 2005-2006 academic year. Student learning and the overall effectiveness of the instructional model were assessed. Student performance after introducing the instructional model was compared to that in prior years. Results Student performance on course examinations improved compared to previous years. Students expressed overall enthusiasm about the course and better understood the value of medicinal chemistry to clinical practice. Conclusion The explicit integration of the cognitive and affective learning objectives improved student performance, student ability to apply medicinal chemistry to clinical practice, and student attitude towards the discipline. Testing this instructional model provided validation to this theoretical framework. The model is effective for both our campus and distance-students. This instructional model may also have broad-based applications to other science courses. PMID:18483599

"Let's Get Physical": Advantages of a Physical Model over 3D Computer Models and Textbooks in Learning Imaging Anatomy

ERIC Educational Resources Information Center

Preece, Daniel; Williams, Sarah B.; Lam, Richard; Weller, Renate

2013-01-01

Three-dimensional (3D) information plays an important part in medical and veterinary education. Appreciating complex 3D spatial relationships requires a strong foundational understanding of anatomy and mental 3D visualization skills. Novel learning resources have been introduced to anatomy training to achieve this. Objective evaluation of their…

The Effects of Mathematical Modeling on Creative Production Ability and Self-Directed Learning Attitude

ERIC Educational Resources Information Center

Kim, Sun Hee; Kim, Soojin

2010-01-01

What should we do to educate the mathematically gifted and how should we do it? In this research, to satisfy diverse mathematical and cognitive demands of the gifted who have excellent learning ability and task tenacity in mathematics, we sought to apply mathematical modeling. One of the objectives of the gifted education in Korea is cultivating…

Summer Teacher Enhancement Institute for Science, Mathematics, and Technology Using the Problem-Based Learning Model

NASA Technical Reports Server (NTRS)

Petersen, Richard H.

1997-01-01

The objectives of the Institute were: (a) increase participants' content knowledge about aeronautics, science, mathematics, and technology, (b) model and promote the use of scientific inquiry through problem-based learning, (c) investigate the use of instructional technologies and their applications to curricula, and (d) encourage the dissemination of TEI experiences to colleagues, students, and parents.

Mobile Voting Systems for Creating Collaboration Environments and Getting Immediate Feedback: A New Curriculum Model of a University Lecture

ERIC Educational Resources Information Center

Titova, Svetlana; Talmo, Tord

2014-01-01

Mobile devices can enhance learning and teaching by providing instant feedback and better diagnosis of learning problems, helping design new assessment models, enhancing learner autonomy and creating new formats of enquiry-based activities. The objective of this paper is to investigate the pedagogical impact of mobile voting tools. The authors'…

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

ERIC Educational Resources Information Center

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…

Exploring In-Service Teachers' Perceptions on Values-Based Water Education via Interactive Instructional Strategies that Enhance Meaningful Learning

ERIC Educational Resources Information Center

Thoe, Ng Khar

2007-01-01

Instructional strategies determine the approaches an educator may take to achieve learning objectives. Research has shown that sets of strategies or instructional models anchored on social constructivist learning theories were found to be effective in enhancing active participation. It is particularly influential and meaningful in many areas of…

Understanding E-Learning Adoption in Brazil: Major Determinants and Gender Effects

ERIC Educational Resources Information Center

Okazaki, Shintaro; dos Santos, Luiz Miguel Renda

2012-01-01

The objective of this study is to examine factors influencing e-learning adoption and the moderating role of gender. This study extends the technology acceptance model (TAM) by adding attitude and social interaction. The new construct of social interaction is applied to the South American context. Gender effects on e-learning adoption from…

Collaborative Learning Utilizing a Domain-Based Shared Data Repository to Enhance Learning Outcomes

ERIC Educational Resources Information Center

Lubliner, David; Widmeyer, George; Deek, Fadi P.

2009-01-01

The objective of this study was to determine whether there was a quantifiable improvement in learning outcomes by integrating course materials in a 4-year baccalaureate program, utilizing a knowledge repository with a conceptual map that spans a discipline. Two new models were developed to provide the framework for this knowledge repository. A…

What Are the Effects of Implementing Learning-Focused Strategies in Biology and Physical Science Classrooms?

ERIC Educational Resources Information Center

Simmons, Robin

2013-01-01

The objective of this study was to determine if Learning-Focused Strategies (LFS) implemented in high school science courses would affect student achievement and the pass rate of biology and physical science Common District Assessments (CDAs). The LFS, specific teaching strategies contained in the Learning-Focused Strategies Model (LFSM) Program…

Preparing Scholars of Teaching and Learning Using a Model of Collaborative Peer Consulting and Action Research

ERIC Educational Resources Information Center

Waterman, Margaret; Weber, Janet; Pracht, Carl; Conway, Kathleen; Kunz, David; Evans, Beverly; Hoffman, Steven; Smentkowski, Brian; Starrett, David

2010-01-01

The Scholarship of Teaching and Learning (SoTL) Fellows Program at Southeast Missouri State University supports an annual cohort of 10 faculty Fellows to evaluate, through individual research projects, the effect of teaching on student learning of two or more of the university's General Education objectives. Designed around practical action…

Online Object Tracking, Learning and Parsing with And-Or Graphs.

PubMed

Wu, Tianfu; Lu, Yang; Zhu, Song-Chun

2017-12-01

This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks  , [3] , and the VOT benchmarks [4] -VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network   [5] , [6] . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.

Introducing Seismic Tomography with Computational Modeling

NASA Astrophysics Data System (ADS)

Neves, R.; Neves, M. L.; Teodoro, V.

2011-12-01

Learning seismic tomography principles and techniques involves advanced physical and computational knowledge. In depth learning of such computational skills is a difficult cognitive process that requires a strong background in physics, mathematics and computer programming. The corresponding learning environments and pedagogic methodologies should then involve sets of computational modelling activities with computer software systems which allow students the possibility to improve their mathematical or programming knowledge and simultaneously focus on the learning of seismic wave propagation and inverse theory. To reduce the level of cognitive opacity associated with mathematical or programming knowledge, several computer modelling systems have already been developed (Neves & Teodoro, 2010). Among such systems, Modellus is particularly well suited to achieve this goal because it is a domain general environment for explorative and expressive modelling with the following main advantages: 1) an easy and intuitive creation of mathematical models using just standard mathematical notation; 2) the simultaneous exploration of images, tables, graphs and object animations; 3) the attribution of mathematical properties expressed in the models to animated objects; and finally 4) the computation and display of mathematical quantities obtained from the analysis of images and graphs. Here we describe virtual simulations and educational exercises which enable students an easy grasp of the fundamental of seismic tomography. The simulations make the lecture more interactive and allow students the possibility to overcome their lack of advanced mathematical or programming knowledge and focus on the learning of seismological concepts and processes taking advantage of basic scientific computation methods and tools.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

PubMed Central

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning’s dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions. PMID:22746951

Group-Based Active Learning of Classification Models.

PubMed

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.

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

NASA Astrophysics Data System (ADS)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Exploiting range imagery: techniques and applications

NASA Astrophysics Data System (ADS)

Armbruster, Walter

2009-07-01

Practically no applications exist for which automatic processing of 2D intensity imagery can equal human visual perception. This is not the case for range imagery. The paper gives examples of 3D laser radar applications, for which automatic data processing can exceed human visual cognition capabilities and describes basic processing techniques for attaining these results. The examples are drawn from the fields of helicopter obstacle avoidance, object detection in surveillance applications, object recognition at high range, multi-object-tracking, and object re-identification in range image sequences. Processing times and recognition performances are summarized. The techniques used exploit the bijective continuity of the imaging process as well as its independence of object reflectivity, emissivity and illumination. This allows precise formulations of the probability distributions involved in figure-ground segmentation, feature-based object classification and model based object recognition. The probabilistic approach guarantees optimal solutions for single images and enables Bayesian learning in range image sequences. Finally, due to recent results in 3D-surface completion, no prior model libraries are required for recognizing and re-identifying objects of quite general object categories, opening the way to unsupervised learning and fully autonomous cognitive systems.

Applying learning theories and instructional design models for effective instruction.

PubMed

Khalil, Mohammed K; Elkhider, Ihsan A

2016-06-01

Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning outcomes, the science of instruction and instructional design models are used to guide the development of instructional design strategies that elicit appropriate cognitive processes. Here, the major learning theories are discussed and selected examples of instructional design models are explained. The main objective of this article is to present the science of learning and instruction as theoretical evidence for the design and delivery of instructional materials. In addition, this article provides a practical framework for implementing those theories in the classroom and laboratory. Copyright © 2016 The American Physiological Society.

On the potential for using immersive virtual environments to support laboratory experiment contextualisation

NASA Astrophysics Data System (ADS)

Machet, Tania; Lowe, David; Gütl, Christian

2012-12-01

This paper explores the hypothesis that embedding a laboratory activity into a virtual environment can provide a richer experimental context and hence improve the understanding of the relationship between a theoretical model and the real world, particularly in terms of the model's strengths and weaknesses. While an identified learning objective of laboratories is to support the understanding of the relationship between models and reality, the paper illustrates that this understanding is hindered by inherently limited experiments and that there is scope for improvement. Despite the contextualisation of learning activities having been shown to support learning objectives in many fields, there is traditionally little contextual information presented during laboratory experimentation. The paper argues that the enhancing laboratory activity with contextual information affords an opportunity to improve students' understanding of the relationship between the theoretical model and the experiment (which is effectively a proxy for the complex real world), thereby improving their understanding of the relationship between the model and reality. The authors propose that these improvements can be achieved by setting remote laboratories within context-rich virtual worlds.

Knowledge acquisition and learning process description in context of e-learning

NASA Astrophysics Data System (ADS)

Kiselev, B. G.; Yakutenko, V. A.; Yuriev, M. A.

2017-01-01

This paper investigates the problem of design of e-learning and MOOC systems. It describes instructional design-based approaches to e-learning systems design: IMS Learning Design, MISA and TELOS. To solve this problem we present Knowledge Field of Educational Environment with Competence boundary conditions - instructional engineering method for self-learning systems design. It is based on the simplified TELOS approach and enables a user to create their individual learning path by choosing prerequisite and target competencies. The paper provides the ontology model for the described instructional engineering method, real life use cases and the classification of the presented model. Ontology model consists of 13 classes and 15 properties. Some of them are inherited from Knowledge Field of Educational Environment and some are new and describe competence boundary conditions and knowledge validation objects. Ontology model uses logical constraints and is described using OWL 2 standard. To give TELOS users better understanding of our approach we list mapping between TELOS and KFEEC.

Developing Learning Objectives for a Model Course to Prepare Adults for the Assessment of Prior, Non-Sponsored Learning by Portfolio Evaluation.

ERIC Educational Resources Information Center

Stevens, Mary A.

A study was conducted in order to develop a systematic method for the evaluation of students' prior, non-sponsored learning for the award of college credit at Blackhawk College (Illinois). It was determined that a course designed to prepare the student for assessment of prior learning was the best way for the institution to provide assistance to…

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

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

NASA Astrophysics Data System (ADS)

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

NASA Astrophysics Data System (ADS)

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.

An Investigation of Difficulties Experienced by Students Developing Unified Modelling Language (UML) Class and Sequence Diagrams

ERIC Educational Resources Information Center

Sien, Ven Yu

2011-01-01

Object-oriented analysis and design (OOAD) is not an easy subject to learn. There are many challenges confronting students when studying OOAD. Students have particular difficulty abstracting real-world problems within the context of OOAD. They are unable to effectively build object-oriented (OO) models from the problem domain because they…

Supporting Collaborative Learning and Problem-Solving in a Constraint-Based CSCL Environment for UML Class Diagrams

ERIC Educational Resources Information Center

Baghaei, Nilufar; Mitrovic, Antonija; Irwin, Warwick

2007-01-01

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on…

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

PubMed

Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

2017-04-01

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

The Role of Multicultural Information in Experiential Learning

ERIC Educational Resources Information Center

Shen, Lan

2011-01-01

This paper is based on the author's empirical experience in assisting cultural immersion programs through developing multicultural collections, promoting diversity resources, and creating a supportive information environment for faculty and students. After summarizing the significance, goals, learning objectives, and program models of cultural…

Object Oriented Modeling and Design

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali

2007-01-01

The Object Oriented Modeling and Design seminar is intended for software professionals and students, it covers the concepts and a language-independent graphical notation that can be used to analyze problem requirements, and design a solution to the problem. The seminar discusses the three kinds of object-oriented models class, state, and interaction. The class model represents the static structure of a system, the state model describes the aspects of a system that change over time as well as control behavior and the interaction model describes how objects collaborate to achieve overall results. Existing knowledge of object oriented programming may benefit the learning of modeling and good design. Specific expectations are: Create a class model, Read, recognize, and describe a class model, Describe association and link, Show abstract classes used with multiple inheritance, Explain metadata, reification and constraints, Group classes into a package, Read, recognize, and describe a state model, Explain states and transitions, Read, recognize, and describe interaction model, Explain Use cases and use case relationships, Show concurrency in activity diagram, Object interactions in sequence diagram.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

Managing the travel model process : small and medium-sized MPOs. Instructor guide.

DOT National Transportation Integrated Search

2013-09-01

The learning objectives of this course were to: explain fundamental travel model concepts; describe the model development process; identify key inputs and describe the quality control process; and identify and manage resources.

Managing the travel model process : small and medium-sized MPOs. Participant handbook.

DOT National Transportation Integrated Search

2013-09-01

The learning objectives of this course were to: explain fundamental travel model concepts; describe the model development process; identify key inputs and describe the quality control process; and identify and manage resources.

Some problems with the design of self-learning management systems

NASA Technical Reports Server (NTRS)

Flikop, Ziny

1992-01-01

In this paper some problems in the design of management systems for complex objects are discussed. Considering the absence of adequate models and the fact that human expertise in the management of non-stationary objects becomes obsolete quickly, the use of self-learning together with a two-step optimization of on-line control rules is suggested. To prepare for the object analysis, a set of definitions has been proposed. Traditional and fuzzy sets approaches are used in the analysis. To decrease the reaction time of the control system, we propose the development of control rules without feedback.

How do children learn to follow gaze, share joint attention, imitate their teachers, and use tools during social interactions?

PubMed

Grossberg, Stephen; Vladusich, Tony

2010-01-01

How does an infant learn through visual experience to imitate actions of adult teachers, despite the fact that the infant and adult view one another and the world from different perspectives? To accomplish this, an infant needs to learn how to share joint attention with adult teachers and to follow their gaze towards valued goal objects. The infant also needs to be capable of view-invariant object learning and recognition whereby it can carry out goal-directed behaviors, such as the use of tools, using different object views than the ones that its teachers use. Such capabilities are often attributed to "mirror neurons". This attribution does not, however, explain the brain processes whereby these competences arise. This article describes the CRIB (Circular Reactions for Imitative Behavior) neural model of how the brain achieves these goals through inter-personal circular reactions. Inter-personal circular reactions generalize the intra-personal circular reactions of Piaget, which clarify how infants learn from their own babbled arm movements and reactive eye movements how to carry out volitional reaches, with or without tools, towards valued goal objects. The article proposes how intra-personal circular reactions create a foundation for inter-personal circular reactions when infants and other learners interact with external teachers in space. Both types of circular reactions involve learned coordinate transformations between body-centered arm movement commands and retinotopic visual feedback, and coordination of processes within and between the What and Where cortical processing streams. Specific breakdowns of model processes generate formal symptoms similar to clinical symptoms of autism. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

A Separable Two-Dimensional Random Field Model of Binary Response Data from Multi-Day Behavioral Experiments.

PubMed

Malem-Shinitski, Noa; Zhang, Yingzhuo; Gray, Daniel T; Burke, Sara N; Smith, Anne C; Barnes, Carol A; Ba, Demba

2018-04-18

The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups. Copyright © 2018. Published by Elsevier B.V.

Implementation of a flipped classroom educational model in a predoctoral dental course.

PubMed

Park, Sang E; Howell, T Howard

2015-05-01

This article describes the development and implementation of a flipped classroom model to promote student-centered learning as part of a predoctoral dental course. This model redesigns the traditional lecture-style classroom into a blended learning model that combines active learning pedagogy with instructional technology and "flips" the sequence so that students use online resources to learn content ahead of class and then use class time for discussion. The dental anatomy portion of a second-year DMD course at Harvard School of Dental Medicine was redesigned using the flipped classroom model. The 36 students in the course viewed online materials before class; then, during class, small groups of students participated in peer teaching and team discussions based on learning objectives under the supervision of faculty. The utilization of pre- and post-class quizzes as well as peer assessments were critical motivating factors that likely contributed to the increase in student participation in class and helped place learning accountability on the students. Student feedback from a survey after the experience was generally positive with regard to the collaborative and interactive aspects of this form of blended learning.

Atoms of recognition in human and computer vision.

PubMed

Ullman, Shimon; Assif, Liav; Fetaya, Ethan; Harari, Daniel

2016-03-08

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

Designing Mathematical Learning Environments for Teachers

ERIC Educational Resources Information Center

Madden, Sandra R.

2010-01-01

Technology use in mathematics often involves either exploratory or expressive modeling. When using exploratory models, students use technology to investigate a premade expert model of some phenomena. When creating expressive models, students have greater flexibility for constructing their own model for investigation using objects and mechanisms…

Bio-Inspired Neural Model for Learning Dynamic Models

NASA Technical Reports Server (NTRS)

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.

The Child as Econometrician: A Rational Model of Preference Understanding in Children

PubMed Central

Lucas, Christopher G.; Griffiths, Thomas L.; Xu, Fei; Fawcett, Christine; Gopnik, Alison; Kushnir, Tamar; Markson, Lori; Hu, Jane

2014-01-01

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding. PMID:24667309

The child as econometrician: a rational model of preference understanding in children.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Xu, Fei; Fawcett, Christine; Gopnik, Alison; Kushnir, Tamar; Markson, Lori; Hu, Jane

2014-01-01

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding.

School-Based Health Education Programmes, Health-Learning Capacity and Child Oral Health--related Quality of Life

ERIC Educational Resources Information Center

Freeman, Ruth; Gibson, Barry; Humphris, Gerry; Leonard, Helen; Yuan, Siyang; Whelton, Helen

2016-01-01

Objective: To use a model of health learning to examine the role of health-learning capacity and the effect of a school-based oral health education intervention (Winning Smiles) on the health outcome, child oral health-related quality of life (COHRQoL). Setting: Primary schools, high social deprivation, Ireland/Northern Ireland. Design: Cluster…

Information Processing Approaches to Cognitive Development

DTIC Science & Technology

1989-08-04

O’Connor (Eds.), Intelligence and learning . New York: Plenum Press. Deloache, J.S. (1988). The development of representation in young chidren . In H.W...Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, Learning , and Transfer. Cognitive Psychology, 20...Production system models of learning and development. Cambridge, MA: MIT Press. TWO KINDS OF INFORMATION PROCESSING APPROACHES TO COGNITIVE DEVELOPMENT

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

The Kinematic Learning Model using Video and Interfaces Analysis

NASA Astrophysics Data System (ADS)

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.

What can biochemistry students learn about protein translation? Using variation theory to explore the space of learning created by some common external representations

NASA Astrophysics Data System (ADS)

Bussey, Thomas J.

Biochemistry education relies heavily on students' ability to visualize abstract cellular and molecular processes, mechanisms, and components. As such, biochemistry educators often turn to external representations to provide tangible, working models from which students' internal representations (mental models) can be constructed, evaluated, and revised. However, prior research has shown that, while potentially beneficial, external representations can also lead to alternative student conceptions. Considering the breadth of biochemical phenomena, protein translation has been identified as an essential biochemical process and can subsequently be considered a fundamental concept for biochemistry students to learn. External representations of translation range from static diagrams to dynamic animations, from simplistic, stylized illustrations to more complex, realistic presentations. In order to explore the potential for student learning about protein translation from some common external representations of translation, I used variation theory. Variation theory offers a theoretical framework from which to explore what is intended for students to learn, what is possible for students to learn, and what students actually learn about an object of learning, e.g., protein translation. The goals of this project were threefold. First, I wanted to identify instructors' intentions for student learning about protein translation. From a phenomenographic analysis of instructor interviews, I was able to determine the critical features instructors felt their students should be learning. Second, I wanted to determine which features of protein translation were possible for students to learn from some common external representations of the process. From a variation analysis of the three representations shown to students, I was able to describe the possible combinations of features enacted by the sequential viewing of pairs of representations. Third, I wanted to identify what students actually learned about protein translation by viewing these external representations. From a phenomenographic analysis of student interviews, I was able to describe changes between students prior lived object of learning and their post lived object of learning. Based on the findings from this project, I can conclude that variation can be used to cue students to notice particular features of an external representation. Additionally, students' prior knowledge and, potentially, the intended objects of learning from previous instructors can also affect what students can learn from a representation. Finally, further study is needed to identify the extent to which mode and level of abstraction of an external representation affect student learning outcomes.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

DTIC Science & Technology

2014-05-16

ML-o-scope: a diagnostic visualization system for deep machine learning pipelines Daniel Bruckner Electrical Engineering and Computer Sciences... machine learning pipelines 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f...the system as a support for tuning large scale object-classification pipelines. 1 Introduction A new generation of pipelined machine learning models

Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars

NASA Astrophysics Data System (ADS)

Reis, Itamar; Poznanski, Dovi; Baron, Dalya; Zasowski, Gail; Shahaf, Sahar

2018-05-01

In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data.

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

PubMed Central

Weisswange, Thomas H.; Rothkopf, Constantin A.; Rodemann, Tobias; Triesch, Jochen

2011-01-01

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference. PMID:21750717

Learning and exploration in action-perception loops.

PubMed

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation.

PubMed

Leibo, Joel Z; Liao, Qianli; Anselmi, Fabio; Freiwald, Winrich A; Poggio, Tomaso

2017-01-09

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and robust against identity-preserving transformations, like depth rotations [1, 2]. Current computational models of object recognition, including recent deep-learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations [3-6]. Here, we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules generate approximate invariance to identity-preserving transformations at the top level of the processing hierarchy. However, all past models tested failed to reproduce the most salient property of an intermediate representation of a three-level face-processing hierarchy in the brain: mirror-symmetric tuning to head orientation [7]. Here, we demonstrate that one specific biologically plausible Hebb-type learning rule generates mirror-symmetric tuning to bilaterally symmetric stimuli, like faces, at intermediate levels of the architecture and show why it does so. Thus, the tuning properties of individual cells inside the visual stream appear to result from group properties of the stimuli they encode and to reflect the learning rules that sculpted the information-processing system within which they reside. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning multisensory representations for auditory-visual transfer of sequence category knowledge: a probabilistic language of thought approach.

PubMed

Yildirim, Ilker; Jacobs, Robert A

2015-06-01

If a person is trained to recognize or categorize objects or events using one sensory modality, the person can often recognize or categorize those same (or similar) objects and events via a novel modality. This phenomenon is an instance of cross-modal transfer of knowledge. Here, we study the Multisensory Hypothesis which states that people extract the intrinsic, modality-independent properties of objects and events, and represent these properties in multisensory representations. These representations underlie cross-modal transfer of knowledge. We conducted an experiment evaluating whether people transfer sequence category knowledge across auditory and visual domains. Our experimental data clearly indicate that we do. We also developed a computational model accounting for our experimental results. Consistent with the probabilistic language of thought approach to cognitive modeling, our model formalizes multisensory representations as symbolic "computer programs" and uses Bayesian inference to learn these representations. Because the model demonstrates how the acquisition and use of amodal, multisensory representations can underlie cross-modal transfer of knowledge, and because the model accounts for subjects' experimental performances, our work lends credence to the Multisensory Hypothesis. Overall, our work suggests that people automatically extract and represent objects' and events' intrinsic properties, and use these properties to process and understand the same (and similar) objects and events when they are perceived through novel sensory modalities.

Learning Object Repositories

ERIC Educational Resources Information Center

Lehman, Rosemary

2007-01-01

This chapter looks at the development and nature of learning objects, meta-tagging standards and taxonomies, learning object repositories, learning object repository characteristics, and types of learning object repositories, with type examples. (Contains 1 table.)

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

CAN-Care: an innovative model of practice-based learning.

PubMed

Raines, Deborah A

2006-01-01

The "Collaborative Approach to Nursing Care" (CAN-Care) Model of practice-based education is designed to meet the unique learning needs of the accelerated nursing program student. The model is based on a synergistic partnership between the academic and service settings, the vision of which is to create an innovative practice-based learning model, resulting in a positive experience for both the student and unit-based nurse. Thus, the objectives of quality outcomes for both the college and Health Care Organization are fulfilled. Specifically, the goal is the education of nurses ready to meet the challenges of caring for persons in the complex health care environment of the 21st century.

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

NASA Astrophysics Data System (ADS)

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.

Learning-based stochastic object models for use in optimizing imaging systems

NASA Astrophysics Data System (ADS)

Dolly, Steven R.; Anastasio, Mark A.; Yu, Lifeng; Li, Hua

2017-03-01

It is widely known that the optimization of imaging systems based on objective, or task-based, measures of image quality via computer-simulation requires use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in anatomy within a specified ensemble of patients remains a challenging task. Because they are established by use of image data corresponding a single patient, previously reported numerical anatomical models lack of the ability to accurately model inter- patient variations in anatomy. In certain applications, however, databases of high-quality volumetric images are available that can facilitate this task. In this work, a novel and tractable methodology for learning a SOM from a set of volumetric training images is developed. The proposed method is based upon geometric attribute distribution (GAD) models, which characterize the inter-structural centroid variations and the intra-structural shape variations of each individual anatomical structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations learned from training data. By use of the GAD models, random organ shapes and positions can be generated and integrated to form an anatomical phantom. The randomness in organ shape and position will reflect the variability of anatomy present in the training data. To demonstrate the methodology, a SOM corresponding to the pelvis of an adult male was computed and a corresponding ensemble of phantoms was created. Additionally, computer-simulated X-ray projection images corresponding to the phantoms were computed, from which tomographic images were reconstructed.

Enhancing Undergraduate Agro-Ecological Laboratory Employment through Experiential Learning

ERIC Educational Resources Information Center

Grossman, J. M.; Patel, M.; Drinkwater, L. E.

2010-01-01

We piloted an educational model, the Sustainable Agriculture Scholars Program, linking research in organic agriculture to experiential learning activities for summer undergraduate employees in 2007 and 2008. Our objectives were to: (1) further student understanding of sustainable agriculture research, (2) increase student interest in sustainable…

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments

PubMed Central

Ramon Soria, Pablo; Arrue, Begoña C.; Ollero, Anibal

2017-01-01

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors. PMID:28067851

Impaired Value Learning for Faces in Preschoolers With Autism Spectrum Disorder.

PubMed

Wang, Quan; DiNicola, Lauren; Heymann, Perrine; Hampson, Michelle; Chawarska, Katarzyna

2018-01-01

One of the common findings in autism spectrum disorder (ASD) is limited selective attention toward social objects, such as faces. Evidence from both human and nonhuman primate studies suggests that selection of objects for processing is guided by the appraisal of object values. We hypothesized that impairments in selective attention in ASD may reflect a disruption of a system supporting learning about object values in the social domain. We examined value learning in social (faces) and nonsocial (fractals) domains in preschoolers with ASD (n = 25) and typically developing (TD) controls (n = 28), using a novel value learning task implemented on a gaze-contingent eye-tracking platform consisting of value learning and a selective attention choice test. Children with ASD performed more poorly than TD controls on the social value learning task, but both groups performed similarly on the nonsocial task. Within-group comparisons indicated that value learning in TD children was enhanced on the social compared to the nonsocial task, but no such enhancement was seen in children with ASD. Performance in the social and nonsocial conditions was correlated in the ASD but not in the TD group. The study provides support for a domain-specific impairment in value learning for faces in ASD, and suggests that, in ASD, value learning in social and nonsocial domains may rely on a shared mechanism. These findings have implications both for models of selective social attention deficits in autism and for identification of novel treatment targets. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A neurocomputational account of taxonomic responding and fast mapping in early word learning.

PubMed

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to the quality of prelexical, categorical representations in the model. We show how synaptogenesis supports coherent generalization of word-object associations and show that later synaptic pruning minimizes metabolic costs without being detrimental to word learning. The role played by joint-attentional activities is identified in the model, both at the level of selecting efficient cross-modal synapses and at the behavioral level, by accelerating and refining overall vocabulary acquisition. The model can account for the qualitative shift in the way infants use words, from an associative to a referential-like use, for the pattern of overextension errors in production and comprehension observed during early childhood and typicality effects observed in lexical development. Interesting by-products of the model include a potential explanation of the shift from prototype to exemplar-based effects reported for adult category formation, an account of mispronunciation effects in early lexical development, and extendability to include accounts of individual differences in lexical development and specific disorders such as Williams syndrome. The model demonstrates how an established constraint on lexical learning, which has often been regarded as domain-specific, can emerge from domain-general learning principles that are simultaneously biologically, psychologically, and socially plausible.

Goal-Proximity Decision-Making

ERIC Educational Resources Information Center

Veksler, Vladislav D.; Gray, Wayne D.; Schoelles, Michael J.

2013-01-01

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed…

Predictive Models of target organ and Systemic toxicities (BOSC)

EPA Science Inventory

The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

Learning, Attention/Hyperactivity, and Conduct Problems as Sequelae of Excessive Daytime Sleepiness in a General Population Study of Young Children

PubMed Central

Calhoun, Susan L.; Fernandez-Mendoza, Julio; Vgontzas, Alexandros N.; Mayes, Susan D.; Tsaoussoglou, Marina; Rodriguez-Muñoz, Alfredo; Bixler, Edward O.

2012-01-01

Study Objectives: Although excessive daytime sleepiness (EDS) is a common problem in children, with estimates of 15%; few studies have investigated the sequelae of EDS in young children. We investigated the association of EDS with objective neurocognitive measures and parent reported learning, attention/hyperactivity, and conduct problems in a large general population sample of children. Design: Cross-sectional. Setting: Population based. Participants: 508 children from The Penn State Child Cohort. Interventions: N/A. Measurements and Results: Children underwent a 9-h polysomnogram, comprehensive neurocognitive testing, and parent rating scales. Children were divided into 2 groups: those with and without parent-reported EDS. Structural equation modeling was used to examine whether processing speed and working memory performance would mediate the relationship between EDS and learning, attention/hyperactivity, and conduct problems. Logistic regression models suggest that parent-reported learning, attention/hyperactivity, and conduct problems, as well as objective measurement of processing speed and working memory are significant sequelae of EDS, even when controlling for AHI and objective markers of sleep. Path analysis demonstrates that processing speed and working memory performance are strong mediators of the association of EDS with learning and attention/hyperactivity problems, while to a slightly lesser degree are mediators from EDS to conduct problems. Conclusions: This study suggests that in a large general population sample of young children, parent-reported EDS is associated with neurobehavioral (learning, attention/hyperactivity, conduct) problems and poorer performance in processing speed and working memory. Impairment due to EDS in daytime cognitive and behavioral functioning can have a significant impact on children's development. Citation: Calhoun SL; Fernandez-Mendoza J; Vgontzas AN; Mayes SD; Tsaoussoglou M; Rodriguez-Muñoz A; Bixler EO. Learning, attention/hyperactivity, and conduct problems as sequelae of excessive daytime sleepiness in a general population study of young children. SLEEP 2012;35(5):627-632. PMID:22547888

Mere exposure alters category learning of novel objects.

PubMed

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.

Mere Exposure Alters Category Learning of Novel Objects

PubMed Central

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. PMID:21833209

Training Technology Handbook Development. Phase I. Annotated Literature Review.

DTIC Science & Technology

1981-08-01

chief means for currently influencing the students , learning is through the sequencing of instruction . Use of the findings, models, and theories from...determine what aspects of the learning experience might influence student attitudes toward computer-assisted instruction (CAI). Sixty-four randomly...learners seem to learn most efficiently when left alone with the instructional objective and the necessary materials. The middle aptitude trainees appear to

Assessing the Effectiveness of STAD Model and Problem Based Learning in Mathematics Learning Achievement and Problem Solving Ability

ERIC Educational Resources Information Center

Rattanatumma, Tawachai; Puncreobutr, Vichian

2016-01-01

The objective of this study was to compare the effectiveness of teaching methods in improving Mathematics Learning Achievement and Problem solving ability of students at an international college. This is a Quasi-Experimental Research which was done the study with the first year students who have registered to study Mathematics subject at St.…

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

PubMed Central

Erdogan, Goker; Yildirim, Ilker; Jacobs, Robert A.

2015-01-01

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception. PMID:26554704

Frequency-specific hippocampal-prefrontal interactions during associative learning

PubMed Central

Brincat, Scott L.; Miller, Earl K.

2015-01-01

Much of our knowledge of the world depends on learning associations (e.g., face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive/mnemonic abilities are akin to humans. Here, we show functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object-pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, while HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences, and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC, and increased with learning. Rapid object associative learning may occur in PFC, while HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. PMID:25706471

A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection.

PubMed

Li, Jia; Xia, Changqun; Chen, Xiaowu

2017-10-12

Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos. From the user data, we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for videobased salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliencyguided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at the pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are constructed in an unsupervised manner that automatically infers a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. In experiments, the proposed unsupervised approach is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 imagebased classic (unsupervised or non-deep learning) models, six image-based deep learning models, and five video-based unsupervised models. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.

Monocular Depth Perception and Robotic Grasping of Novel Objects

DTIC Science & Technology

2009-06-01

resulting algorithm is able to learn monocular vision cues that accurately estimate the relative depths of obstacles in a scene. Reinforcement learning ... learning still make sense in these settings? Since many of the cues that are useful for estimating depth can be re-created in synthetic images, we...supervised learning approach to this problem, and use a Markov Random Field (MRF) to model the scene depth as a function of the image features. We show

Biased Competition in Visual Processing Hierarchies: A Learning Approach Using Multiple Cues.

PubMed

Gepperth, Alexander R T; Rebhan, Sven; Hasler, Stephan; Fritsch, Jannik

2011-03-01

In this contribution, we present a large-scale hierarchical system for object detection fusing bottom-up (signal-driven) processing results with top-down (model or task-driven) attentional modulation. Specifically, we focus on the question of how the autonomous learning of invariant models can be embedded into a performing system and how such models can be used to define object-specific attentional modulation signals. Our system implements bi-directional data flow in a processing hierarchy. The bottom-up data flow proceeds from a preprocessing level to the hypothesis level where object hypotheses created by exhaustive object detection algorithms are represented in a roughly retinotopic way. A competitive selection mechanism is used to determine the most confident hypotheses, which are used on the system level to train multimodal models that link object identity to invariant hypothesis properties. The top-down data flow originates at the system level, where the trained multimodal models are used to obtain space- and feature-based attentional modulation signals, providing biases for the competitive selection process at the hypothesis level. This results in object-specific hypothesis facilitation/suppression in certain image regions which we show to be applicable to different object detection mechanisms. In order to demonstrate the benefits of this approach, we apply the system to the detection of cars in a variety of challenging traffic videos. Evaluating our approach on a publicly available dataset containing approximately 3,500 annotated video images from more than 1 h of driving, we can show strong increases in performance and generalization when compared to object detection in isolation. Furthermore, we compare our results to a late hypothesis rejection approach, showing that early coupling of top-down and bottom-up information is a favorable approach especially when processing resources are constrained.

Classroom Assessment Techniques: A Conceptual Model for CATs in the Online Classroom

ERIC Educational Resources Information Center

Bergquist, Emily; Holbeck, Rick

2014-01-01

Formative assessments are an important part of the teaching and learning cycle. Instructors need to monitor student learning and check for understanding throughout the instructional phase of teaching to confirm that students understand the objective before embarking on the summative assessment. Typically, online classrooms are developed with…

Constructivist Learning of Anatomy: Gaining Knowledge by Creating Anatomical Casts

ERIC Educational Resources Information Center

Hermiz, David J.; O'Sullivan, Daniel J.; Lujan, Heidi L.; DiCarlo, Stephen E.

2011-01-01

Educators are encouraged to provide inquiry-based, collaborative, and problem solving activities that enhance learning and promote curiosity, skepticism, objectivity, and the use of scientific reasoning. Making anatomical casts or models by injecting solidifying substances into organs is an example of a constructivist activity for achieving these…

Learning through Transitions: The Role of Institutions

ERIC Educational Resources Information Center

Zittoun, Tania

2008-01-01

In this paper two models are proposed for analysing transitions in education. Firstly, transitions are the processes that follow ruptures perceived by people. They include learning, identity change, and meaning making processes. Secondly, processes of change are observed through a semiotic prism, articulating self-other-object-sense of the object…

The Economics of Time in Learning.

ERIC Educational Resources Information Center

Christoffersson, Nils-Olaf

The use of a mathematical model supported by empirical findings had developed a method of cost effectiveness that can be used in evaluations between educational objectives and goals. Educational time allocation can be studied and developed into a micro-level economic theory of decision. Learning has been defined as increments which can be…

Secondary Social Studies: Alaska Curriculum Guide. Second Edition.

ERIC Educational Resources Information Center

Alaska State Dept. of Education, Juneau. Office of Curriculum Services.

A secondary social studies model curriculum guide for Alaska is presented. The body of the guide lists topics/concepts, learning outcomes/objectives, and sample learning activities in a 3 column format. The first column, topics/concepts, describes the content area, defining the subject broadly and listing subconcepts or associated vocabulary. The…

Learning Portfolio Analysis and Mining for SCORM Compliant Environment

ERIC Educational Resources Information Center

Su, Jun-Ming; Tseng, Shian-Shyong; Wang, Wei; Weng, Jui-Feng; Yang, Jin Tan David; Tsai, Wen-Nung

2006-01-01

With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM) 2004 provides the Sequencing and Navigation (SN) Specification to define the course sequencing behavior, control the sequencing, selecting and delivering of course, and organize the content into a…

Service Learning in Schools: Training Counselors for Group Work

ERIC Educational Resources Information Center

Bjornestad, Andrea; Mims, Grace Ann; Mims, Matthew

2016-01-01

The purpose of the study was to explore the experiences of counselors-in-training via student reflection journals as part of a service-learning project in a group counseling course. The counselors-in-training facilitated psychoeducational groups at an alternative high school. The Objective, Reflective, Interpretive, Decisional model was utilized…

Learning Processes and Approaches: Examining Their Interrelationships to Understand Student Learning

ERIC Educational Resources Information Center

Chennamsetti, Prashanti

2008-01-01

The purpose of this paper is to explore the relationship between two contrasting research paradigms, namely, cognitive and experiential research, a significant literature review previously unaddressed. To achieve this objective, a conceptual description of three theoretical frameworks, Dual-Store model, Levels of Processing (LOP; drawn from…

Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications

PubMed Central

Moreno-Roldán, José-Miguel; Luque-Nieto, Miguel-Ángel; Poncela, Javier; Otero, Pablo

2017-01-01

Video services are meant to be a fundamental tool in the development of oceanic research. The current technology for underwater networks (UWNs) imposes strong constraints in the transmission capacity since only a severely limited bitrate is available. However, previous studies have shown that the quality of experience (QoE) is enough for ocean scientists to consider the service useful, although the perceived quality can change significantly for small ranges of variation of video parameters. In this context, objective video quality assessment (VQA) methods become essential in network planning and real time quality adaptation fields. This paper presents two specialized models for objective VQA, designed to match the special requirements of UWNs. The models are built upon machine learning techniques and trained with actual user data gathered from subjective tests. Our performance analysis shows how both of them can successfully estimate quality as a mean opinion score (MOS) value and, for the second model, even compute a distribution function for user scores. PMID:28333123

Inferring Interaction Force from Visual Information without Using Physical Force Sensors.

PubMed

Hwang, Wonjun; Lim, Soo-Chul

2017-10-26

In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

Scaling up spike-and-slab models for unsupervised feature learning.

PubMed

Goodfellow, Ian J; Courville, Aaron; Bengio, Yoshua

2013-08-01

We describe the use of two spike-and-slab models for modeling real-valued data, with an emphasis on their applications to object recognition. The first model, which we call spike-and-slab sparse coding (S3C), is a preexisting model for which we introduce a faster approximate inference algorithm. We introduce a deep variant of S3C, which we call the partially directed deep Boltzmann machine (PD-DBM) and extend our S3C inference algorithm for use on this model. We describe learning procedures for each. We demonstrate that our inference procedure for S3C enables scaling the model to unprecedented large problem sizes, and demonstrate that using S3C as a feature extractor results in very good object recognition performance, particularly when the number of labeled examples is low. We show that the PD-DBM generates better samples than its shallow counterpart, and that unlike DBMs or DBNs, the PD-DBM may be trained successfully without greedy layerwise training.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

Interactive Inverse Groundwater Modeling - Addressing User Fatigue

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B. S.

2006-12-01

This paper builds on ongoing research on developing an interactive and multi-objective framework to solve the groundwater inverse problem. In this work we solve the classic groundwater inverse problem of estimating a spatially continuous conductivity field, given field measurements of hydraulic heads. The proposed framework is based on an interactive multi-objective genetic algorithm (IMOGA) that not only considers quantitative measures such as calibration error and degree of regularization, but also takes into account expert knowledge about the structure of the underlying conductivity field expressed as subjective rankings of potential conductivity fields by the expert. The IMOGA converges to the optimal Pareto front representing the best trade- off among the qualitative as well as quantitative objectives. However, since the IMOGA is a population-based iterative search it requires the user to evaluate hundreds of solutions. This leads to the problem of 'user fatigue'. We propose a two step methodology to combat user fatigue in such interactive systems. The first step is choosing only a few highly representative solutions to be shown to the expert for ranking. Spatial clustering is used to group the search space based on the similarity of the conductivity fields. Sampling is then carried out from different clusters to improve the diversity of solutions shown to the user. Once the expert has ranked representative solutions from each cluster a machine learning model is used to 'learn user preference' and extrapolate these for the solutions not ranked by the expert. We investigate different machine learning models such as Decision Trees, Bayesian learning model, and instance based weighting to model user preference. In addition, we also investigate ways to improve the performance of these models by providing information about the spatial structure of the conductivity fields (which is what the expert bases his or her rank on). Results are shown for each of these machine learning models and the advantages and disadvantages for each approach are discussed. These results indicate that using the proposed two-step methodology leads to significant reduction in user-fatigue without deteriorating the solution quality of the IMOGA.

Learning visuomotor transformations for gaze-control and grasping.

PubMed

Hoffmann, Heiko; Schenck, Wolfram; Möller, Ralf

2005-08-01

For reaching to and grasping of an object, visual information about the object must be transformed into motor or postural commands for the arm and hand. In this paper, we present a robot model for visually guided reaching and grasping. The model mimics two alternative processing pathways for grasping, which are also likely to coexist in the human brain. The first pathway directly uses the retinal activation to encode the target position. In the second pathway, a saccade controller makes the eyes (cameras) focus on the target, and the gaze direction is used instead as positional input. For both pathways, an arm controller transforms information on the target's position and orientation into an arm posture suitable for grasping. For the training of the saccade controller, we suggest a novel staged learning method which does not require a teacher that provides the necessary motor commands. The arm controller uses unsupervised learning: it is based on a density model of the sensor and the motor data. Using this density, a mapping is achieved by completing a partially given sensorimotor pattern. The controller can cope with the ambiguity in having a set of redundant arm postures for a given target. The combined model of saccade and arm controller was able to fixate and grasp an elongated object with arbitrary orientation and at arbitrary position on a table in 94% of trials.

A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots.

PubMed

Gutiérrez, Marco A; Manso, Luis J; Pandya, Harit; Núñez, Pedro

2017-02-11

Object detection and classification have countless applications in human-robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found. Ambient intelligence systems equipped with different sensors can also benefit from the ability to find objects, enabling them to inform humans about their location. For these applications to succeed, systems need to detect the objects that may potentially contain other objects, working with relatively low-resolution sensor data. A passive learning architecture for sensors has been designed in order to take advantage of multimodal information, obtained using an RGB-D camera and trained semantic language models. The main contribution of the architecture lies in the improvement of the performance of the sensor under conditions of low resolution and high light variations using a combination of image labeling and word semantics. The tests performed on each of the stages of the architecture compare this solution with current research labeling techniques for the application of an autonomous social robot working in an apartment. The results obtained demonstrate that the proposed sensor architecture outperforms state-of-the-art approaches.

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge.

PubMed

Navarrete, Jairo A; Dartnell, Pablo

2017-08-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called "flexibility" whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena.

Towards a category theory approach to analogy: Analyzing re-representation and acquisition of numerical knowledge

PubMed Central

2017-01-01

Category Theory, a branch of mathematics, has shown promise as a modeling framework for higher-level cognition. We introduce an algebraic model for analogy that uses the language of category theory to explore analogy-related cognitive phenomena. To illustrate the potential of this approach, we use this model to explore three objects of study in cognitive literature. First, (a) we use commutative diagrams to analyze an effect of playing particular educational board games on the learning of numbers. Second, (b) we employ a notion called coequalizer as a formal model of re-representation that explains a property of computational models of analogy called “flexibility” whereby non-similar representational elements are considered matches and placed in structural correspondence. Finally, (c) we build a formal learning model which shows that re-representation, language processing and analogy making can explain the acquisition of knowledge of rational numbers. These objects of study provide a picture of acquisition of numerical knowledge that is compatible with empirical evidence and offers insights on possible connections between notions such as relational knowledge, analogy, learning, conceptual knowledge, re-representation and procedural knowledge. This suggests that the approach presented here facilitates mathematical modeling of cognition and provides novel ways to think about analogy-related cognitive phenomena. PMID:28841643

Virtual Patients in Primary Care: Developing a Reusable Model That Fosters Reflective Practice and Clinical Reasoning

PubMed Central

Zary, Nabil; Björklund, Karin; Toth-Pal, Eva; Leanderson, Charlotte

2014-01-01

Background Primary care is an integral part of the medical curriculum at Karolinska Institutet, Sweden. It is present at every stage of the students’ education. Virtual patients (VPs) may support learning processes and be a valuable complement in teaching communication skills, patient-centeredness, clinical reasoning, and reflective thinking. Current literature on virtual patients lacks reports on how to design and use virtual patients with a primary care perspective. Objective The objective of this study was to create a model for a virtual patient in primary care that facilitates medical students’ reflective practice and clinical reasoning. The main research question was how to design a virtual patient model with embedded process skills suitable for primary care education. Methods The VP model was developed using the Open Tufts University Sciences Knowledgebase (OpenTUSK) virtual patient system as a prototyping tool. Both the VP model and the case created using the developed model were validated by a group of 10 experienced primary care physicians and then further improved by a work group of faculty involved in the medical program. The students’ opinions on the VP were investigated through focus group interviews with 14 students and the results analyzed using content analysis. Results The VP primary care model was based on a patient-centered model of consultation modified according to the Calgary-Cambridge Guides, and the learning outcomes of the study program in medicine were taken into account. The VP primary care model is based on Kolb’s learning theories and consists of several learning cycles. Each learning cycle includes a didactic inventory and then provides the student with a concrete experience (video, pictures, and other material) and preformulated feedback. The students’ learning process was visualized by requiring the students to expose their clinical reasoning and reflections in-action in every learning cycle. Content analysis of the focus group interviews showed good acceptance of the model by students. The VP was regarded as an intermediate learning activity and a complement to both the theoretical and the clinical part of the education, filling out gaps in clinical knowledge. The content of the VP case was regarded as authentic and the students appreciated the immediate feedback. The students found the structure of the model interactive and easy to follow. The students also reported that the VP case supported their self-directed learning and reflective ability. Conclusions We have built a new VP model for primary care with embedded communication training and iterated learning cycles that in pilot testing showed good acceptance by students, supporting their self-directed learning and reflective thinking. PMID:24394603

What is the impact of professional learning on physical activity interventions among preschool children? A systematic review.

PubMed

Peden, M E; Okely, A D; Eady, M J; Jones, R A

2018-05-31

The purpose of this systematic review was to investigate professional learning models (length, mode, content) offered as part of objectively measured physical childcare-based interventions. A systematic review of eight electronic databases was conducted to June 2017. Only English, peer-reviewed studies that evaluated childcare-based physical activity interventions, incorporated professional learning and reported objectively measured physical activity were included. Study designs included randomized controlled trails, cluster randomized trials, experimental or pilot studies. The search identified 11 studies. Ten studies objectively measured physical activity using accelerometers; five studies used both accelerometer and direct observation tools and one study measured physical activity using direct observation only. Seven of these studies reported statistically significant intervention effects. Only six studies described all components of professional learning, but only two studies reported specific professional learning outcomes and physical activity outcomes. No patterns were identified between the length, mode and content of professional learning and children's physical activity outcomes in childcare settings. Educators play a critical role in modifying children's levels of physical activity in childcare settings. The findings of this review suggest that professional learning offered as part of a physical activity intervention that potentially impacts on children's physical activity outcomes remains under-reported. © 2018 World Obesity Federation.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies.

PubMed

Miñano Pérez, Pablo; Castejón Costa, Juan-Luis; Gilar Corbí, Raquel

2012-03-01

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

An error-tuned model for sensorimotor learning

PubMed Central

Sadeghi, Mohsen; Wolpert, Daniel M.

2017-01-01

Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom-up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can vary. In the model, visual information regarding the context (the orientation of the object) allows the appropriate primitives to be engaged. This top-down module selection is implemented by a Gaussian function tuned for the visual orientation of the object. Second, each module's contribution adapts across trials in proportion to its ability to decrease the current kinematic error. Specifically, adaptation is implemented by cosine tuning of primitives to the current direction of the error, which we show to be theoretically optimal for reducing error. This error-tuned model makes two novel predictions. First, interference should occur between alternating dynamics only when the kinematic errors associated with each oppose one another. In contrast, dynamics which lead to orthogonal errors should not interfere. Second, kinematic errors alone should be sufficient to engage the appropriate modules, even in the absence of contextual information normally provided by vision. We confirm both these predictions experimentally and show that the model can also account for data from previous experiments. Our results suggest that two interacting processes account for module selection during sensorimotor control and learning. PMID:29253869

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

The role of partial knowledge in statistical word learning

PubMed Central

Fricker, Damian C.; Yu, Chen; Smith, Linda B.

2013-01-01

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980

Common world model for unmanned systems

NASA Astrophysics Data System (ADS)

Dean, Robert Michael S.

2013-05-01

The Robotic Collaborative Technology Alliance (RCTA) seeks to provide adaptive robot capabilities which move beyond traditional metric algorithms to include cognitive capabilities. Key to this effort is the Common World Model, which moves beyond the state-of-the-art by representing the world using metric, semantic, and symbolic information. It joins these layers of information to define objects in the world. These objects may be reasoned upon jointly using traditional geometric, symbolic cognitive algorithms and new computational nodes formed by the combination of these disciplines. The Common World Model must understand how these objects relate to each other. Our world model includes the concept of Self-Information about the robot. By encoding current capability, component status, task execution state, and histories we track information which enables the robot to reason and adapt its performance using Meta-Cognition and Machine Learning principles. The world model includes models of how aspects of the environment behave, which enable prediction of future world states. To manage complexity, we adopted a phased implementation approach to the world model. We discuss the design of "Phase 1" of this world model, and interfaces by tracing perception data through the system from the source to the meta-cognitive layers provided by ACT-R and SS-RICS. We close with lessons learned from implementation and how the design relates to Open Architecture.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

Learning Application of Astronomy Based Augmented Reality using Android Platform

NASA Astrophysics Data System (ADS)

Maleke, B.; Paseru, D.; Padang, R.

2018-02-01

Astronomy is a branch of science involving observations of celestial bodies such as stars, planets, nebular comets, star clusters, and galaxies as well as natural phenomena occurring outside the Earth’s atmosphere. The way of learning of Astronomy is quite varied, such as by using a book or observe directly with a telescope. But both ways of learning have shortcomings, for example learning through books is only presented in the form of interesting 2D drawings. While learning with a telescope requires a fairly expensive cost to buy the equipment. This study will present a more interesting way of learning from the previous one, namely through Augmented Reality (AR) application using Android platform. Augmented Reality is a combination of virtual world (virtual) and real world (real) made by computer. Virtual objects can be text, animation, 3D models or videos that are combined with the actual environment so that the user feels the virtual object is in his environment. With the use of the Android platform, this application makes the learning method more interesting because it can be used on various Android smartphones so that learning can be done anytime and anywhere. The methodology used in making applications is Multimedia Lifecycle, along with C # language for AR programming and flowchart as a modelling tool. The results of research on some users stated that this application can run well and can be used as an alternative way of learning Astronomy with more interesting.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Detecting overlapping instances in microscopy images using extremal region trees.

PubMed

Arteta, Carlos; Lempitsky, Victor; Noble, J Alison; Zisserman, Andrew

2016-01-01

In many microscopy applications the images may contain both regions of low and high cell densities corresponding to different tissues or colonies at different stages of growth. This poses a challenge to most previously developed automated cell detection and counting methods, which are designed to handle either the low-density scenario (through cell detection) or the high-density scenario (through density estimation or texture analysis). The objective of this work is to detect all the instances of an object of interest in microscopy images. The instances may be partially overlapping and clustered. To this end we introduce a tree-structured discrete graphical model that is used to select and label a set of non-overlapping regions in the image by a global optimization of a classification score. Each region is labeled with the number of instances it contains - for example regions can be selected that contain two or three object instances, by defining separate classes for tuples of objects in the detection process. We show that this formulation can be learned within the structured output SVM framework and that the inference in such a model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations - a dot on each instance. The candidate regions for the selection are obtained as extremal region of a surface computed from the microscopy image, and we show that the performance of the model can be improved by considering a proxy problem for learning the surface that allows better selection of the extremal regions. Furthermore, we consider a number of variations for the loss function used in the structured output learning. The model is applied and evaluated over six quite disparate data sets of images covering: fluorescence microscopy, weak-fluorescence molecular images, phase contrast microscopy and histopathology images, and is shown to exceed the state of the art in performance. Copyright © 2015 Elsevier B.V. All rights reserved.

Using optimization models to demonstrate the need for structural changes in training programs for surgical medical residents.

PubMed

Turner, Jonathan; Kim, Kibaek; Mehrotra, Sanjay; DaRosa, Debra A; Daskin, Mark S; Rodriguez, Heron E

2013-09-01

The primary goal of a residency program is to prepare trainees for unsupervised care. Duty hour restrictions imposed throughout the prior decade require that residents work significantly fewer hours. Moreover, various stakeholders (e.g. the hospital, mentors, other residents, educators, and patients) require them to prioritize very different activities, often conflicting with their learning goals. Surgical residents' learning goals include providing continuity throughout a patient's pre-, peri-, and post-operative care as well as achieving sufficient surgical experience levels in various procedure types and participating in various formal educational activities, among other things. To complicate matters, senior residents often compete with other residents for surgical experience. This paper features experiments using an optimization model and a real dataset. The experiments test the viability of achieving the above goals at a major academic center using existing models of delivering medical education and training to surgical residents. It develops a detailed multi-objective, two-stage stochastic optimization model with anticipatory capabilities solved over a rolling time horizon. A novel feature of the models is the incorporation of learning curve theory in the objection function. Using a deterministic version of the model, we identify bounds on the achievement of learning goals under existing training paradigms. The computational results highlight the structural problems in the current surgical resident educational system. These results further corroborate earlier findings and suggest an educational system redesign is necessary for surgical medical residents.

Feature Integration in the Mapping of Multi-Attribute Visual Stimuli to Responses

PubMed Central

Ishizaki, Takuya; Morita, Hiromi; Morita, Masahiko

2015-01-01

In the human visual system, different attributes of an object, such as shape and color, are separately processed in different modules and then integrated to elicit a specific response. In this process, different attributes are thought to be temporarily “bound” together by focusing attention on the object; however, how such binding contributes to stimulus-response mapping remains unclear. Here we report that learning and performance of stimulus-response tasks was more difficult when three attributes of the stimulus determined the correct response than when two attributes did. We also found that spatially separated presentations of attributes considerably complicated the task, although they did not markedly affect target detection. These results are consistent with a paired-attribute model in which bound feature pairs, rather than object representations, are associated with responses by learning. This suggests that attention does not bind three or more attributes into a unitary object representation, and long-term learning is required for their integration. PMID:25762010

Designing Inquiry Starters

NASA Astrophysics Data System (ADS)

Kluger-Bell, B.

2010-12-01

The term "Inquiry Starter" comes from the Institute for Inquiry's model for teaching and learning science through inquiry. It refers to the first phase of an inquiry activity where learners engage in actions that stimulate their curiosity and generate questions for further investigation. In the Professional Development Program, staff and participants have designed a wide variety of inquiry activities with a number of variations on the inquiry starter. This has provided a laboratory for examining inquiry starter design. In this paper, I describe and examine in detail the elements of this design and how the design of those elements is related to achieving learning objectives. There are a number of important common objectives in all inquiry starters. For example, all starters must define a domain for investigation and engage the learner's curiosity in that domain. There are also critical differences in learning objectives depending on the content area being studied, the learners' background knowledge and skills, and many other factors. In this paper I examine designs for both of these types of objectives.

Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program

USGS Publications Warehouse

Melis, Theodore S.; Walters, Carl; Korman, Josh

2015-01-01

With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.

Student perceptions of independent versus facilitated small group learning approaches to compressed medical anatomy education.

PubMed

Whelan, Alexander; Leddy, John J; Mindra, Sean; Matthew Hughes, J D; El-Bialy, Safaa; Ramnanan, Christopher J

2016-01-01

The purpose of this study was to compare student perceptions regarding two, small group learning approaches to compressed (46.5 prosection-based laboratory hours), integrated anatomy education at the University of Ottawa medical program. In the facilitated active learning (FAL) approach, tutors engage students and are expected to enable and balance both active learning and progression through laboratory objectives. In contrast, the emphasized independent learning (EIL) approach stresses elements from the "flipped classroom" educational model: prelaboratory preparation, independent laboratory learning, and limited tutor involvement. Quantitative (Likert-style questions) and qualitative data (independent thematic analysis of open-ended commentary) from a survey of students who had completed the preclerkship curriculum identified strengths from the EIL (promoting student collaboration and communication) and FAL (successful progression through objectives) approaches. However, EIL led to student frustration related to a lack of direction and impaired completion of objectives, whereas active learning opportunities in FAL were highly variable and dependent on tutor teaching style. A "hidden curriculum" was also identified, where students (particularly EIL and clerkship students) commonly compared their compressed anatomy education or their anatomy learning environment with other approaches. Finally, while both groups highly regarded the efficiency of prosection-based learning and expressed value for cadaveric-based learning, student commentary noted that the lack of grade value dedicated to anatomy assessment limited student accountability. This study revealed critical insights into small group learning in compressed anatomy education, including the need to balance student active learning opportunities with appropriate direction and feedback (including assessment). © 2015 American Association of Anatomists.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

PubMed Central

Jie, Biao; Liu, Mingxia; Liu, Jun

2016-01-01

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Neurally and ocularly informed graph-based models for searching 3D environments

NASA Astrophysics Data System (ADS)

Jangraw, David C.; Wang, Jun; Lance, Brent J.; Chang, Shih-Fu; Sajda, Paul

2014-08-01

Objective. As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions—our implicit ‘labeling’ of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment. Approach. First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the ‘similar’ objects it identifies. Main results. We show that by exploiting the subjects’ implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers’ inference of subjects’ implicit labeling. Significance. In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user’s interests.

E-Learning as an Opportunity for the Public Administration

NASA Astrophysics Data System (ADS)

Casagranda, Milena; Colazzo, Luigi; Molinari, Andrea; Tomasini, Sara

In this paper we will describe the results of a learning project in the Public Administration, highlighting the methodological approach based on a blended training model in a context that has never experienced this type of activities. The observations contained in the paper will be focused on the evaluation results of this experience and the redesign elements in term of alternation between the classroom and distance training, methodologies, the value and use of the e-learning platform and learning evaluation. The elements that emerge will also provide the basis for the design of future teaching actions for this context (in which at this moment we are involved). The objective is to identify a "learning model", related also to the use of technological tools that are able to support lifelong learning and to define dynamics and process relating to facilitating learning activities of teachers and tutors.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

Critical Success Factor for Implementing Vocational Blended Learning

NASA Astrophysics Data System (ADS)

Dewi, K. C.; Ciptayani, P. I.; Surjono, H. D.; Priyanto

2018-01-01

Blended learning provides many benefits to the flexibility of time, place and situation constraints. The research’s objectives was describing the factors that determine the successful implementation of blended learning in vocational higher education. The research used a qualitative approach, data collected through observations and interviews by questionnare based on the CSFs indicators refers to TAM and Kliger. Data analysis was inductive method. The result provided an illustration that the success of vocational blended learning implementation was largely determined by the selection of instructional models that are inline with learning achievement target. The effectiveness of blended learning required the existence of policy support, readiness of IT infrastructure. Changing lecturer’s culture by utilizing ICT can also encourage the accelerated process of successful implementation. It can concluded that determinant factor of successful implementation of blended learning in vocational education is determined by teacher’s ability in mastering the pedagogical knowledge of designing instructional models.

Using Blended Learning for Enhancing EFL Prospective Teachers' Pedagogical Knowledge and Performance

ERIC Educational Resources Information Center

Badawi, Mohamed Farrag

2009-01-01

The basic objective of the present study is to investigate the effectiveness of using blended learning model in developing EFL prospective teachers' pedagogical knowledge and performance. The study sample included 38 EFL Saudi prospective teachers (fourth-year students) at the Faculty of Education & Arts, University of Tabuk, KSA. To collect…

An Inquiry into Flipped Learning in Fourth Grade Math Instruction

ERIC Educational Resources Information Center

D'addato, Teresa; Miller, Libbi R.

2016-01-01

The objective of this action research project was to better understand the impact of flipped learning on fourth grade math students in a socioeconomically disadvantaged setting. A flipped instructional model was implemented with the group of students enrolled in the researcher's class. Data was collected in the form of classroom observations,…

Application of Resource Description Framework to Personalise Learning: Systematic Review and Methodology

ERIC Educational Resources Information Center

Jevsikova, Tatjana; Berniukevicius, Andrius; Kurilovas, Eugenijus

2017-01-01

The paper is aimed to present a methodology of learning personalisation based on applying Resource Description Framework (RDF) standard model. Research results are two-fold: first, the results of systematic literature review on Linked Data, RDF "subject-predicate-object" triples, and Web Ontology Language (OWL) application in education…

Private Practice: Exploring the Missing Social Dimension in "Reflective Practice"

ERIC Educational Resources Information Center

Kotzee, Ben

2012-01-01

In professional education today, Schon's concept of "reflective practice" underpins much thinking about learning at work. This approach--with its emphasis on the inner life of the professional and on her own interpretations of her learning experiences--is increasingly being challenged: often cited objections are that the model ignores factors like…

Teaching Psychosomatic Medicine Using Problem-Based Learning and Role-Playing

ERIC Educational Resources Information Center

Heru, Alison M.

2011-01-01

Objective: Problem-based learning (PBL) has been implemented in medical education world-wide. Despite its popularity, it has not been generally considered useful for residency programs. The author presents a model for the implementation of PBL in residency programs. Method: The author presents a description of a PBL curriculum for teaching…

Modeling of Students' Profile and Learning Chronicle with Data Cubes

ERIC Educational Resources Information Center

Ola, Ade G.; Bai, Xue; Omojokun, Emmanuel E.

2014-01-01

Over the years, companies have relied on On-Line Analytical Processing (OLAP) to answer complex questions relating to issues in business environments such as identifying profitability, trends, correlations, and patterns. This paper addresses the application of OLAP in education and learning. The objective of the research presented in the paper is…

Who or What Contributes to Student Satisfaction in Different Blended Learning Modalities?

ERIC Educational Resources Information Center

Diep, Anh-Nguyet; Zhu, Chang; Struyven, Katrien; Blieck, Yves

2017-01-01

Different blended learning (BL) modalities and the interaction effect between human and technological factors on student satisfaction need adequately researched to shed more light on successful BL implementation. The objective of the present article is three-fold: (1) to present a model to predict student satisfaction with BL programs, (2) to…

An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling

ERIC Educational Resources Information Center

Mustafa, Hassan M. H.; Tourkia, Fadhel Ben; Ramadan, Ramadan Mohamed

2017-01-01

The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which is associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of elearning performance considering…

Adopting SCORM 1.2 Standards in a Courseware Production Environment

ERIC Educational Resources Information Center

Barker, Bradley

2004-01-01

The Sharable Content Object Reference Model (SCORM) is a technology framework for Web-based learning technology. Originated by the Department of Defense and accelerated by the Advanced Distributed Learning initiative SCORM was released in January of 2000 (ADL, 2003). The goals of SCORM are to decrease the cost of training, while increasing the…

The Effects of a Multiyear Universal Social-Emotional Learning Program: The Role of Student and School Characteristics

ERIC Educational Resources Information Center

Bierman, Karen L.; Coie, John D.; Dodge, Kenneth A.; Greenberg, Mark T.; Lochman, John E.; McMahon, Robert J.; Pinderhughes, Ellen

2010-01-01

Objective: This article examines the impact of a universal social-emotional learning program, the Fast Track PATHS (Promoting Alternative Thinking Strategies) curriculum and teacher consultation, embedded within the Fast Track selective prevention model. Method: The longitudinal analysis involved 2,937 children of multiple ethnicities who remained…

Learning from Japanese Approach to Teachers' Professional Development: Can "Jugyou Kenkyu" Work in Other Countries?

ERIC Educational Resources Information Center

Masami, Matoba; Reza, Sarkar Arani M.

2005-01-01

This paper tries to present a careful analysis of current trends and challenges to importing Japanese model of teachers' professional development. The objective is to examine what "we" can learn from Japanese approach to improving instruction, especially "Jugyou Kenkyu" (Lesson Study) as a collaborative research on the…

Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation.

PubMed

Wang, Peng; Zheng, Yefeng; John, Matthias; Comaniciu, Dorin

2012-01-01

Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation (TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.

Marketing and Distribution: Better Learning Experiences through Proper Coordination.

ERIC Educational Resources Information Center

Coakley, Carroll B.

1979-01-01

Presents a cooperative education model that correlates the student's occupational objective with his/her training station. Components of the model discussed are (1) the task analysis, (2) the job description, (3) training plans, and (4) student evaluation. (LRA)

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

ERIC Educational Resources Information Center

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…

Reference frames in allocentric representations are invariant across static and active encoding

PubMed Central

Chan, Edgar; Baumann, Oliver; Bellgrove, Mark A.; Mattingley, Jason B.

2013-01-01

An influential model of spatial memory—the so-called reference systems account—proposes that relationships between objects are biased by salient axes (“frames of reference”) provided by environmental cues, such as the geometry of a room. In this study, we sought to examine the extent to which a salient environmental feature influences the formation of spatial memories when learning occurs via a single, static viewpoint and via active navigation, where information has to be integrated across multiple viewpoints. In our study, participants learned the spatial layout of an object array that was arranged with respect to a prominent environmental feature within a virtual arena. Location memory was tested using judgments of relative direction. Experiment 1A employed a design similar to previous studies whereby learning of object-location information occurred from a single, static viewpoint. Consistent with previous studies, spatial judgments were significantly more accurate when made from an orientation that was aligned, as opposed to misaligned, with the salient environmental feature. In Experiment 1B, a fresh group of participants learned the same object-location information through active exploration, which required integration of spatial information over time from a ground-level perspective. As in Experiment 1A, object-location information was organized around the salient environmental cue. Taken together, the findings suggest that the learning condition (static vs. active) does not affect the reference system employed to encode object-location information. Spatial reference systems appear to be a ubiquitous property of spatial representations, and might serve to reduce the cognitive demands of spatial processing. PMID:24009595

An insect-inspired model for visual binding II: functional analysis and visual attention.

PubMed

Northcutt, Brandon D; Higgins, Charles M

2017-04-01

We have developed a neural network model capable of performing visual binding inspired by neuronal circuitry in the optic glomeruli of flies: a brain area that lies just downstream of the optic lobes where early visual processing is performed. This visual binding model is able to detect objects in dynamic image sequences and bind together their respective characteristic visual features-such as color, motion, and orientation-by taking advantage of their common temporal fluctuations. Visual binding is represented in the form of an inhibitory weight matrix which learns over time which features originate from a given visual object. In the present work, we show that information represented implicitly in this weight matrix can be used to explicitly count the number of objects present in the visual image, to enumerate their specific visual characteristics, and even to create an enhanced image in which one particular object is emphasized over others, thus implementing a simple form of visual attention. Further, we present a detailed analysis which reveals the function and theoretical limitations of the visual binding network and in this context describe a novel network learning rule which is optimized for visual binding.

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

NASA Astrophysics Data System (ADS)

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.

Representing Energy. II. Energy Tracking Representations

ERIC Educational Resources Information Center

Scherr, Rachel E.; Close, Hunter G.; Close, Eleanor W.; Vokos, Stamatis

2012-01-01

The Energy Project at Seattle Pacific University has developed representations that embody the substance metaphor and support learners in conserving and tracking energy as it flows from object to object and changes form. Such representations enable detailed modeling of energy dynamics in complex physical processes. We assess student learning by…

From SCORM to Common Cartridge: A Step Forward

ERIC Educational Resources Information Center

Gonzalez-Barbone, Victor; Anido-Rifon, Luis

2010-01-01

Shareable Content Object Reference Model (SCORM) was proposed as a standard for sharable learning object packaging, delivering and sequencing. Several years later, Common Cartridge (CC) is proposed as an enhancement of SCORM offering more flexibility and addressing needs not originally envisioned, namely assessment and web 2.0 standards, content…

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

NASA Astrophysics Data System (ADS)

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.

Assessing Learning, Quality and Engagement in Learning Objects: The Learning Object Evaluation Scale for Students (LOES-S)

ERIC Educational Resources Information Center

Kay, Robin H.; Knaack, Liesel

2009-01-01

Learning objects are interactive web-based tools that support the learning of specific concepts by enhancing, amplifying, and/or guiding the cognitive processes of learners. Research on the impact, effectiveness, and usefulness of learning objects is limited, partially because comprehensive, theoretically based, reliable, and valid evaluation…

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

ERIC Educational Resources Information Center

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…

Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling.

PubMed

Khan, Zulfiqar Hasan; Gu, Irene Yu-Hua

2013-12-01

This paper proposes a novel Bayesian online learning and tracking scheme for video objects on Grassmann manifolds. Although manifold visual object tracking is promising, large and fast nonplanar (or out-of-plane) pose changes and long-term partial occlusions of deformable objects in video remain a challenge that limits the tracking performance. The proposed method tackles these problems with the main novelties on: 1) online estimation of object appearances on Grassmann manifolds; 2) optimal criterion-based occlusion handling for online updating of object appearances; 3) a nonlinear dynamic model for both the appearance basis matrix and its velocity; and 4) Bayesian formulations, separately for the tracking process and the online learning process, that are realized by employing two particle filters: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in an alternating fashion to mitigate the tracking drift. Experiments using the proposed tracker on videos captured by a single dynamic/static camera have shown robust tracking performance, particularly for scenarios when target objects contain significant nonplanar pose changes and long-term partial occlusions. Comparisons with eight existing state-of-the-art/most relevant manifold/nonmanifold trackers with evaluations have provided further support to the proposed scheme.

A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning

NASA Astrophysics Data System (ADS)

Rabbani, Masoud; Montazeri, Mona; Farrokhi-Asl, Hamed; Rafiei, Hamed

2016-12-01

Mixed-model assembly lines are increasingly accepted in many industrial environments to meet the growing trend of greater product variability, diversification of customer demands, and shorter life cycles. In this research, a new mathematical model is presented considering balancing a mixed-model U-line and human-related issues, simultaneously. The objective function consists of two separate components. The first part of the objective function is related to balance problem. In this part, objective functions are minimizing the cycle time, minimizing the number of workstations, and maximizing the line efficiencies. The second part is related to human issues and consists of hiring cost, firing cost, training cost, and salary. To solve the presented model, two well-known multi-objective evolutionary algorithms, namely non-dominated sorting genetic algorithm and multi-objective particle swarm optimization, have been used. A simple solution representation is provided in this paper to encode the solutions. Finally, the computational results are compared and analyzed.

Learning Objects and Gerontology

ERIC Educational Resources Information Center

Weinreich, Donna M.; Tompkins, Catherine J.

2006-01-01

Virtual AGE (vAGE) is an asynchronous educational environment that utilizes learning objects focused on gerontology and a learning anytime/anywhere philosophy. This paper discusses the benefits of asynchronous instruction and the process of creating learning objects. Learning objects are "small, reusable chunks of instructional media" Wiley…

Improved analyses using function datasets and statistical modeling

Treesearch

John S. Hogland; Nathaniel M. Anderson

2014-01-01

Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...

A framework for teaching medical students and residents about practice-based learning and improvement, synthesized from a literature review.

PubMed

Ogrinc, Greg; Headrick, Linda A; Mutha, Sunita; Coleman, Mary T; O'Donnell, Joseph; Miles, Paul V

2003-07-01

To create a framework for teaching the knowledge and skills of practice-based learning and improvement to medical students and residents based on proven, effective strategies. The authors conducted a Medline search of English-language articles published between 1996 and May 2001, using the term "quality improvement" (QI), and cross-matched it with "medical education" and "health professions education." A thematic-synthesis method of review was used to compile the information from the articles. Based on the literature review, an expert panel recommended educational objectives for practice-based learning and improvement. Twenty-seven articles met the inclusion criteria. The majority of studies were conducted in academic medical centers and medical schools and 40% addressed experiential learning of QI. More than 75% were qualitative case reports capturing educational outcomes, and 7% included an experimental study design. The expert panel integrated data from the literature review with the Dreyfus model of professional skill acquisition, the Institute for Healthcare Improvement's (IHI) knowledge domains for improving health care, and the ACGME competencies and generated a framework of core educational objectives about teaching practice-based learning and improvement to medical students and residents. Teaching the knowledge and skills of practice-based learning and improvement to medical students and residents is a necessary and important foundation for improving patient care. The authors present a framework of learning objectives-informed by the literature and synthesized by the expert panel-to assist educational leaders when integrating these objectives into a curriculum. This framework serves as a blueprint to bridge the gap between current knowledge and future practice needs.

Investigating the potential of e-Learning in healthcare postgraduate curricula: a structural equation model.

PubMed

Katharaki, Maria; Daskalakis, Stelios; Mantas, John

2010-01-01

The objective of this paper is to assess the future adaptability of e-Learning platforms within postgraduate modules. An ongoing empirical assessment was conducted amongst postgraduate students, based on the Technology Acceptance Model (TAM). The current paper presents the outcomes from the second phase of a survey, involving fifty six participants. Data analysis was performed using a structural equation model, based on partial least squares. Results highlighted the very strong effect of perceived usefulness and perceived ease of use to attitude towards using e-Learning platforms. Consequently, attitude towards use proved to be a very strong predictor of behavioral intention. Perceived usefulness, on the contrary, did not prove to have an effect to behavioral intention. Implications on the potential of using e-Learning platforms are discussed along with limitations and future directions of the study.

Analysis of Blended Learning Implementation on Waste Treatment Subjects in Agricultural Vocational School

NASA Astrophysics Data System (ADS)

Sugiarti, Y.; Nurmayani, S.; Mujdalipah, S.

2018-02-01

Waste treatment is one of the productive subjects in vocational high school in programs of Agricultural Processing Technology which is one of the objectives learning has been assigned in graduate competency standards (SKL) of Vocational High School. Based on case studies that have been conducted in SMK Pertanian Pembangunan Negeri Lembang, waste treatment subjects had still use the lecture method or conventional method, and students are less enthusiastic in learning process. Therefore, the implementation of more interactive learning models such as blended learning with Edmodo is one of alternative models to resolve the issue. So, the purpose of this study is to formulate the appropriate learning syntax for the implementation of blended learning with Edmodo to agree the requirement characteristics of students and waste treatment subject and explain the learning outcome obtained by students in the cognitive aspects on the subjects of waste treatment. This research was conducted by the method of classroom action research (CAR) with a Mc. Tagart model. The result from this research is the implementation of blended learning with Edmodo on the subjects of waste treatment can improve student learning outcomes in the cognitive aspects with the maximum increase in the value of N-gain 0.82, as well as student learning completeness criteria reaching 100% on cycle 2. Based on the condition of subject research the formulation of appropriate learning syntax for implementation of blended learning model with Edmodo on waste treatment subject are 1) Self-paced learning, 2) Group networking, 3) Live Event- collaboration, 4) Association - communication, 5) Assessment - Performance material support. In summary, implementation of blended learning model with Edmodo on waste treatment subject can improve improve student learning outcomes in the cognitive aspects and conducted in five steps on syntax.

Addition of fornix transection to frontal-temporal disconnection increases the impairment in object-in-place memory in macaque monkeys.

PubMed

Wilson, C R E; Baxter, M G; Easton, A; Gaffan, D

2008-04-01

Both frontal-inferotemporal disconnection and fornix transection (Fx) in the monkey impair object-in-place scene learning, a model of human episodic memory. If the contribution of the fornix to scene learning is via interaction with or modulation of frontal-temporal interaction--that is, if they form a unitary system--then Fx should have no further effect when added to frontal-temporal disconnection. However, if the contribution of the fornix is to some extent distinct, then fornix lesions may produce an additional deficit in scene learning beyond that caused by frontal-temporal disconnection. To distinguish between these possibilities, we trained three male rhesus monkeys on the object-in-place scene-learning task. We tested their learning on the task following frontal-temporal disconnection, achieved by crossed unilateral aspiration of the frontal cortex in one hemisphere and the inferotemporal cortex in the other, and again following the addition of Fx. The monkeys were significantly impaired in scene learning following frontal-temporal disconnection, and furthermore showed a significant increase in this impairment following the addition of Fx, from 32.8% error to 40.5% error (chance = 50%). The increased impairment following the addition of Fx provides evidence that the fornix and frontal-inferotemporal interaction make distinct contributions to episodic memory.

Conceptual Model Learning Objects and Design Recommendations for Small Screens

ERIC Educational Resources Information Center

Churchill, Daniel

2011-01-01

This article presents recommendations for the design of conceptual models for applications via handheld devices such as personal digital assistants and some mobile phones. The recommendations were developed over a number of years through experience that involves design of conceptual models, and applications of these multimedia representations with…

A Multidimensional Curriculum Model for Heritage or International Language Instruction.

ERIC Educational Resources Information Center

Lazaruk, Wally

1993-01-01

Describes the Multidimension Curriculum Model for developing a language curriculum and suggests a generic approach to selecting and sequencing learning objectives. Alberta Education used this model to design a new French-as-a-Second-Language program. The experience/communication, culture, language, and general language components at the beginning,…

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

The development of mathematics courseware for learning line and angle

NASA Astrophysics Data System (ADS)

Halim, Noor Dayana Abd; Han, Ong Boon; Abdullah, Zaleha; Yusup, Junaidah

2015-05-01

Learning software is a teaching aid which is often used in schools to increase students' motivation, attract students' attention and also improve the quality of teaching and learning process. However, the development of learning software should be followed the phases in Instructional Design (ID) Model, therefore the process can be carried out systematic and orderly. Thus, this concept paper describes the application of ADDIE model in the development of mathematics learning courseware for learning Line and Angle named CBL-Math. ADDIE model consists of five consecutive phases which are Analysis, Design, Development, Implementation and Evaluation. Each phase must be properly planned in order to achieve the objectives stated. Other than to describe the processes occurring in each phase, this paper also demonstrating how cognitive theory of multimedia learning principles are integrated in the developed courseware. The principles that applied in the courseware reduce the students' cognitive load while learning the topic of line and angle. With well prepared development process and the integration of appropriate principles, it is expected that the developed software can help students learn effectively and also increase students' achievement in the topic of Line and Angle.

Neurally and ocularly informed graph-based models for searching 3D environments.

PubMed

Jangraw, David C; Wang, Jun; Lance, Brent J; Chang, Shih-Fu; Sajda, Paul

2014-08-01

As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions-our implicit 'labeling' of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment. First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the 'similar' objects it identifies. We show that by exploiting the subjects' implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers' inference of subjects' implicit labeling. In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user's interests.

Focusing on learning through constructive alignment with task-oriented portfolio assessment

NASA Astrophysics Data System (ADS)

Cain, A.; Grundy, J.; Woodward, C. J.

2018-07-01

Approaches to learning have been shown to have a significant impact on student success in technical units. This paper reports on an action research study that applied the principles of constructive alignment to improve student learning outcomes in programming units. The proposed model uses frequent formative feedback to engage students with unit material, and encourage them to adopt deep approaches to learning. Our results provide a set of guiding principles and a structured teaching approach that focuses students on meeting unit learning objectives, the goal of constructive alignment. The results are demonstrated via descriptions of the resulting teaching and learning environment, student results, and staff and student reflections.

The Greatest Learning Return on Your Pedagogical Investment: Alignment, Assessment or In-Class Instruction?

PubMed Central

Holt, Emily A.; Young, Craig; Keetch, Jared; Larsen, Skylar; Mollner, Brayden

2015-01-01

Critical thinking is often considered an essential learning outcome of institutions in higher education. Previous work has proposed three pedagogical strategies to address this goal: more active, student-centered in-class instruction, assessments which contain higher-order cognitive questions, and greater alignment within a classroom (i.e., high agreement of the cognitive level of learning objectives, assessments, and in-class instruction). Our goals were to determine which of these factors, individually or the interactions therein, contributed most to improvements in university students’ critical thinking. We assessed students’ higher-order cognitive skills in introductory non-majors biology courses the first and last week of instruction. For each of the fifteen sections observed, we also measured the cognitive level of assessments and learning objectives, evaluated the learner-centeredness of each classroom, and calculated an alignment score for each class. The best model to explain improvements in students’ high-order cognitive skills contained the measure of learner-centeredness of the class and pre-quiz scores as a covariate. The cognitive level of assessments, learning objectives, nor alignment explained improvements in students’ critical thinking. In accordance with much of the current literature, our findings support that more student-centered classes had greater improvements in student learning. However, more research is needed to clarify the role of assessment and alignment in student learning. PMID:26340659

The Greatest Learning Return on Your Pedagogical Investment: Alignment, Assessment or In-Class Instruction?

PubMed

Holt, Emily A; Young, Craig; Keetch, Jared; Larsen, Skylar; Mollner, Brayden

2015-01-01

Critical thinking is often considered an essential learning outcome of institutions in higher education. Previous work has proposed three pedagogical strategies to address this goal: more active, student-centered in-class instruction, assessments which contain higher-order cognitive questions, and greater alignment within a classroom (i.e., high agreement of the cognitive level of learning objectives, assessments, and in-class instruction). Our goals were to determine which of these factors, individually or the interactions therein, contributed most to improvements in university students' critical thinking. We assessed students' higher-order cognitive skills in introductory non-majors biology courses the first and last week of instruction. For each of the fifteen sections observed, we also measured the cognitive level of assessments and learning objectives, evaluated the learner-centeredness of each classroom, and calculated an alignment score for each class. The best model to explain improvements in students' high-order cognitive skills contained the measure of learner-centeredness of the class and pre-quiz scores as a covariate. The cognitive level of assessments, learning objectives, nor alignment explained improvements in students' critical thinking. In accordance with much of the current literature, our findings support that more student-centered classes had greater improvements in student learning. However, more research is needed to clarify the role of assessment and alignment in student learning.

Is the Recall of Verbal-Spatial Information from Working Memory Affected by Symptoms of ADHD?

ERIC Educational Resources Information Center

Caterino, Linda C.; Verdi, Michael P.

2012-01-01

Objective: The Kulhavy model for text learning using organized spatial displays proposes that learning will be increased when participants view visual images prior to related text. In contrast to previous studies, this study also included students who exhibited symptoms of ADHD. Method: Participants were presented with either a map-text or…

Alternative Approaches for Educating Future Global Marketing Professionals: A Comparison of Foreign Study and Research-Intensive Marketing Programs

ERIC Educational Resources Information Center

Kaufman, Peter A.; Melton, Horace L.; Varner, Iris I.; Hoelscher, Mark; Schmidt, Klaus; Spaulding, Aslihan D.

2011-01-01

Using an experiential learning model as a conceptual background, this article discusses characteristics and learning objectives for well-known foreign study programs such as study tours, study abroad, and internships and compares them with a less common overseas program called the "Global Marketing Program" (GMP). GMP involves…

Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

ERIC Educational Resources Information Center

Engelbrecht, Jeffrey C.

2003-01-01

Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

Training Australian General Practitioners in Rural Public Health: Impact, Desirability and Adaptability of Hybrid Problem-Based Learning

ERIC Educational Resources Information Center

Gladman, Justin; Perkins, David

2013-01-01

Context and Objective: Australian rural general practitioners (GPs) require public health knowledge. This study explored the suitability of teaching complex public health issues related to Aboriginal health by way of a hybrid problem-based learning (PBL) model within an intensive training retreat for GP registrars, when numerous trainees have no…

Easing Access for Lifelong Learners: A Comparison of European Models for University Lifelong Learning

ERIC Educational Resources Information Center

Müller, Romina; Remdisch, Sabine; Köhler, Katharina; Marr, Liz; Repo, Saara; Yndigegn, Carsten

2015-01-01

Easing access to higher education (HE) for those engaging in lifelong learning has been a common policy objective across the European Union since the late 1990s. To reach this goal, the transition between vocational and academic routes must be simplified, but European countries are at different developmental stages. This article maps the…

Cross Cultural Analysis of the Use and Perceptions of Web-Based Learning Systems

ERIC Educational Resources Information Center

Arenas-Gaitan, Jorge; Ramirez-Correa, Patricio E.; Rondan-Cataluna, F. Javier

2011-01-01

The main objective of this paper is to examine cultural differences and technology acceptances from students of two universities, one is from a European country: Spain, and the other is in Latin America: Chile. Both of them provide their students with e-learning platforms. The technology acceptance model (TAM) and Hofstede's cultural dimensions…

Testing Methodology in the Student Learning Process

ERIC Educational Resources Information Center

Gorbunova, Tatiana N.

2017-01-01

The subject of the research is to build methodologies to evaluate the student knowledge by testing. The author points to the importance of feedback about the mastering level in the learning process. Testing is considered as a tool. The object of the study is to create the test system models for defence practice problems. Special attention is paid…

Extended Worksheet Developed According to 5E Model Based on Constructivist Learning Approach

ERIC Educational Resources Information Center

Töman, Ufuk; Akdeniz, Ali Riza; Odabasi Çimer, Sabiha; Gürbüz, Fatih

2013-01-01

In order to achieve the targeted objectives desired level of education and modern learning theories for learner centered methods are recommended. In this context the use of worksheets developed and that student participation is considered to be one of the methods. This research is one of the ethyl alcohol fermentation biology issues and prepare…

[Construction and Application of Innovative Education Technology Strategies in Nursing].

PubMed

Chao, Li-Fen; Huang, Hsiang-Ping; Ni, Lee-Fen; Tsai, Chia-Lan; Huang, Tsuey-Yuan

2017-12-01

The evolution of information and communication technologies has deeply impacted education reform, promoted the development of digital-learning models, and stimulated the development of diverse nursing education strategies in order to better fulfill needs and expand in new directions. The present paper introduces the intelligent-learning resources that are available for basic medical science education, problem-based learning, nursing scenario-based learning, objective structured clinical examinations, and other similar activities in the Department of Nursing at Chang Gung University of Science and Technology. The program is offered in two parts: specialized classroom facilities and cloud computing / mobile-learning. The latter includes high-fidelity simulation classrooms, online e-books, and virtual interactive simulation and augmented reality mobile-learning materials, which are provided through multimedia technology development, learning management systems, web-certificated examinations, and automated teaching and learning feedback mechanisms. It is expected that the teaching experiences that are shared in this article may be used as a reference for applying professional wisdom teaching models into nursing education.

Improving CNN Performance Accuracies With Min-Max Objective.

PubMed

Shi, Weiwei; Gong, Yihong; Tao, Xiaoyu; Wang, Jinjun; Zheng, Nanning

2017-06-09

We propose a novel method for improving performance accuracies of convolutional neural network (CNN) without the need to increase the network complexity. We accomplish the goal by applying the proposed Min-Max objective to a layer below the output layer of a CNN model in the course of training. The Min-Max objective explicitly ensures that the feature maps learned by a CNN model have the minimum within-manifold distance for each object manifold and the maximum between-manifold distances among different object manifolds. The Min-Max objective is general and able to be applied to different CNNs with insignificant increases in computation cost. Moreover, an incremental minibatch training procedure is also proposed in conjunction with the Min-Max objective to enable the handling of large-scale training data. Comprehensive experimental evaluations on several benchmark data sets with both the image classification and face verification tasks reveal that employing the proposed Min-Max objective in the training process can remarkably improve performance accuracies of a CNN model in comparison with the same model trained without using this objective.

Learning invariance from natural images inspired by observations in the primary visual cortex.

PubMed

Teichmann, Michael; Wiltschut, Jan; Hamker, Fred

2012-05-01

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

Interpretable Decision Sets: A Joint Framework for Description and Prediction

PubMed Central

Lakkaraju, Himabindu; Bach, Stephen H.; Jure, Leskovec

2016-01-01

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency. PMID:27853627

The Impact and Promise of Open-Source Computational Material for Physics Teaching

NASA Astrophysics Data System (ADS)

Christian, Wolfgang

2017-01-01

A computer-based modeling approach to teaching must be flexible because students and teachers have different skills and varying levels of preparation. Learning how to run the ``software du jour'' is not the objective for integrating computational physics material into the curriculum. Learning computational thinking, how to use computation and computer-based visualization to communicate ideas, how to design and build models, and how to use ready-to-run models to foster critical thinking is the objective. Our computational modeling approach to teaching is a research-proven pedagogy that predates computers. It attempts to enhance student achievement through the Modeling Cycle. This approach was pioneered by Robert Karplus and the SCIS Project in the 1960s and 70s and later extended by the Modeling Instruction Program led by Jane Jackson and David Hestenes at Arizona State University. This talk describes a no-cost open-source computational approach aligned with a Modeling Cycle pedagogy. Our tools, curricular material, and ready-to-run examples are freely available from the Open Source Physics Collection hosted on the AAPT-ComPADRE digital library. Examples will be presented.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

PubMed

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

Recognition vs Reverse Engineering in Boolean Concepts Learning

ERIC Educational Resources Information Center

Shafat, Gabriel; Levin, Ilya

2012-01-01

This paper deals with two types of logical problems--recognition problems and reverse engineering problems, and with the interrelations between these types of problems. The recognition problems are modeled in the form of a visual representation of various objects in a common pattern, with a composition of represented objects in the pattern.…

From Research Resources to Learning Objects: Process Model and Virtualization Experiences

ERIC Educational Resources Information Center

Sierra, Jose Luis; Fernandez-Valmayor, Alfredo; Guinea, Mercedes; Hernanz, Hector

2006-01-01

Typically, most research and academic institutions own and archive a great amount of objects and research related resources that have been produced, used and maintained over long periods of time by different types of "domain experts" (e.g. lecturers and researchers). Although the potential educational value of these resources is very…

Fast automated segmentation of multiple objects via spatially weighted shape learning

NASA Astrophysics Data System (ADS)

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.

Fast automated segmentation of multiple objects via spatially weighted shape learning.

PubMed

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

2016-11-21

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.

Visual recognition and inference using dynamic overcomplete sparse learning.

PubMed

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Model-based choices involve prospective neural activity

PubMed Central

Doll, Bradley B.; Duncan, Katherine D.; Simon, Dylan A.; Shohamy, Daphna; Daw, Nathaniel D.

2015-01-01

Decisions may arise via “model-free” repetition of previously reinforced actions, or by “model-based” evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional MRI and a sequential reward-learning task in which paths contained decodable object categories, we found that humans’ model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables, and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection. PMID:25799041

Individual and social learning processes involved in the acquisition and generalization of tool use in macaques

PubMed Central

Macellini, S.; Maranesi, M.; Bonini, L.; Simone, L.; Rozzi, S.; Ferrari, P. F.; Fogassi, L.

2012-01-01

Macaques can efficiently use several tools, but their capacity to discriminate the relevant physical features of a tool and the social factors contributing to their acquisition are still poorly explored. In a series of studies, we investigated macaques' ability to generalize the use of a stick as a tool to new objects having different physical features (study 1), or to new contexts, requiring them to adapt the previously learned motor strategy (study 2). We then assessed whether the observation of a skilled model might facilitate tool-use learning by naive observer monkeys (study 3). Results of study 1 and study 2 showed that monkeys trained to use a tool generalize this ability to tools of different shape and length, and learn to adapt their motor strategy to a new task. Study 3 demonstrated that observing a skilled model increases the observers' manipulations of a stick, thus facilitating the individual discovery of the relevant properties of this object as a tool. These findings support the view that in macaques, the motor system can be modified through tool use and that it has a limited capacity to adjust the learnt motor skills to a new context. Social factors, although important to facilitate the interaction with tools, are not crucial for tool-use learning. PMID:22106424

Improving Science Process Skills for Primary School Students Through 5E Instructional Model-Based Learning

NASA Astrophysics Data System (ADS)

Choirunnisa, N. L.; Prabowo, P.; Suryanti, S.

2018-01-01

The main objective of this study is to describe the effectiveness of 5E instructional model-based learning to improve primary school students’ science process skills. The science process skills is important for students as it is the foundation for enhancing the mastery of concepts and thinking skills needed in the 21st century. The design of this study was experimental involving one group pre-test and post-test design. The result of this study shows that (1) the implementation of learning in both of classes, IVA and IVB, show that the percentage of learning implementation increased which indicates a better quality of learning and (2) the percentage of students’ science process skills test results on the aspects of observing, formulating hypotheses, determining variable, interpreting data and communicating increased as well.

Unintended knowledge learnt in primary science practical lessons

NASA Astrophysics Data System (ADS)

Park, Jisun; Abrahams, Ian; Song, Jinwoong

2016-11-01

This study explored the different kinds of unintended learning in primary school practical science lessons. In this study, unintended learning has been defined as student learning that was found to occur that was not included in the teachers learning objectives for that specific lesson. A total of 22 lessons, taught by five teachers in Korean primary schools with 10- to 12-year-old students, were audio-and video recorded. Pre-lesson interviews with the teachers were conducted to ascertain their intended learning objectives. Students were asked to write short memos after the lesson about what they learnt. Post-lesson interviews with students and teachers were undertaken. What emerged was that there were three types of knowledge that students learnt unintentionally: factual knowledge gained by phenomenon-based reasoning, conceptual knowledge gained by relation- or model-based reasoning, and procedural knowledge acquired by practice. Most unintended learning found in this study fell into the factual knowledge and only a few cases of conceptual knowledge were found. Cases of both explicit procedural knowledge and implicit procedural knowledge were found. This study is significant in that it suggests how unintended learning in practical work can be facilitated as an educative opportunity for meaningful learning by exploring what and how students learnt.

Infants Encode Phonetic Detail during Cross-Situational Word Learning

PubMed Central

Escudero, Paola; Mulak, Karen E.; Vlach, Haley A.

2016-01-01

Infants often hear new words in the context of more than one candidate referent. In cross-situational word learning (XSWL), word-object mappings are determined by tracking co-occurrences of words and candidate referents across multiple learning events. Research demonstrates that infants can learn words in XSWL paradigms, suggesting that it is a viable model of real-world word learning. However, these studies have all presented infants with words that have no or minimal phonological overlap (e.g., BLICKET and GAX). Words often contain some degree of phonological overlap, and it is unknown whether infants can simultaneously encode fine phonological detail while learning words via XSWL. We tested 12-, 15-, 17-, and 20-month-olds’ XSWL of eight words that, when paired, formed non-minimal pairs (MPs; e.g., BON–DEET) or MPs (e.g., BON–TON, DEET–DIT). The results demonstrated that infants are able to learn word-object mappings and encode them with sufficient phonetic detail as to identify words in both non-minimal and MP contexts. Thus, this work suggests that infants are able to simultaneously discriminate phonetic differences between words and map words to referents in an implicit learning paradigm such as XSWL. PMID:27708605

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

PubMed

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, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Virtual Workshop Environment (VWE): A Taxonomy and Service Oriented Architecture (SOA) Framework for Modularized Virtual Learning Environments (VLE)--Applying the Learning Object Concept to the VLE

ERIC Educational Resources Information Center

Paulsson, Fredrik; Naeve, Ambjorn

2006-01-01

Based on existing Learning Object taxonomies, this article suggests an alternative Learning Object taxonomy, combined with a general Service Oriented Architecture (SOA) framework, aiming to transfer the modularized concept of Learning Objects to modularized Virtual Learning Environments. The taxonomy and SOA-framework exposes a need for a clearer…

Can social semantic web techniques foster collaborative curriculum mapping in medicine?

PubMed

Spreckelsen, Cord; Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-08-15

Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives ("LOs"). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 ("good") in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system's ability to support curriculum revision. The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems.

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

PubMed

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

Role of cerebellum in learning postural tasks.

PubMed

Ioffe, M E; Chernikova, L A; Ustinova, K I

2007-01-01

For a long time, the cerebellum has been known to be a structure related to posture and equilibrium control. According to the anatomic structure of inputs and internal structure of the cerebellum, its role in learning was theoretically reasoned and experimentally proved. The hypothesis of an inverse internal model based on feedback-error learning mechanism combines feedforward control by the cerebellum and feedback control by the cerebral motor cortex. The cerebellar cortex is suggested to acquire internal models of the body and objects in the external world. During learning of a new tool the motor cortex receives feedback from the realized movement while the cerebellum produces only feedforward command. To realize a desired movement without feedback of the realized movement, the cerebellum needs to form an inverse model of the hand/arm system. This suggestion was supported by FMRi data. The role of cerebellum in learning new postural tasks mainly concerns reorganization of natural synergies. A learned postural pattern in dogs has been shown to be disturbed after lesions of the cerebral motor cortex or cerebellar nuclei. In humans, learning voluntary control of center of pressure position is greatly disturbed after cerebellar lesions. However, motor cortex and basal ganglia are also involved in the feedback learning postural tasks.

Policy improvement by a model-free Dyna architecture.

PubMed

Hwang, Kao-Shing; Lo, Chia-Yue

2013-05-01

The objective of this paper is to accelerate the process of policy improvement in reinforcement learning. The proposed Dyna-style system combines two learning schemes, one of which utilizes a temporal difference method for direct learning; the other uses relative values for indirect learning in planning between two successive direct learning cycles. Instead of establishing a complicated world model, the approach introduces a simple predictor of average rewards to actor-critic architecture in the simulation (planning) mode. The relative value of a state, defined as the accumulated differences between immediate reward and average reward, is used to steer the improvement process in the right direction. The proposed learning scheme is applied to control a pendulum system for tracking a desired trajectory to demonstrate its adaptability and robustness. Through reinforcement signals from the environment, the system takes the appropriate action to drive an unknown dynamic to track desired outputs in few learning cycles. Comparisons are made between the proposed model-free method, a connectionist adaptive heuristic critic, and an advanced method of Dyna-Q learning in the experiments of labyrinth exploration. The proposed method outperforms its counterparts in terms of elapsed time and convergence rate.

Object-Oriented Control System Design Using On-Line Training of Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed

1997-01-01

This report deals with the object-oriented model development of a neuro-controller design for permanent magnet (PM) dc motor drives. The system under study is described as a collection of interacting objects. Each object module describes the object behaviors, called methods. The characteristics of the object are included in its variables. The knowledge of the object exists within its variables, and the performance is determined by its methods. This structure maps well to the real world objects that comprise the system being modeled. A dynamic learning architecture that possesses the capabilities of simultaneous on-line identification and control is incorporated to enforce constraints on connections and control the dynamics of the motor. The control action is implemented "on-line", in "real time" in such a way that the predicted trajectory follows a specified reference model. A design example of controlling a PM dc motor drive on-line shows the effectiveness of the design tool. This will therefore be very useful in aerospace applications. It is expected to provide an innovative and noval software model for the rocket engine numerical simulator executive.

Learning Efficient Sparse and Low Rank Models.

PubMed

Sprechmann, P; Bronstein, A M; Sapiro, G

2015-09-01

Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.

Behavior learning in differential games and reorientation maneuvers

NASA Astrophysics Data System (ADS)

Satak, Neha

The purpose of this dissertation is to apply behavior learning concepts to incomplete- information continuous time games. Realistic game scenarios are often incomplete-information games in which the players withhold information. A player may not know its opponent's objectives and strategies prior to the start of the game. This lack of information can limit the player's ability to play optimally. If the player can observe the opponent's actions, it can better optimize its achievements by taking corrective actions. In this research, a framework to learn an opponent's behavior and take corrective actions is developed. The framework will allow a player to observe the opponent's actions and formulate behavior models. The developed behavior model can then be utilized to find the best actions for the player that optimizes the player's objective function. In addition, the framework proposes that the player plays a safe strategy at the beginning of the game. A safe strategy is defined in this research as a strategy that guarantees a minimum pay-off to the player independent of the other player's actions. During the initial part of the game, the player will play the safe strategy until it learns the opponent's behavior. Two methods to develop behavior models that differ in the formulation of the behavior model are proposed. The first method is the Cost-Strategy Recognition (CSR) method in which the player formulates an objective function and a strategy for the opponent. The opponent is presumed to be rational and therefore will play to optimize its objective function. The strategy of the opponent is dependent on the information available to the opponent about other players in the game. A strategy formulation presumes a certain level of information available to the opponent. The previous observations of the opponent's actions are used to estimate the parameters of the formulated behavior model. The estimated behavior model predicts the opponent's future actions. The second method is the Direct Approximation of Value Function (DAVF) method. In this method, unlike the CSR method, the player formulates an objective function for the opponent but does not formulates a strategy directly; rather, indirectly the player assumes that the opponent is playing optimally. Thus, a value function satisfying the HJB equation corresponding to the opponent's cost function exists. The DAVF method finds an approximate solution for the value function based on previous observations of the opponent's control. The approximate solution to the value function is then used to predict the opponent's future behavior. Game examples in which only a single player is learning its opponent's behavior are simulated. Subsequently, examples in which both players in a two-player game are learning each other's behavior are simulated. In the second part of this research, a reorientation control maneuver for a spinning spacecraft will be developed. This will aid the application of behavior learning and differential games concepts to the specific scenario involving multiple spinning spacecraft. An impulsive reorientation maneuver with coasting will be analytically designed to reorient the spin axis of the spacecraft using a single body fixed thruster. Cooperative maneuvers of multiple spacecraft optimizing fuel and relative orientation will be designed. Pareto optimality concepts will be used to arrive at mutually agreeable reorientation maneuvers for the cooperating spinning spacecraft.

The Potential Consequence of Using Value-Added Models to Evaluate Teachers

ERIC Educational Resources Information Center

Shen, Zuchao; Simon, Carlee Escue; Kelcey, Ben

2016-01-01

Value-added models try to separate the contribution of individual teachers or schools to students' learning growth measured by standardized test scores. There is a policy trend to use value-added modeling to evaluate teachers because of its face validity and superficial objectiveness. This article investigates the potential long term consequences…

Using TI-Nspire in a Modelling Teacher's Training Course

ERIC Educational Resources Information Center

Flores, Ángel Homero; Gómez, Adriana; Chávez, Xochitl

2015-01-01

Using Mathematical Modelling has become a useful tool in teaching-learning mathematics at all levels. This is so because mathematical objects are seen from their very applications, giving them meaning from the beginning. In this paper we present some details on the development of a teacher's training course called Modelling in the Teaching of…

Learning and disrupting invariance in visual recognition with a temporal association rule

PubMed Central

Isik, Leyla; Leibo, Joel Z.; Poggio, Tomaso

2012-01-01

Learning by temporal association rules such as Foldiak's trace rule is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments have shown that invariance can be broken at both the psychophysical and single cell levels. We show (1) that temporal association learning provides appropriate invariance in models of object recognition inspired by the visual cortex, (2) that we can replicate the “invariance disruption” experiments using these models with a temporal association learning rule to develop and maintain invariance, and (3) that despite dramatic single cell effects, a population of cells is very robust to these disruptions. We argue that these models account for the stability of perceptual invariance despite the underlying plasticity of the system, the variability of the visual world and expected noise in the biological mechanisms. PMID:22754523

A longitudinal interprofessional simulation curriculum for critical care teams: Exploring successes and challenges.

PubMed

Leclair, Laurie W; Dawson, Mary; Howe, Alison; Hale, Sue; Zelman, Eric; Clouser, Ryan; Garrison, Garth; Allen, Gilman

2018-05-01

Interprofessional care teams are the backbone of intensive care units (ICUs) where severity of illness is high and care requires varied skills and experience. Despite this care model, longitudinal educational programmes for such workplace teams rarely include all professions. In this article, we report findings on the initial assessment and evaluation of an ongoing, longitudinal simulation-based curriculum for interprofessional workplace critical care teams. The study had two independent components, quantitative learner assessment and qualitative curricular evaluation. To assess curriculum effectiveness at meeting learning objectives, participant-reported key learning points identified using a self-assessment tool administered immediately following curricular participation were mapped to session learning objectives. To evaluate the curriculum, we conducted a qualitative study using a phenomenology approach involving purposeful sampling of nine curricular participants undergoing recorded semi-structured interviews. Verbatim transcripts were reviewed by two independent readers to derive themes further subdivided into successes and barriers. Learner self-assessment demonstrated that the majority of learners, across all professions, achieved at least one intended learning objective with senior learners more likely to report team-based objectives and junior learners more likely to report knowledge/practice objectives. Successes identified by curricular evaluation included authentic critical care curricular content, safe learning environment, and team comradery from shared experience. Barriers included unfamiliarity with the simulation environment and clinical coverage for curricular participation. This study suggests that a sustainable interprofessional curriculum for workplace ICU critical care teams can achieve the desired educational impact and effectively deliver authentic simulated work experiences if barriers to educational engagement and participation can be overcome.

A case-based, small-group cooperative learning course in preclinical veterinary science aimed at bridging basic science and clinical literacy.

PubMed

Schoeman, J P; van Schoor, M; van der Merwe, L L; Meintjes, R A

2009-03-01

In 1999 a dedicated problem-based learning course was introduced into the lecture-based preclinical veterinary curriculum of the University of Pretoria. The Introduction to Clinical Studies Course combines traditional lectures, practical sessions, student self-learning and guided tutorials. The self-directed component of the course utilises case-based, small-group cooperative learning as an educational vehicle to link basic science with clinical medicine. The aim of this article is to describe the objectives and structure of the course and to report the results of the assessment of the students' perceptions on some aspects of the course. Students reacted very positively to the ability of the course to equip them with problem-solving skills. Students indicated positive perceptions about the workload of the course. There were, however, significantly lower scores for the clarity of the course objectives. Although the study guide for the course is very comprehensive, the practice regarding the objectives is still uncertain. It is imperative to set clear objectives in non-traditional, student-centred courses. The objectives have to be explained at the outset and reiterated throughout the course. Tutors should also communicate the rationale behind problem-based learning as a pedagogical method to the students. Further research is needed to verify the effectiveness of this course in bridging the gap between basic science and clinical literacy in veterinary science. Ongoing feedback and assessment of the management and content are important to refine this model for integrating basic science with clinical literacy.

Understanding Learner Acceptance of Learning Objects: The Roles of Learning Object Characteristics and Individual Differences

ERIC Educational Resources Information Center

Lau, Siong-Hoe; Woods, Peter C.

2009-01-01

Many organisations and institutions have integrated learning objects into their e-learning systems to make the instructional resources more efficient. Like any other information systems, this trend has made user acceptance of learning objects an increasingly critical issue as a high level of learner satisfaction and acceptance reflects that the…

Introduction and Assessment of a Blended-Learning Model to Teach Patient Assessment in a Doctor of Pharmacy Program

PubMed Central

Woodruff, Ashley; Prescott, Gina M.; Albanese, Nicole; Bernhardi, Christian; Doloresco, Fred

2016-01-01

Objective. To integrate a blended-learning model into a two-course patient assessment sequence in a doctor of pharmacy (PharmD) program and to assess the academic performance and perceptions of enrolled students. Design. A blended-learning model consisting of a flipped classroom format was integrated into a patient assessment (PA) course sequence. Course grades of students in the blended-learning (intervention) and traditional-classroom (control) groups were compared. A survey was administered to assess student perceptions. Assessment. The mean numeric grades of students in the intervention group were higher than those of students in the traditional group (PA1 course: 92.2±3.1 vs 90.0±4.3; and PA2 course: 90.3±4.9 vs 85.8±4.2). Eighty-six percent of the students in the intervention group agreed that the instructional methodologies used in this course facilitated understanding of the material. Conclusion. The blended-learning model was associated with improved academic performance and was well-received by students. PMID:28179725

Introduction and Assessment of a Blended-Learning Model to Teach Patient Assessment in a Doctor of Pharmacy Program.

PubMed

Prescott, William Allan; Woodruff, Ashley; Prescott, Gina M; Albanese, Nicole; Bernhardi, Christian; Doloresco, Fred

2016-12-25

Objective. To integrate a blended-learning model into a two-course patient assessment sequence in a doctor of pharmacy (PharmD) program and to assess the academic performance and perceptions of enrolled students. Design. A blended-learning model consisting of a flipped classroom format was integrated into a patient assessment (PA) course sequence. Course grades of students in the blended-learning (intervention) and traditional-classroom (control) groups were compared. A survey was administered to assess student perceptions. Assessment. The mean numeric grades of students in the intervention group were higher than those of students in the traditional group (PA1 course: 92.2±3.1 vs 90.0±4.3; and PA2 course: 90.3±4.9 vs 85.8±4.2). Eighty-six percent of the students in the intervention group agreed that the instructional methodologies used in this course facilitated understanding of the material. Conclusion. The blended-learning model was associated with improved academic performance and was well-received by students.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

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

PubMed Central

Feng, Di

2018-01-01

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). PMID:29466300

Spatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition

PubMed Central

Tian, Moqian; Grill-Spector, Kalanit

2015-01-01

Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of object recognition and for the development of computational algorithms that learn from examples. PMID:26024454

Learning Progress in Evolution Theory: Climbing a Ladder or Roaming a Landscape?

ERIC Educational Resources Information Center

Zabel, Jorg; Gropengiesser, Harald

2011-01-01

The objective of this naturalistic study was to explore, model and visualise the learning progress of 13-year-old students in the domain of evolution theory. Data were collected under actual classroom conditions and with a sample size of 107 learners, which followed a teaching unit on Darwin's theory of natural selection. Before and after the…

A Study of the Cognitive Diffusion Model: Facilitating Students' High Level Cognitive Processes with Authentic Support

ERIC Educational Resources Information Center

Huang, Yueh-Min; Shadiev, Rustam; Sun, Ai; Hwang, Wu-Yuin; Liu, Tzu-Yu

2017-01-01

For this study the researchers designed learning activities to enhance students' high level cognitive processes. Students learned new information in a classroom setting and then applied and analyzed their new knowledge in familiar authentic contexts by taking pictures of objects found there, describing them, and sharing their homework with peers.…

Problem Based Learning in Cooperative Situation (PBLCS) and Its Impact on Development of Personal Intelligence

ERIC Educational Resources Information Center

Talib, Ahmad; Bini Kailani, Ismail

2014-01-01

The objective of this study was focused on the observation on the practice of PBLCS learning model, and its impact on the development of personal intelligence (interpersonal and intrapersonal) students. This study used a quasi-experimental design with one factor measurement. The study population was students of class XI, IPA (Natural Science) SMAN…

Getting Things Done. A Learning Package for Process Skills. An Occasional Paper.

ERIC Educational Resources Information Center

Taylor, Max

This manual is designed to help teachers and tutors implement a 4-day modular course in the skills and processes necessary to get things done. The aims and content of the course are described. A course summary is provided along with a model course program that includes parallel lists of objectives, suggested learning activities and text materials,…

Analysis of Person-Situation Interactions in Educational Settings via Cross-Classified Multilevel Longitudinal Modelling: Illustrated with the Example of Students' Stress Experience

ERIC Educational Resources Information Center

Kärner, Tobias; Sembill, Detlef; Aßmann, Christian; Friederichs, Edgar; Carstensen, Claus H.

2017-01-01

The investigation of learning processes by assessing students' experience along with objective characteristics within a classroom context has a long tradition in empirical learning process research (e.g. Sembill, 1984 et passim; Wild & Krapp, 1996). However, most of the existing studies confine themselves to psychological variables that seem…

A Neurocomputational Account of Taxonomic Responding and Fast Mapping in Early Word Learning

ERIC Educational Resources Information Center

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

Creating Curriculum of English for Conservative Tourism for Junior Guides to Promote Tourist Attractions in Thailand

ERIC Educational Resources Information Center

Wimontham, Onsiri

2018-01-01

This research was supported the research fund of 2017 by Office of the Higher Education Commission of Thailand. The objectives of this research are listed: (1) To form the model of teaching and learning English for local development by English curriculum (B. Ed.) students' participation in training on out-of-classroom learning management, which…

The Micro-Social Preschool Learning System, Vineland, New Jersey; Childhood Education. Model Programs.

ERIC Educational Resources Information Center

American Institutes for Research in the Behavioral Sciences, Palo Alto, CA.

The booklet describes the Micro-Social Preschool Learning System for children from poor migrant families in Vineland, New Jersey. Of the population of 50,000, approximately 20% is Puerto Rican, 10% Appalachian white, and 7% black. Language objectives of the program are to develop the ability to speak and understand 2,000 basic words in English…

Pre-School Foreign Language Teaching and Learning--A Network Innovation Project in Slovenia

ERIC Educational Resources Information Center

Brumen, Mihaela; Berro, Fanika Fras; Cagran, Branka

2017-01-01

The paper describes some findings about teaching foreign languages (FL) in a pre-school setting obtained from the Network Innovation Project (NIP). The aims of the NIP were to research and practise the most effective teaching approaches and organizational models in teaching and learning of FL in pre-schools. The objectives were to determine how…

Our (Represented) World: A Quantum-Like Object

NASA Astrophysics Data System (ADS)

Lambert-Mogiliansky, Ariane; Dubois, François

It has been suggested that observed cognitive limitations may be an expression of the quantum-like structure of the mind. In this chapter we explore some implications of this hypothesis for learning i.e., for the construction of a representation of the world. For a quantum-like individual, there exists a multiplicity of mentally incompatible (Bohr complementary) but equally valid and complete representations (mental pictures) of the world. The process of learning i.e., of constructing a representation, involves two kinds of operations on the mental picture. The acquisition of new data which is modelled as a preparation procedure and the processing of data which is modelled as an introspective measurement operation. This process is shown not to converge to a single mental picture. Rather, it can evolve forever. We define a concept of entropy to capture relative intrinsic uncertainty. The analysis suggests a new perspective on learning. First, it implies that we must turn to double objectification as in Quantum Mechanics: the cognitive process is the primary object of learning. Second, it suggests that a representation of the world arises as the result of creative interplay between the mind and the environment.

Does the Sound of a Barking Dog Activate its Corresponding Visual Form? An fMRI Investigation of Modality-Specific Semantic Access

PubMed Central

Reilly, Jamie; Garcia, Amanda; Binney, Richard J.

2016-01-01

Much remains to be learned about the neural architecture underlying word meaning. Fully distributed models of semantic memory predict that the sound of a barking dog will conjointly engage a network of distributed sensorimotor spokes. An alternative framework holds that modality-specific features additionally converge within transmodal hubs. Participants underwent functional MRI while covertly naming familiar objects versus newly learned novel objects from only one of their constituent semantic features (visual form, characteristic sound, or point-light motion representation). Relative to the novel object baseline, familiar concepts elicited greater activation within association regions specific to that presentation modality. Furthermore, visual form elicited activation within high-level auditory association cortex. Conversely, environmental sounds elicited activation in regions proximal to visual association cortex. Both conditions commonly engaged a putative hub region within lateral anterior temporal cortex. These results support hybrid semantic models in which local hubs and distributed spokes are dually engaged in service of semantic memory. PMID:27289210

Design of Intelligent Robot as A Tool for Teaching Media Based on Computer Interactive Learning and Computer Assisted Learning to Improve the Skill of University Student

NASA Astrophysics Data System (ADS)

Zuhrie, M. S.; Basuki, I.; Asto B, I. G. P.; Anifah, L.

2018-01-01

The focus of the research is the teaching module which incorporates manufacturing, planning mechanical designing, controlling system through microprocessor technology and maneuverability of the robot. Computer interactive and computer-assisted learning is strategies that emphasize the use of computers and learning aids (computer assisted learning) in teaching and learning activity. This research applied the 4-D model research and development. The model is suggested by Thiagarajan, et.al (1974). 4-D Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with an objective to produce a tool of learning in the form of intelligent robot modules and kit based on Computer Interactive Learning and Computer Assisted Learning. From the data of the Indonesia Robot Contest during the period of 2009-2015, it can be seen that the modules that have been developed confirm the fourth stage of the research methods of development; disseminate method. The modules which have been developed for students guide students to produce Intelligent Robot Tool for Teaching Based on Computer Interactive Learning and Computer Assisted Learning. Results of students’ responses also showed a positive feedback to relate to the module of robotics and computer-based interactive learning.

Non-linear learning in online tutorial to enhance students’ knowledge on normal distribution application topic

NASA Astrophysics Data System (ADS)

Kartono; Suryadi, D.; Herman, T.

2018-01-01

This study aimed to analyze the enhancement of non-linear learning (NLL) in the online tutorial (OT) content to students’ knowledge of normal distribution application (KONDA). KONDA is a competence expected to be achieved after students studied the topic of normal distribution application in the course named Education Statistics. The analysis was performed by quasi-experiment study design. The subject of the study was divided into an experimental class that was given OT content in NLL model and a control class which was given OT content in conventional learning (CL) model. Data used in this study were the results of online objective tests to measure students’ statistical prior knowledge (SPK) and students’ pre- and post-test of KONDA. The statistical analysis test of a gain score of KONDA of students who had low and moderate SPK’s scores showed students’ KONDA who learn OT content with NLL model was better than students’ KONDA who learn OT content with CL model. Meanwhile, for students who had high SPK’s scores, the gain score of students who learn OT content with NLL model had relatively similar with the gain score of students who learn OT content with CL model. Based on those findings it could be concluded that the NLL model applied to OT content could enhance KONDA of students in low and moderate SPK’s levels. Extra and more challenging didactical situation was needed for students in high SPK’s level to achieve the significant gain score.

Media development effectiveness of geography 3d muckups

NASA Astrophysics Data System (ADS)

Prasetya, S. P.; Daryono; Budiyanto, E.

2018-01-01

Geography examines geosphere phenomena that occurs in a space associated with humans on earth’s surface. Media 3D models are an important visual media in presenting spatial objects on the earth’s surface. This study aims to develop a decent 3D mockups media used for learning materials and test the effectiveness of media geography 3D mockups on learning outcomes. The study involved 90 students of Geography Education, Faculty of Social Sciences and Law, State University of Surabaya. Method development using a model of the Borg and Gall (1989) which has been modified into three stages, namely the introduction, development, and testing. The study produced instructional media 3D Muckups eligible to be used as a learning medium for the material hydrosphere geography, geology, and geomorphology. 3D mockups media use in learning geography materials can increase the activity of students, student interest and a positive response to raise the student learning outcomes as the material can be delivered more concrete geography. Based on observations conducted student activity occurs continuously increase in the use of 3D models for learning geography material.

Sex-specific effects of Cacna1c haploinsufficiency on object recognition, spatial memory, and reversal learning capabilities in rats.

PubMed

Braun, Moria D; Kisko, Theresa M; Vecchia, Débora Dalla; Andreatini, Roberto; Schwarting, Rainer K W; Wöhr, Markus

2018-05-23

The CACNA1C gene is strongly implicated in the etiology of multiple major neuropsychiatric disorders, such as bipolar disorder, major depression, and schizophrenia, with cognitive deficits being a common feature. It is unclear, however, by which mechanisms CACNA1C variants advance the risk of developing neuropsychiatric disorders. This study set out to investigate cognitive functioning in a newly developed genetic Cacna1c rat model. Specifically, spatial and reversal learning, as well as object recognition memory were assessed in heterozygous Cacna1c +/- rats and compared to wildtype Cacna1c +/+ littermate controls in both sexes. Our results show that both Cacna1c +/+ and Cacna1c +/- animals were able to learn the rewarded arm configuration of a radial maze over the course of seven days. Both groups also showed reversal learning patterns indicative of intact abilities. In females, genotype differences were evident in the initial spatial learning phase, with Cacna1c +/- females showing hypo-activity and fewer mixed errors. In males, a difference was found during probe trials for both learning phases, with Cacna1c +/- rats displaying better distinction between previously baited and non-baited arms; and regarding cognitive flexibility in favor of the Cacna1c +/+ animals. All experimental groups proved to be sensitive to reward magnitude and fully able to distinguish between novel and familiar objects in the novel object recognition task. Taken together, these results indicate that Cacna1c haploinsufficiency has a minor, but positive impact on (spatial) memory functions in rats. Copyright © 2018 Elsevier Inc. All rights reserved.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

A remote sensing computer-assisted learning tool developed using the unified modeling language

NASA Astrophysics Data System (ADS)

Friedrich, J.; Karslioglu, M. O.

The goal of this work has been to create an easy-to-use and simple-to-make learning tool for remote sensing at an introductory level. Many students struggle to comprehend what seems to be a very basic knowledge of digital images, image processing and image arithmetic, for example. Because professional programs are generally too complex and overwhelming for beginners and often not tailored to the specific needs of a course regarding functionality, a computer-assisted learning (CAL) program was developed based on the unified modeling language (UML), the present standard for object-oriented (OO) system development. A major advantage of this approach is an easier transition from modeling to coding of such an application, if modern UML tools are being used. After introducing the constructed UML model, its implementation is briefly described followed by a series of learning exercises. They illustrate how the resulting CAL tool supports students taking an introductory course in remote sensing at the author's institution.

A biological hierarchical model based underwater moving object detection.

PubMed

Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

2014-01-01

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

A Biological Hierarchical Model Based Underwater Moving Object Detection

PubMed Central

Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen

2014-01-01

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194

Anthropogenic and technogenic factors of operational risk at hazardous industrial objects of fuel-power complex

NASA Astrophysics Data System (ADS)

Magid, S. I.; Arkhipova, E. N.; Kulichikhin, V. V.; Zagretdinov, I. Sh.

2016-12-01

Technogenic and anthropogenic accidence at hazardous industrial objects (HIO) in the Russian Federation has been considered. The accidence level at HIO, including power plants and network enterprises, is determined by anthropogenic reasons, so-called "human factor", in 70% of all cases. The analysis of incidents caused by personnel has shown that errors occur most often during accidental situations, launches, holdups, routine switches, and other effects on equipment controls. It has been demonstrated that skills needed to perform type and routine switches can be learned, to certain limits, on real operating equipment, while combating emergency and accidental situations can be learned only with the help of modern training simulators developed based on information technologies. Problems arising during the following processes have been considered: development of mathematical and software support of modern training equipment associated, in one way or another, with adequate power-generating object modeling in accordance with human operator specifics; modeling and/or simulation of the corresponding control and management systems; organization of the education system (functional supply of the instructor, education and methodological resources (EMR)); organization of the program-technical, scalable and adaptable, platform for modeling of the main and secondary functions of the training simulator. It has been concluded that the systemic approach principle on the necessity and sufficiency in the applied methodology allows to reproduce all technological characteristics of the equipment, its topological completeness, as well as to achieve the acceptable counting rate. The initial "rough" models of processes in the equipment are based on the normative techniques and equation coefficients taken from the normative materials as well. Then, the synthesis of "fine" models has been carried out following the global practice in modeling and training simulator building, i.e., verification of "rough" models based on experimental data available to the developer. Finally, the last stage of modeling is adaptation (validation) of "fine" models to the prototype object using experimental data on the power-generating object and tests of these models with operating and maintaining personnel. These stages determine adequacy of the used mathematical model for a particular training simulator and, thus, its compliance with such modern scientific criteria as objectivity and experimental verifiability.

ADDIE Model Application Promoting Interactive Multimedia

NASA Astrophysics Data System (ADS)

Baharuddin, B.

2018-02-01

This paper presents the benefits of interactive learning in a vocational high school, which is developed by Research and Developmet (R&D) method. The questionnaires, documentations, and instrument tests are used to obtain data and it is analyzed by descriptive statistic. The results show the students’ competence is generated up to 80.00 %, and the subject matter aspects of the content is up to 90.00 %. The learning outcomes average is 85. This type media fulfils the proposed objective which can enhance the learning outcome.

The influence of personality on neural mechanisms of observational fear and reward learning

PubMed Central

Hooker, Christine I.; Verosky, Sara C.; Miyakawa, Asako; Knight, Robert T.; D’Esposito, Mark

2012-01-01

Fear and reward learning can occur through direct experience or observation. Both channels can enhance survival or create maladaptive behavior. We used fMRI to isolate neural mechanisms of observational fear and reward learning and investigate whether neural response varied according to individual differences in neuroticism and extraversion. Participants learned object-emotion associations by observing a woman respond with fearful (or neutral) and happy (or neutral) facial expressions to novel objects. The amygdala-hippocampal complex was active when learning the object-fear association, and the hippocampus was active when learning the object-happy association. After learning, objects were presented alone; amygdala activity was greater for the fear (vs. neutral) and happy (vs. neutral) associated object. Importantly, greater amygdala-hippocampal activity during fear (vs. neutral) learning predicted better recognition of learned objects on a subsequent memory test. Furthermore, personality modulated neural mechanisms of learning. Neuroticism positively correlated with neural activity in the amygdala and hippocampus during fear (vs. neutral) learning. Low extraversion/high introversion was related to faster behavioral predictions of the fearful and neutral expressions during fear learning. In addition, low extraversion/high introversion was related to greater amygdala activity during happy (vs. neutral) learning, happy (vs. neutral) object recognition, and faster reaction times for predicting happy and neutral expressions during reward learning. These findings suggest that neuroticism is associated with an increased sensitivity in the neural mechanism for fear learning which leads to enhanced encoding of fear associations, and that low extraversion/high introversion is related to enhanced conditionability for both fear and reward learning. PMID:18573512

Implementation and Refinement of a Problem-based Learning Model: A Ten-Year Experience

PubMed Central

Crabtree, Brian L.; Theilman, Gary D.; Ross, Brendan S.; Cleary, John D.; Byrd, H. Joseph

2007-01-01

Objectives To evaluate the effectiveness of a problem-based learning (PBL) model implemented in 1995 at the University of Mississippi School of Pharmacy. Design The third-professional (P3) year curriculum was reoriented from a faculty-centered model of teaching to a student-centered model of learning. Didactic lectures and structured classroom time were diminished. Small student groups were organized and a faculty facilitator monitored each group's discussions and provided individual student assessments. At the end of each 8-week block, students were assessed on group participation, disease and drug content knowledge, and problem-solving abilities. Faculty and student input was solicited at the end of each year to aid programmatic improvement. In 2000, a formal 5-year review of the PBL program was conducted. Assessment Recommendations for improvement included clarifying course objectives, adopting a peer-review process for examination materials, refining the group assessment instruments, and providing an opportunity for student remediation after a course was failed. A weekly case conference presided over by a faculty content expert was also recommended. Ongoing critical evaluation during the following 5-year period was provided by graduates of the program, faculty participants, and accreditation reviews. Conclusion Over our 10-year experience with a PBL model of P3 education, we found that although the initial challenges of increased demands on personnel and teaching space were easily overcome, student acceptance of the program depended on their acknowledgment of the practical benefits of active learning and on the value afforded their input on curricular development. PMID:17429517

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives.

PubMed

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives

PubMed Central

Li, Cai; Lowe, Robert; Ziemke, Tom

2014-01-01

In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a “reshaping” function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal “reshaping” functions). In this article, we use this architecture with the actor-critic algorithms for finding a good “reshaping” function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion. PMID:25324773

Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

PubMed Central

Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

2014-01-01

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations. PMID:25538637

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

PubMed

Yuan, Tao; Zheng, Xinqi; Hu, Xuan; Zhou, Wei; Wang, Wei

2014-01-01

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Preparing for the Market. Teacher Edition. Fashion Buying Series.

ERIC Educational Resources Information Center

Collins, Cindy

This teacher's guide presents material for a unit on preparing for the retail fashion market. Content focuses on merchandise plans, computing open-to-buy, computing turnover, the components of a model stock plan, and criteria used when selecting a supplier. The guide contains 5 objectives, 6 group learning activities keyed to the objectives, 21…

Multi-objective group scheduling optimization integrated with preventive maintenance

NASA Astrophysics Data System (ADS)

Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

2017-11-01

This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

Student generated learning objectives: extent of congruence with faculty set objectives and factors influencing their generation.

PubMed

Abdul Ghaffar Al-Shaibani, Tarik A; Sachs-Robertson, Annette; Al Shazali, Hafiz O; Sequeira, Reginald P; Hamdy, Hosam; Al-Roomi, Khaldoon

2003-07-01

A problem-based learning strategy is used for curriculum planning and implementation at the Arabian Gulf University, Bahrain. Problems are constructed in a way that faculty-set objectives are expected to be identified by students during tutorials. Students in small groups, along with a tutor functioning as a facilitator, identify learning issues and define their learning objectives. We compared objectives identified by student groups with faculty-set objectives to determine extent of congruence, and identified factors that influenced students' ability at identifying faculty-set objectives. Male and female students were segregated and randomly grouped. A faculty tutor was allocated for each group. This study was based on 13 problems given to entry-level medical students. Pooled objectives of these problems were classified into four categories: structural, functional, clinical and psychosocial. Univariate analysis of variance was used for comparison, and a p > 0.05 was considered significant. The mean of overall objectives generated by the students was 54.2%, for each problem. Students identified psychosocial learning objectives more readily than structural ones. Female students identified more psychosocial objectives, whereas male students identified more of structural objectives. Tutor characteristics such as medical/non-medical background, and the years of teaching were correlated with categories of learning issues identified. Students identify part of the faculty-set learning objectives during tutorials with a faculty tutor acting as a facilitator. Students' gender influences types of learning issues identified. Content expertise of tutors does not influence identification of learning needs by students.

The Symmetry of Partner Modelling

ERIC Educational Resources Information Center

Dillenbourg, Pierre; Lemaignan, Séverin; Sangin, Mirweis; Nova, Nicolas; Molinari, Gaëlle

2016-01-01

Collaborative learning has often been associated with the construction of a shared understanding of the situation at hand. The psycholinguistics mechanisms at work while establishing common grounds are the object of scientific controversy. We postulate that collaborative tasks require some level of mutual modelling, i.e. that each partner needs…

Strategies for improving the quality of verbal patient and family education: a review of the literature and creation of the EDUCATE model

PubMed Central

Marcus, Cara

2014-01-01

Objective: Patient and family education includes print, audio-visual methods, demonstration, and verbal instruction. Our objective was to study verbal instruction as a component of patient and family education and make recommendations for best practices for healthcare providers who use this method. Methods: We conducted a literature review of articles from 1990 to 2014 about verbal education and collaborated on departmental presentations to determine best practices. A survey was sent to all nursing staff to determine perceptions of verbal education and barriers to learning. Results: Through our work, we were able to identify verbal education models, best practices, and needs. We then constructed the EDUCATE model of verbal education, which built upon our findings. Conclusion: Verbal education of patients and family members requires a multidisciplinary approach that takes into account learning styles, literacy, and culture to apply clear communication and methods for the assessment of learning. Providers need the skills, time, and training to effectively perform patient and family verbal education every time they care for patients. Further research needs to be performed on how to test, document, and quantify patients' comprehension and retention of verbal instructions. PMID:25750796

The Impact of Secondary School Students' Preconceptions on the Evolution of their Mental Models of the Greenhouse effect and Global Warming

NASA Astrophysics Data System (ADS)

Reinfried, Sibylle; Tempelmann, Sebastian

2014-01-01

This paper provides a video-based learning process study that investigates the kinds of mental models of the atmospheric greenhouse effect 13-year-old learners have and how these mental models change with a learning environment, which is optimised in regard to instructional psychology. The objective of this explorative study was to observe and analyse the learners' learning pathways according to their previous knowledge in detail and to understand the mental model formation processes associated with them more precisely. For the analysis of the learning pathways, drawings, texts, video and interview transcripts from 12 students were studied using qualitative methods. The learning pathways pursued by the learners significantly depend on their domain-specific previous knowledge. The learners' preconceptions could be typified based on specific characteristics, whereby three preconception types could be formed. The 'isolated pieces of knowledge' type of learners, who have very little or no previous knowledge about the greenhouse effect, build new mental models that are close to the target model. 'Reduced heat output' type of learners, who have previous knowledge that indicates compliances with central ideas of the normative model, reconstruct their knowledge by reorganising and interpreting their existing knowledge structures. 'Increasing heat input' type of learners, whose previous knowledge consists of subjective worldly knowledge, which has a greater personal explanatory value than the information from the learning environment, have more difficulties changing their mental models. They have to fundamentally reconstruct their mental models.

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

ERIC Educational Resources Information Center

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…

Object Oriented Learning Objects

ERIC Educational Resources Information Center

Morris, Ed

2005-01-01

We apply the object oriented software engineering (OOSE) design methodology for software objects (SOs) to learning objects (LOs). OOSE extends and refines design principles for authoring dynamic reusable LOs. Our learning object class (LOC) is a template from which individualised LOs can be dynamically created for, or by, students. The properties…

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

ERIC Educational Resources Information Center

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…

Governance and assessment in a widely distributed medical education program in Australia.

PubMed

Solarsh, Geoff; Lindley, Jennifer; Whyte, Gordon; Fahey, Michael; Walker, Amanda

2012-06-01

The learning objectives, curriculum content, and assessment standards for distributed medical education programs must be aligned across the health care systems and community contexts in which their students train. In this article, the authors describe their experiences at Monash University implementing a distributed medical education program at metropolitan, regional, and rural Australian sites and an offshore Malaysian site, using four different implementation models. Standardizing learning objectives, curriculum content, and assessment standards across all sites while allowing for site-specific implementation models created challenges for educational alignment. At the same time, this diversity created opportunities to customize the curriculum to fit a variety of settings and for innovations that have enriched the educational system as a whole.Developing these distributed medical education programs required a detailed review of Monash's learning objectives and curriculum content and their relevance to the four different sites. It also required a review of assessment methods to ensure an identical and equitable system of assessment for students at all sites. It additionally demanded changes to the systems of governance and the management of the educational program away from a centrally constructed and mandated curriculum to more collaborative approaches to curriculum design and implementation involving discipline leaders at multiple sites.Distributed medical education programs, like that at Monash, in which cohorts of students undertake the same curriculum in different contexts, provide potentially powerful research platforms to compare different pedagogical approaches to medical education and the impact of context on learning outcomes.

[Competence Based Catalogue of Learning Objectives for Rehabilitation, Physical Medicine, Naturopathic Treatment - Revised Version - Joint Recommendations of the German Society of Rehabilitation Science and the German Society of Physical Medicine and Rehabilitation].

PubMed

Mau, Wilfried; Liebl, Max Emanuel; Deck, Ruth; Lange, Uwe; Smolenski, Ulrich Christian; Walter, Susanne; Gutenbrunner, Christoph

2017-12-01

Since the first publication of learning objectives for the interdisciplinary subject "Rehabilitation, Physical Medicine, Naturopathic Treatment" in undergraduate medical education in 2004 a revision is reasonable due to heterogenous teaching programmes in the faculties and the introduction of the National Competence Based Catalogue of Learning Objectives in Medicine as well as the "Masterplan Medical Education 2020". Therefore the German Society of Rehabilitation Science and the German Society of Physical Medicine and Rehabilitation started a structured consensus process using the DELPHI-method to reduce the learning objectives and arrange them more clearly. Objectives of particular significance are emphasised. All learning objectives are assigned to the cognitive and methodological level 1 or to the action level 2. The learning objectives refer to the less detailed National Competence Based Catalogue of Learning Objectives in Medicine. The revised learning objectives will contribute to further progress in competence based and more homogenous medical teaching in core objectives of Rehabilitation, Physical Medicine, and Naturopathic Treatment in the faculties. © Georg Thieme Verlag KG Stuttgart · New York.

An Interactive Augmented Reality Implementation of Hijaiyah Alphabet for Children Education

NASA Astrophysics Data System (ADS)

Rahmat, R. F.; Akbar, F.; Syahputra, M. F.; Budiman, M. A.; Hizriadi, A.

2018-03-01

Hijaiyah alphabet is letters used in the Qur’an. An attractive and exciting learning process of Hijaiyah alphabet is necessary for the children. One of the alternatives to create attractive and interesting learning process of Hijaiyah alphabet is to develop it into a mobile application using augmented reality technology. Augmented reality is a technology that combines two-dimensional or three-dimensional virtual objects into actual three-dimensional circles and projects them in real time. The purpose of application aims to foster the children interest in learning Hijaiyah alphabet. This application is using Smartphone and marker as the medium. It was built using Unity and augmented reality library, namely Vuforia, then using Blender as the 3D object modeling software. The output generated from this research is the learning application of Hijaiyah letters using augmented reality. How to use it is as follows: first, place marker that has been registered and printed; second, the smartphone camera will track the marker. If the marker is invalid, the user should repeat the tracking process. If the marker is valid and identified, the marker will have projected the objects of Hijaiyah alphabet in three-dimensional form. Lastly, the user can learn and understand the shape and pronunciation of Hijaiyah alphabet by touching the virtual button on the marker

Cultural Resource Predictive Modeling

DTIC Science & Technology

2017-10-01

property to manage ? a. Yes 2) Do you use CRPM (Cultural Resource Predictive Modeling) No, but I use predictive modelling informally . For example...resource program and provide support to the test ranges for their missions. This document will provide information such as lessons learned, points...of contact, and resources to the range cultural resource managers . Objective/Scope: Identify existing cultural resource predictive models and

Can Social Semantic Web Techniques Foster Collaborative Curriculum Mapping In Medicine?

PubMed Central

Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-01-01

Background Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. Objective The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. Methods A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives (“LOs”). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. Results At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 (“good”) in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system’s ability to support curriculum revision. Conclusions The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems. PMID:23948519

Joint object and action recognition via fusion of partially observable surveillance imagery data

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex L.

2017-05-01

Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In this paper, we treat the problem of "action-object" as a duality problem. We postulate a correlation between observed human actions and the object that is being utilized within those actions, and conversely, if an object being handled is recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study, we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks (CNNs) learning capability, we present an architecture design using a new CNN model to learn "action-object" perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational contexts. The results of our comparative investigation are discussed and presented in detail.

Hybrid Multiagent System for Automatic Object Learning Classification

NASA Astrophysics Data System (ADS)

Gil, Ana; de La Prieta, Fernando; López, Vivian F.

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

Deep Learning for Lowtextured Image Matching

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Fedorenko, V. V.; Fomin, N. A.

2018-05-01

Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Hence, automatic documentation of the archeological process using Structure from Motion (SfM) methods is challenging. Nevertheless, such documentation is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development of a new Wide Image Zone Adaptive Robust feature Descriptor (WIZARD) based on the deep learning. We use a convolutional auto-encoder to compress discriminative features of a local path into a descriptor code. We build a codebook to perform point matching on multiple images. The matching is performed using the nearest neighbor search and a modified voting algorithm. We present a new "Multi-view Amphora" (Amphora) dataset for evaluation of point matching algorithms. The dataset includes images of an Ancient Greek vase found at Taman Peninsula in Southern Russia. The dataset provides color images, a ground truth 3D model, and a ground truth optical flow. We evaluated the WIZARD descriptor on the "Amphora" dataset to show that it outperforms the SIFT and SURF descriptors on the complex patch pairs.

Histone deacetylase inhibitors improve learning consolidation in young and in KA-induced-neurodegeneration and SAMP-8-mutant mice.

PubMed

Fontán-Lozano, Angela; Romero-Granados, Rocío; Troncoso, Julieta; Múnera, Alejandro; Delgado-García, José María; Carrión, Angel M

2008-10-01

Histone deacetylases (HDAC) are enzymes that maintain chromatin in a condensate state, related with absence of transcription. We have studied the role of HDAC on learning and memory processes. Both eyeblink classical conditioning (EBCC) and object recognition memory (ORM) induced an increase in histone H3 acetylation (Ac-H3). Systemic treatment with HDAC inhibitors improved cognitive processes in EBCC and in ORM tests. Immunohistochemistry and gene expression analyses indicated that administration of HDAC inhibitors decreased the stimulation threshold for Ac-H3, and gene expression to reach the levels required for learning and memory. Finally, we evaluated the effect of systemic administration of HDAC inhibitors to mice models of neurodegeneration and aging. HDAC inhibitors reversed learning and consolidation deficits in ORM in these models. These results point out HDAC inhibitors as candidate agents for the palliative treatment of learning and memory impairments in aging and in neurodegenerative disorders.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

Reducing uncertainty about objective functions in adaptive management

USGS Publications Warehouse

Williams, B.K.

2012-01-01

This paper extends the uncertainty framework of adaptive management to include uncertainty about the objectives to be used in guiding decisions. Adaptive decision making typically assumes explicit and agreed-upon objectives for management, but allows for uncertainty as to the structure of the decision process that generates change through time. Yet it is not unusual for there to be uncertainty (or disagreement) about objectives, with different stakeholders expressing different views not only about resource responses to management but also about the appropriate management objectives. In this paper I extend the treatment of uncertainty in adaptive management, and describe a stochastic structure for the joint occurrence of uncertainty about objectives as well as models, and show how adaptive decision making and the assessment of post-decision monitoring data can be used to reduce uncertainties of both kinds. Different degrees of association between model and objective uncertainty lead to different patterns of learning about objectives. ?? 2011.