Project organized Problem-based learning in Distance Education

DEFF Research Database (Denmark)

Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten

2002-01-01

Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....

Project-Organized Problem-Based Learning in Distance Education

DEFF Research Database (Denmark)

Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten

2003-01-01

Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....

Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

Czech Academy of Sciences Publication Activity Database

Neruda, Roman; Vidnerová, Petra

2009-01-01

Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

Educational Designs Supporting Student Engagement Through Network Project Studies

DEFF Research Database (Denmark)

Nielsen, Jørgen Lerche

2016-01-01

Internationally, new pedagogical approaches emphasizing collaboration or learning in networks have been developed following the introduction of new technologies, especially the spread of social media. It is interesting to see such pedagogical developments in relation to similar approaches......, developed from the traditions of organizing university studies through student-driven project work and problem-driven learning approaches, which have been developed at the Danish universities of Roskilde and Aalborg as early as from the beginning of the 1970s. Specific educational designs integrating...... digital media are discussed, especially focusing on student engagement and the implications of organizing the pedagogical practice as networked project work. The discussions are based on the author’s experiences during 16 years of teaching and supervising at the Danish Master’s Program of ICT and Learning...

Cascade Error Projection: A New Learning Algorithm

Science.gov (United States)

Duong, T. A.; Stubberud, A. R.; Daud, T.; Thakoor, A. P.

1995-01-01

A new neural network architecture and a hardware implementable learning algorithm is proposed. The algorithm, called cascade error projection (CEP), handles lack of precision and circuit noise better than existing algorithms.

A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

Science.gov (United States)

Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

2015-04-01

Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

Interconnecting Networks of Practice for Professional Learning

Directory of Open Access Journals (Sweden)

Julie Mackey

2011-03-01

Full Text Available The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers create their own networks of practice as they blend online and offline interactions with fellow learners and workplace colleagues. Teachers’ perspectives of their professional learning activities challenge the way universities design formal online learning communities and highlight the potential for networked learning in the zones and intersections between professional practice and study.The article extends the concepts of Lave and Wenger’s (1991 communities of practice social theory of learning by considering the role participants play in determining their engagement and connections in and across boundaries between online learning communities and professional practice. It provides insights into the applicability of connectivist concepts for developing online pedagogies to promote socially networked learning and for emphasising the role of the learner in defining their learning pathways.

Redes de aprendizaje, aprendizaje en red Learning Networks, Networked Learning

Directory of Open Access Journals (Sweden)

Peter Sloep

2011-10-01

Full Text Available Las redes de aprendizaje (Learning Networks son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades o educación no-formal (formación profesional. Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks

Projective Simulation compared to reinforcement learning

OpenAIRE

Bjerland, Øystein Førsund

2015-01-01

This thesis explores the model of projective simulation (PS), a novel approach for an artificial intelligence (AI) agent. The model of PS learns by interacting with the environment it is situated in, and allows for simulating actions before real action is taken. The action selection is based on a random walk through the episodic & compositional memory (ECM), which is a network of clips that represent previous experienced percepts. The network takes percepts as inpu...

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

Energy Technology Data Exchange (ETDEWEB)

Chen, Barry Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-11-29

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

Collaborative Learning in Advanced Supply Systems: The KLASS Pilot Project.

Science.gov (United States)

Rhodes, Ed; Carter, Ruth

2003-01-01

The Knowledge and Learning in Advanced Supply Systems (KLASS) project developed collaborative learning networks of suppliers in the British automotive and aerospace industries. Methods included face-to-face and distance learning, work toward National Vocational Qualifications, and diagnostic workshops for senior managers on improving quality,…

Learning and coding in biological neural networks

Science.gov (United States)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

Learning Analytics for Networked Learning Models

Science.gov (United States)

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

The TENCompetence Infrastructure: A Learning Network Implementation

Science.gov (United States)

Vogten, Hubert; Martens, Harrie; Lemmers, Ruud

The TENCompetence project developed a first release of a Learning Network infrastructure to support individuals, groups and organisations in professional competence development. This infrastructure Learning Network infrastructure was released as open source to the community thereby allowing users and organisations to use and contribute to this development as they see fit. The infrastructure consists of client applications providing the user experience and server components that provide the services to these clients. These services implement the domain model (Koper 2006) by provisioning the entities of the domain model (see also Sect. 18.4) and henceforth will be referenced as domain entity services.

Learning Networks for Lifelong Learning

OpenAIRE

Sloep, Peter

2009-01-01

Presentation in a seminar organized by Christopher Hoadley at Penn State University, October 2004.Contains general introduction into the Learning Network Programme and a demonstration of the Netlogo Simulation of a Learning Network.

Learning Initiatives for Network Economies in Asia (LIRNEasia ...

International Development Research Centre (IDRC) Digital Library (Canada)

Learning Initiatives for Network Economies in Asia (LIRNEasia) : Building Capacity in ICT Policy ... LIRNEasia seeks to build capacity for evidence-based interventions in the public policy process by persons attuned to the ... Project status.

Home and away : learning in and learning from organisational networks in Europe

NARCIS (Netherlands)

Docherty, P.; Huzzard, T.; Leede, J. de

2003-01-01

This report is a comparative analysis of the various learning networks established within the Innoflex Project. The report recaps on the central argument underpinning Innoflex, namely that traditional ways of organising workplaces and traditional styles of management cannot achieve the commitment,

Developing student engagement in networked teaching and learning practices through problem- and project-based learning approaches

DEFF Research Database (Denmark)

Andreasen, Lars Birch; Lerche Nielsen, Jørgen

2012-01-01

This paper focuses on how learner engagement can be facilitated through use of social media and communication technologies. The discussions are based on the Danish Master’s Programme of ICT and Learning (MIL), where students study in groups within a networked learning structure. The paper reflect...

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

Science.gov (United States)

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

Evaluation of the Project Management Competences Based on the Semantic Networks

Directory of Open Access Journals (Sweden)

Constanta Nicoleta BODEA

2008-01-01

Full Text Available The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

Science.gov (United States)

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

The Role of Action Research in the Development of Learning Networks for Entrepreneurs

Science.gov (United States)

Brett, Valerie; Mullally, Martina; O'Gorman, Bill; Fuller-Love, Nerys

2012-01-01

Developing sustainable learning networks for entrepreneurs is the core objective of the Sustainable Learning Networks in Ireland and Wales (SLNIW) project. One research team drawn from the Centre for Enterprise Development and Regional Economy at Waterford Institute of Technology and the School of Management and Business from Aberystwyth…

Educational designs supporting student engagement through networked project studies

DEFF Research Database (Denmark)

Lerche Nielsen, Jørgen; Andreasen, Lars Birch

2013-01-01

within a networked learning structure are studying in groups combining on-site seminars with independent and challenging virtually organized project periods, implementing new educational technology, which require teachers who are flexible and aware of the different challenges in the networked environment...... activities that unfold. This interplay is important in order to make a difference, as the experience is that new technologies do not in themselves guarantee increasing learning quality. The chapter will discuss examples of how learners as well as teachers have developed imaginative ways of implementing new...... technological possibilities in educational settings. The examples will include how sometimes seemingly simple technologies can be used in innovative pedagogical ways to increase learners’ involvement. Another example to be discussed in the chapter derives from an online seminar on ICT and Learning...

Research, Boundaries, and Policy in Networked Learning

DEFF Research Database (Denmark)

This book presents cutting-edge, peer reviewed research on networked learning organized by three themes: policy in networked learning, researching networked learning, and boundaries in networked learning. The "policy in networked learning" section explores networked learning in relation to policy...... networks, spaces of algorithmic governance and more. The "boundaries in networked learning" section investigates frameworks of students' digital literacy practices, among other important frameworks in digital learning. Lastly, the "research in networked learning" section delves into new research methods...

Individual Learning in Construction Projects: Professions and their Approaches

Directory of Open Access Journals (Sweden)

Ingeborg Wasif

2010-10-01

Full Text Available New materials, use of sophisticated technologies and increased customer demands, in combination with growing competition among construction companies, have led to a high organizational boundaries. The results indicate that personal networks are the most common source of learning for all professions. While clients, architects, and designers also engage in reading and attending courses, site managers and workers are less engaged in these activities. Experimenting and organizing for learning appear to be underutilized strategies by all professions. This leads to the conclusion that attempts to increase learning have to address the differences in learning behaviours of the various groups. Further, focus on experimenting and organizing for learning is a possibility to change the learning behaviour from learning as a consequence of problems to learning for future improvement.degree of specialization. For successful integration of the different professional specialists, there is a need for shared learning between project co-workers. Based on twenty eight interviews in six different Swedish construction projects, this paper illustrates strategies for individual and shared learning, among different actors and across various

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Brouns, Francis; Sloep, Peter

2009-01-01

Brouns, F., & Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation of the Learning Network Programme for a Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and

Network Learning and Innovation in SME Formal Networks

Directory of Open Access Journals (Sweden)

Jivka Deiters

2013-02-01

Full Text Available The driver for this paper is the need to better understand the potential for learning and innovation that networks canprovide especially for small and medium sized enterprises (SMEs which comprise by far the majority of enterprises in the food sector. With the challenges the food sector is facing in the near future, learning and innovation or more focused, as it is being discussed in the paper, ‘learning for innovation’ are not just opportunities but pre‐conditions for the sustainability of the sector. Network initiatives that could provide appropriate support involve social interaction and knowledge exchange, learning, competence development, and coordination (organization and management of implementation. The analysis identifies case studies in any of these orientations which serve different stages of the innovation process: invention and implementation. The variety of network case studies cover networks linked to a focus group for training, research, orconsulting, networks dealing with focused market oriented product or process development, promotional networks, and networks for open exchange and social networking.

Networked curricula: fostering transnational partnership in open and distance learning

Directory of Open Access Journals (Sweden)

María Luz Cacheiro-González

2013-05-01

Full Text Available Transnational Networked Curricula (TNC provides many benefits to the institutions that offer them as well as to the different stakeholders involved, not only the students but also the academics, the institutions as a whole, and the wider society. Supporting Higher Education Institutions in enhancing and implementing international networked practices in virtual campus building is the main aim of the NetCU project, which has been developed by the EADTU, in partnership with 14 member organizations, from 2009 to 2012. The project outcomes intend to facilitate the future set-up of networked curricula in Higher Education institutions and potentially lead to more transnational partnerships in Open and Distance Education (ODE and blended learning, showing challenges, obstacles and ways to overcome them. This paper presents the main products developed in the project, assesses its completeness and usage, and discusses on the challenges of curricula networking starting from the ideas and opinions shared in different stakeholders workshops organized under the NetCU project.

Learning Methods for Radial Basis Functions Networks

Czech Academy of Sciences Publication Activity Database

Neruda, Roman; Kudová, Petra

2005-01-01

Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

Social Media in Adult Education: Insights Gained from Grundtvig Learning Partnership Project “Institutional Strategies Targeting the Uptake of Social Networking in Adult Education (ISTUS”

Directory of Open Access Journals (Sweden)

Vilhelmina Vaičiūnienė

2013-02-01

Full Text Available Purpose—the focus of this article is the role of social media in adult education and their impact on adult students in regards to their educational needs and specific personal situations within the frame of the learning partnership project. The Grundtvig learning partnership project “Institutional Strategies Targeting the Uptake of Social Networking in Adult Education (ISTUS is an international partnership that includes partners from 7 EU countries. The aim of the research presented in this paper is to define cases of the uptake of technologies and applications by MRU students; thus, the objectives of the research are 1 to review literature pertaining to the field of social media in adult education context and 2 to analyse the respondents’ insights as regards learning/teaching practices, resources, and facilities that affect their learning in relation to social networking and media use (taking into consideration both personal and educational perspectives. Design/methodology/approach—the research paper adopts qualitative research approach. Findings—students perceive SM mainly as online communication means (usually informal communication is implied. SM is firstly associated by learners with pastime venue, not educational resource. Thus, methods of teaching/learning in SM and with the help of SM have to be developed and improved. They have to be considered in line with the necessity to develop critical and reflexive thinking skills and media and information literacy skills. The respondents have pointed out both positive and negative aspects of social media use for learning/teaching. Creation of an inner institutional SM type involving qualified people with expertise in SM use for education has been suggested. Research limitations/implications—this article is focused only on the attitudes of MRU students though 105 interviews in total have been conducted within the framework of the project and not only students, but also teachers and

Social Media in Adult Education: Insights Gained from Grundtvig Learning Partnership Project “Institutional Strategies Targeting the Uptake of Social Networking in Adult Education (ISTUS”

Directory of Open Access Journals (Sweden)

Vilhelmina Vaičiūnienė

2012-12-01

Full Text Available Purpose—the focus of this article is the role of social media in adult education and their impact on adult students in regards to their educational needs and specific personal situations within the frame of the learning partnership project. The Grundtvig learning partnership project “Institutional Strategies Targeting the Uptake of Social Networking in Adult Education (ISTUS is an international partnership that includes partners from 7 EU countries. The aim of the research presented in this paper is to define cases of the uptake of technologies and applications by MRU students; thus, the objectives of the research are 1 to review literature pertaining to the field of social media in adult education context and 2 to analyse the respondents’ insights as regards learning/teaching practices, resources, and facilities that affect their learning in relation to social networking and media use (taking into consideration both personal and educational perspectives.Design/methodology/approach—the research paper adopts qualitative research approach.Findings—students perceive SM mainly as online communication means (usually informal communication is implied. SM is firstly associated by learners with pastime venue, not educational resource. Thus, methods of teaching/learning in SM and with the help of SM have to be developed and improved. They have to be considered in line with the necessity to develop critical and reflexive thinking skills and media and information literacy skills. The respondents have pointed out both positive and negative aspects of social media use for learning/teaching. Creation of an inner institutional SM type involving qualified people with expertise in SM use for education has been suggested.Research limitations/implications—this article is focused only on the attitudes of MRU students though 105 interviews in total have been conducted within the framework of the project and not only students, but also teachers and

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

Science.gov (United States)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Supervised dictionary learning for inferring concurrent brain networks.

Science.gov (United States)

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

Blending Formal and Informal Learning Networks for Online Learning

Science.gov (United States)

Czerkawski, Betül C.

2016-01-01

With the emergence of social software and the advance of web-based technologies, online learning networks provide invaluable opportunities for learning, whether formal or informal. Unlike top-down, instructor-centered, and carefully planned formal learning settings, informal learning networks offer more bottom-up, student-centered participatory…

Project ECHO: A Telementoring Network Model for Continuing Professional Development.

Science.gov (United States)

Arora, Sanjeev; Kalishman, Summers G; Thornton, Karla A; Komaromy, Miriam S; Katzman, Joanna G; Struminger, Bruce B; Rayburn, William F

2017-01-01

A major challenge with current systems of CME is the inability to translate the explosive growth in health care knowledge into daily practice. Project ECHO (Extension for Community Healthcare Outcomes) is a telementoring network designed for continuing professional development (CPD) and improving patient outcomes. The purpose of this article was to describe how the model has complied with recommendations from several authoritative reports about redesigning and enhancing CPD. This model links primary care clinicians through a knowledge network with an interprofessional team of specialists from an academic medical center who provide telementoring and ongoing education enabling community clinicians to treat patients with a variety of complex conditions. Knowledge and skills are shared during weekly condition-specific videoconferences. The model exemplifies learning as described in the seven levels of CPD by Moore (participation, satisfaction, learning, competence, performance, patient, and community health). The model is also aligned with recommendations from four national reports intended to redesign knowledge transfer in improving health care. Efforts in learning sessions focus on information that is relevant to practice, focus on evidence, education methodology, tailoring of recommendations to individual needs and community resources, and interprofessionalism. Project ECHO serves as a telementoring network model of CPD that aligns with current best practice recommendations for CME. This transformative initiative has the potential to serve as a leading model for larger scale CPD, nationally and globally, to enhance access to care, improve quality, and reduce cost.

Networked professional learning

NARCIS (Netherlands)

Sloep, Peter

2013-01-01

Sloep, P. B. (2013). Networked professional learning. In A. Littlejohn, & A. Margaryan (Eds.), Technology-enhanced Professional Learning: Processes, Practices and Tools (pp. 97–108). London: Routledge.

Developing an online professional network for veterinary education: the NOVICE project.

Science.gov (United States)

Baillie, Sarah; Kinnison, Tierney; Forrest, Neil; Dale, Vicki H M; Ehlers, Jan P; Koch, Michael; Mándoki, Mira; Ciobotaru, Emilia; de Groot, Esther; Boerboom, Tobias B B; van Beukelen, Peter

2011-01-01

An online professional network for veterinarians, veterinary students, veterinary educationalists, and ICT (Information and Communication Technology) educationalists is being developed under the EU (European Union) Lifelong Learning Programme. The network uses Web 2.0, a term used to describe the new, more interactive version of the Internet, and includes tools such as wikis, blogs, and discussion boards. Focus groups conducted with qualified and student veterinarians within the project's five founding countries (The Netherlands, Germany, United Kingdom, Hungary, Romania) demonstrated that online professional communities can be valuable for accessing information and establishing contacts. Online networks have the potential to overcome common challenges to face-to-face communities-such as distance, cost, and timing-but they have their own drawbacks, such as security and professionalism issues. The Network Of Veterinary ICt in Education (NOVICE) was developed using Elgg, an open-source, free social networking platform, after several software options had been considered. NOVICE aims to promote the understanding of Web 2.0, confidence to use social software tools, and participation in an online community. Therefore, the Web site contains help sections, Frequently Asked Questions, and access to support from ICT experts. Five months after the network's launch (and just over one year into the project) 515 members from 28 countries had registered. Further research will include analysis of a core group's activities, which will inform ongoing support for and development of informal, lifelong learning in a veterinary context.

Time, Space and Structure in an E-Learning and E-Mentoring Project

Science.gov (United States)

Loureiro-Koechlin, Cecilia; Allan, Barbara

2010-01-01

This study focuses on a project, "EMPATHY Net-Works," which developed a learning community as a means of encouraging women to progress into employment and management positions in the logistics and supply chain industries (LaSCI). Learning activities were organised in the form of a taught module containing face-to-face and online elements and…

Problem and Project Based Learning in Hybrid Spaces

DEFF Research Database (Denmark)

Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

2016-01-01

There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem...... and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how students incorporate networked and digital technologies into their group work and into the study places...... they create for themselves. We describe how in one of the programmes ‘nomadic’ groups of students used different technologies and spaces for ‘placemaking’. We then show how their experience and approach to collaborative work differs to that of the more static or ‘artisan’ groups of students in the other...

A Framework for Collaborative Networked Learning in Higher Education: Design & Analysis

Directory of Open Access Journals (Sweden)

Ghassan F. Issa

2014-06-01

Full Text Available This paper presents a comprehensive framework for building collaborative learning networks within higher educational institutions. This framework focuses on systems design and implementation issues in addition to a complete set of evaluation, and analysis tools. The objective of this project is to improve the standards of higher education in Jordan through the implementation of transparent, collaborative, innovative, and modern quality educational programs. The framework highlights the major steps required to plan, design, and implement collaborative learning systems. Several issues are discussed such as unification of courses and program of studies, using appropriate learning management system, software design development using Agile methodology, infrastructure design, access issues, proprietary data storage, and social network analysis (SNA techniques.

Learning conditional Gaussian networks

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

National Research Council Canada - National Science Library

Pazzan, Michael

1997-01-01

.... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

‘Living' theory: a pedagogical framework for process support in networked learning

Directory of Open Access Journals (Sweden)

Philipa Levy

2006-12-01

Full Text Available This paper focuses on the broad outcome of an action research project in which practical theory was developed in the field of networked learning through case-study analysis of learners' experiences and critical evaluation of educational practice. It begins by briefly discussing the pedagogical approach adopted for the case-study course and the action research methodology. It then identifies key dimensions of four interconnected developmental processes–orientation, communication, socialisation and organisation–that were associated with ‘learning to learn' in the course's networked environment, and offers a flavour of participants' experiences in relation to these processes. A number of key evaluation issues that arose are highlighted. Finally, the paper presents the broad conceptual framework for the design and facilitation of process support in networked learning that was derived from this research. The framework proposes a strong, explicit focus on support for process as well as domain learning, and progression from tighter to looser design and facilitation structures for process-focused (as well as domain-focused learning tasks.

Social Interaction in Learning Networks

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

The original publication is available from www.springerlink.com. Sloep, P. (2009). Social Interaction in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp 13-15). Berlin, Germany: Springer Verlag.

New designing of E-Learning systems with using network learning

OpenAIRE

Malayeri, Amin Daneshmand; Abdollahi, Jalal

2010-01-01

One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

Characteristic imsets for learning Bayesian network structure

Czech Academy of Sciences Publication Activity Database

Hemmecke, R.; Lindner, S.; Studený, Milan

2012-01-01

Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

Investigating the Use of a Smartphone Social Networking Application on Language Learning

Science.gov (United States)

Sung, Ko-Yin; Poole, Frederick

2017-01-01

This study explored college students' use of a popular smartphone social networking application, WeChat, in a tandem language learning project. The research questions included (1) How do Chinese-English dyads utilize the WeChat app for weekly language learning?, and (2) What are the perceptions of the Chinese-English dyads on the use of the WeChat…

Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

Science.gov (United States)

Dart, Richard A; Egan, Brent M

2014-06-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

Science.gov (United States)

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

Fastest learning in small-world neural networks

International Nuclear Information System (INIS)

Simard, D.; Nadeau, L.; Kroeger, H.

2005-01-01

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

New tools for scientific learning in the EduSeis project: the e-learning experiment

Directory of Open Access Journals (Sweden)

A. Zollo

2007-06-01

Full Text Available The Educational Seismological Project (EduSeis is a scientific and educational project, the main aim of which is the development and implementation of new teaching methodologies in Earth Sciences, using seismology as a vehicle for scientific learning and awareness of earthquake risk. Within this framework, we have recently been experimenting with new learning and information approaches that are mainly aimed at a high school audience. In particular, we have designed, implemented and tested a model of an e-learning environment in a high school located in the surroundings of the Mt. Vesuvius volcano. The proposed e-learning model is built on the EduSeis concepts and educational materials (web-oriented, and is based on computer-supported collaborative learning. Ten teachers from different disciplines and fifty students at the I.T.I.S. “Majorana” technical high school (Naples have been taking part in a cooperative e-learning experiment in which the students have been working in small groups (communities. The learning process is assisted and supervised by the teachers. The evaluation of the results from this cooperative e-learning experiment has provided useful insights into the content and didactic value of the EduSeis modules and activities. The use of network utilities and the “Learning Community” approach promoted the exchange of ideas and expertises between students and teachers and allowed a new approach to the seismology teaching through a multidisciplinary study.

The Integration of Personal Learning Environments & Open Network Learning Environments

Science.gov (United States)

Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

2012-01-01

Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

Problem and Project Based Learning in Hybrid Spaces:Nomads and Artisans

OpenAIRE

Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

2016-01-01

There is a need within networked learning to understand and conceptualise the interplay between digital and physical spaces or what we could term hybrid spaces. Therefore, we discuss a recent study of students from two different programmes who are engaged in long-term, group-based problem and project based learning. Based on interviews, workshops and observations of students’ actual group practices in open, shared and flexible spaces in Aalborg University (AAU), we identify and discuss how st...

Learning Networks for Professional Development & Lifelong Learning

NARCIS (Netherlands)

Sloep, Peter

2009-01-01

Sloep, P. B. (2009). Learning Networks for Professional Development & Lifelong Learning. Presentation at a NeLLL seminar with Etienne Wenger held at the Open Universiteit Nederland. September, 10, 2009, Heerlen, The Netherlands.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

OpenAIRE

Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla

2012-01-01

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...

Edmodo social learning network for elementary school mathematics learning

Science.gov (United States)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Entropy Learning in Neural Network

Directory of Open Access Journals (Sweden)

Geok See Ng

2017-12-01

Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

Project-based learning with international collaboration for training biomedical engineers.

Science.gov (United States)

Krishnan, Shankar

2011-01-01

Training biomedical engineers while effectively keeping up with the fast paced scientific breakthroughs and the growth in technical innovations poses arduous challenges for educators. Traditional pedagogical methods are employed for coping with the increasing demands in biomedical engineering (BME) training and continuous improvements have been attempted with some success. Project-based learning (PBL) is an academic effort that challenges students by making them carry out interdisciplinary projects aimed at accomplishing a wide range of student learning outcomes. PBL has been shown to be effective in the medical field and has been adopted by other fields including engineering. The impact of globalization in healthcare appears to be steadily increasing which necessitates the inclusion of awareness of relevant international activities in the curriculum. Numerous difficulties are encountered when the formation of a collaborative team is tried, and additional difficulties occur as the collaboration team is extended to international partners. Understanding and agreement of responsibilities becomes somewhat complex and hence the collaborative project has to be planned and executed with clear understanding by all partners and participants. A model for training BME students by adopting PBL with international collaboration is proposed. The results of previous BME project work with international collaboration fit partially into the model. There were many logistic issues and constraints; however, the collaborative projects themselves greatly enhanced the student learning outcomes. This PBL type of learning experience tends to promote long term retention of multidisciplinary material and foster high-order cognitive activities such as analysis, synthesis and evaluation. In addition to introducing the students to experiences encountered in the real-life workforce, the proposed approach enhances developing professional contracts and global networking. In conclusion, despite

Quantitative learning strategies based on word networks

Science.gov (United States)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

Distributed Extreme Learning Machine for Nonlinear Learning over Network

Directory of Open Access Journals (Sweden)

Songyan Huang

2015-02-01

Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

Lessons Learnt from and Sustainability of Adopting a Personal Learning Environment & Network (Ple&N)

Science.gov (United States)

Tsui, Eric; Sabetzadeh, Farzad

2014-01-01

This paper describes the feedback from the configuration and deployment of a Personal Learning Environment & Network (PLE&N) tool to support peer-based social learning for university students and graduates. An extension of an earlier project in which a generic and PLE&N was deployed for all learners, the current PLE&N is a…

Learning Networks Distributed Environment

NARCIS (Netherlands)

Martens, Harrie; Vogten, Hubert; Koper, Rob; Tattersall, Colin; Van Rosmalen, Peter; Sloep, Peter; Van Bruggen, Jan; Spoelstra, Howard

2005-01-01

Learning Networks Distributed Environment is a prototype of an architecture that allows the sharing and modification of learning materials through a number of transport protocols. The prototype implements a p2p protcol using JXTA.

Web 2.0 Tasks in Action: EFL Learning in the U.S. Embassy School Election Project 2012

Directory of Open Access Journals (Sweden)

Joannis Kaliampos

2014-10-01

Full Text Available Exploring topics that are personally relevant and interesting to young adult English as a foreign language (EFL learners remains a core challenge in language teaching. At the same time, the advent of Web 2.0 applications has many repercussions for authentic language learning. The “U.S. Embassy School Election Project 2012” has addressed these questions by combining a close focus on the U.S. Presidential Election with an interactive project scenario. Over 1,400 students across Germany participated in this project and produced an election forecast for an assigned U.S. state based on a survey of regional news media and social network data. Their predictions were in many cases more accurate than those of major U.S. broadcasting networks. This paper discusses the general educational potential of such projects in the contexts of computer-assisted language learning (CALL, intercultural learning, and learning in a task-based project environment. The authors have applied a multimodal qualitative approach to analyze tasks and learner perceptions of tasks in the context of the election project. In a first step, the micro-perspective of the perception of web-based tasks is investigated by example of one selected task cycle and a focus group of three learners. The second part of the analysis represents a bird’s-eye view on the learner products arising out of such tasks.

Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

CERN Document Server

Hu, Fei

2012-01-01

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

Scheduling projects with multiskill learning effect.

Science.gov (United States)

Zha, Hong; Zhang, Lianying

2014-01-01

We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Collective Learning in Games through Social Networks

NARCIS (Netherlands)

Kosterman, S.; Gierasimczuk, N.; Armentano, M.G.; Monteserin, A.; Tang, J.; Yannibelli, V.

2015-01-01

This paper argues that combining social networks communication and games can positively influence the learning behavior of players. We propose a computational model that combines features of social network learning (communication) and game-based learning (strategy reinforcement). The focus is on

Collecting lessons learned : How project-based organizations in the oil and gas industry learn from their projects

NARCIS (Netherlands)

Buttler, T.

2016-01-01

Project-based organizations collect lessons learned in order to improve the performance of projects. They aim to repeat successes by using positive lessons learned, and to avoid repeating negative experiences by using negative lessons learned. Cooke-Davies (2002) claimed that the ability to learn

Project-Based Learning in Programmable Logic Controller

Science.gov (United States)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

Lessons learned about coordinating academic partnerships from an international network for health education.

Science.gov (United States)

Luo, Airong; Omollo, Kathleen Ludewig

2013-11-01

There is a growing trend of academic partnerships between U.S., Canadian, and European health science institutions and academic health centers in low- and middle-income countries. These partnerships often encounter challenges such as resource disparities and power differentials, which affect the motivations, expectations, balance of benefits, and results of the joint projects. Little has been discussed in previous literature regarding the communication and project management processes that affect the success of such partnerships. To fill the gap in the literature, the authors present lessons learned from the African Health Open Educational Resources Network, a multicountry, multiorganizational partnership established in May 2008. The authors introduce the history of the network, then discuss actively engaging stakeholders throughout the project's life cycle (design, planning, execution, and closure) through professional development, relationship building, and assessment activities. They focus on communication and management practices used to identify mutually beneficial project goals, ensure timely completion of deliverables, and develop sustainable sociotechnical infrastructure for future collaborative projects. These activities yielded an interactive process of action, assessment, and reflection to ensure that project goals and values were aligned with implementation. The authors conclude with a discussion of lessons learned and how the partnership project may serve as a model for other universities and academic health centers in high-income countries and low- and middle-income countries that are interested in or currently pursuing international academic partnerships.

Changing Conditions for Networked Learning?

DEFF Research Database (Denmark)

Ryberg, Thomas

2011-01-01

in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective...... of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal...

Learning-parameter adjustment in neural networks

Science.gov (United States)

Heskes, Tom M.; Kappen, Bert

1992-06-01

We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

Researching Design, Experience and Practice of Networked Learning

DEFF Research Database (Denmark)

Hodgson, Vivien; de Laat, Maarten; McConnell, David

2014-01-01

and final section draws attention to a growing topic of interest within networked learning: that of networked learning in informal practices. In addition, we provide a reflection on the theories, methods and settings featured in the networked learning research of the chapters. We conclude the introduction...

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

Networking European Universities through e-learning (reviewed text)

OpenAIRE

Dlouhá, Jana

2008-01-01

Virtual Campus for a Sustainable Europe (VCSE) network has been selected to be part of the EC DG EAC Inventory of innovative good practice on education for sustainable development. The main purpose of the Inventory is to show concrete examples which have been implemented in the Member States under the concept of ESD in formal and non-formal learning contexts and which are at the forefront as regards innovative approaches. Projects/programmes selected as innovative good practice will be use...

Cooperative Learning for Distributed In-Network Traffic Classification

Science.gov (United States)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Distributed Constrained Stochastic Subgradient Algorithms Based on Random Projection and Asynchronous Broadcast over Networks

Directory of Open Access Journals (Sweden)

Junlong Zhu

2017-01-01

Full Text Available We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

Contingent factors affecting network learning

OpenAIRE

Peters, Linda D.; Pressey, Andrew D.; Johnston, Wesley J.

2016-01-01

To increase understanding of the impact of individuals on organizational learning processes, this paper explores the impact of individual cognition and action on the absorptive capacity process of the wider network. In particular this study shows how contingent factors such as social integration mechanisms and power relationships influence how network members engage in, and benefit from, learning. The use of cognitive consistency and sensemaking theory enables examination of how these conting...

Learning and structure of neuronal networks

Indian Academy of Sciences (India)

We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

Network Visualization Project (NVP)

Science.gov (United States)

2016-07-01

Application data flow .............................................................................2 Fig. 2 Sample JSON data...interface supporting improved network analysis and network communication visualization. 2. Application Design NVP consists of 2 parts: back-end data...notation ( JSON ) format. This JSON is provided as input to the front-end application of the project. This interaction of the user with the back-end

ENEA e-Learn Platform for Development and Sustainability with International Renewable Energies Network

Directory of Open Access Journals (Sweden)

Anna Moreno

2007-03-01

Full Text Available The UNESCO office in Venice (the Regional Bureau for Science and Culture in Europe has promoted, in collaboration with the Italian Agency for New Technologies, Energy, and the Environment (ENEA, an e-learning project on renewable energy: the DESIRE-net project (Development and Sustainability with International Renewable Energies network. The project's aim is to share the best available knowledge on renewable energies among all the countries that have joined the project and exploit this knowledge at every level. Currently the project involves 30 Eastern European and Southern Mediterranean countries as well as Australia, Indonesia, and China.

Project Management in Real Time: A Service-Learning Project

Science.gov (United States)

Larson, Erik; Drexler, John A., Jr.

2010-01-01

This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

Lessons learned from decommissioning projects at Los Alamos National Laboratory

International Nuclear Information System (INIS)

Salazar, M.

1995-01-01

This paper describes lessons learned over the last 20 years from 12 decommissioning projects at Los Alamos National Laboratory. These lessons relate both to overall program management and to management of specific projects during the planning and operations phases. The issues include waste management; the National Environmental Policy Act (NEPA); the Resource Conservation and Recovery Act (RCRA); the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA); contracting; public involvement; client/customer interface; and funding. Key elements of our approach are to be proactive; follow the observation method; perform field activities concurrently; develop strategies to keep reportable incidents from delaying work; seek and use programs, methods, etc., in existence to shorten learning curves; network to help develop solutions; and avoid overstudying and overcharacterizing. This approach results in preliminary plans that require very little revision before implementation, reasonable costs and schedules, early acquisition of permits and NEPA documents, preliminary characterization reports, and contracting documents. Our track record is good -- the last four projects (uranium and plutonium-processing facility and three research reactors) have been on budget and on schedule

Safety culture and subcontractor network governance in a complex safety critical project

International Nuclear Information System (INIS)

Oedewald, Pia; Gotcheva, Nadezhda

2015-01-01

In safety critical industries many activities are currently carried out by subcontractor networks. Nevertheless, there are few studies where the core dimensions of resilience would have been studied in safety critical network activities. This paper claims that engineering resilience into a system is largely about steering the development of culture of the system towards better ability to anticipate, monitor, respond and learn. Thus, safety culture literature has relevance in resilience engineering field. This paper analyzes practical and theoretical challenges in applying the concept of safety culture in a complex, dynamic network of subcontractors involved in the construction of a new nuclear power plant in Finland, Olkiluoto 3. The concept of safety culture is in focus since it is widely used in nuclear industry and bridges the scientific and practical interests. This paper approaches subcontractor networks as complex systems. However, the management model of the Olkiluoto 3 project is to a large degree a traditional top-down hierarchy, which creates a mismatch between the management approach and the characteristics of the system to be managed. New insights were drawn from network governance studies. - Highlights: • We studied a relevant topical subject safety culture in nuclear new build project. • We integrated safety science challenges and network governance studies. • We produced practicable insights in managing safety of subcontractor networks

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

Directory of Open Access Journals (Sweden)

Mohd Ishak Bin Ismail

2016-02-01

Full Text Available Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by students in higher learning to communicate to each other, to carry out academic collaboration and sharing resources. Learning through social networking sites is based on the social interaction which learning are emphasizing on students, real world resources, active students` participation, diversity of learning resources and the use of digital tools to deliver meaningful learning. Many studies found the positive, neutral and negative impact of social networking sites on academic performance. Thus, this study will determine the relationship between Facebook usage and academic achievement. Also, it will investigate the association of social capital and academic collaboration to Facebook usage.

The Use Of Social Networking Sites For Learning In Institutions Of Higher Learning

Directory of Open Access Journals (Sweden)

Mange Gladys Nkatha

2015-08-01

Full Text Available Abstract Institutions of higher learning are facing greater challenges to change and subjected to various transformations in the surrounding environment including technology. These challenge and motivate them to explore new ways to improve their teaching approaches. This study sought to investigate the use of social networking site in institutions of higher learning. To this end two objectives were formulated 1 to investigate the current state of the use of social networking sites by the students 2 investigate how social networking sites can be used to promote authentic learning in institutions of higher learning. The study adopted exploratory approach using descriptive survey design where a sample of 10 67 students were picked from Jomo Kenyatta University of Agriculture and Technology JKUAT main campus. The findings indicate the use of social networking sites is a viable option as the students are not only members of social networking sites but also that majority have access to the requisite technological devices. Additionally recommendations for ensuring authentic learning were presented. The researcher recommends the exploration of the leveraging of the existing social networking sites for learning in conjunction with key stakeholders.

A geometric view on learning Bayesian network structures

Czech Academy of Sciences Publication Activity Database

Studený, Milan; Vomlel, Jiří; Hemmecke, R.

2010-01-01

Roč. 51, č. 5 (2010), s. 578-586 ISSN 0888-613X. [PGM 2008] R&D Projects: GA AV ČR(CZ) IAA100750603; GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * inclusion neighborhood * geometric neighborhood * GES algorithm Subject RIV: BA - General Mathematics Impact factor: 1.679, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-0342804. pdf

Structure of Small World Innovation Network and Learning Performance

Directory of Open Access Journals (Sweden)

Shuang Song

2014-01-01

Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

Directory of Open Access Journals (Sweden)

S. Munapo

2012-01-01

Full Text Available

ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

E-learning objects and actor-networks as configuring information literacy teaching

DEFF Research Database (Denmark)

Schreiber, Trine Louise

2017-01-01

Introduction. With actor-network theory (ANT) as the theoretical lens the aim of the paper is to examine attempts to build network for shaping information literacy teaching. Method. The paper is based on a study of a project in 2014-2016 where information professionals representing ten educational...... libraries produced and implemented e-learning objects in information literacy teaching. The material was collected through interviews, observations, documents and feedback sessions. Analysis. Latour´s concept of translation and Callon´s four translation moments are used to analyze the network building...... that a network configuring information literacy teaching based on new interactive roles has not been stabilized. Conclusion. The paper concludes that the strength of ANT is first of all the mediation of an overview of different kinds of actors involved in network building. Further, the paper proposes to combine...

Learning and Model-checking Networks of I/O Automata

DEFF Research Database (Denmark)

Mao, Hua; Jaeger, Manfred

2012-01-01

We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

Science.gov (United States)

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Networks and learning in game theory

NARCIS (Netherlands)

Kets, W.

2008-01-01

This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.

Learning as Issue Framing in Agricultural Innovation Networks

Science.gov (United States)

Tisenkopfs, Talis; Kunda, Ilona; Šumane, Sandra

2014-01-01

Purpose: Networks are increasingly viewed as entities of learning and innovation in agriculture. In this article we explore learning as issue framing in two agricultural innovation networks. Design/methodology/approach: We combine frame analysis and social learning theories to analyse the processes and factors contributing to frame convergence and…

Learning between projects: More than sending messages in bottles

OpenAIRE

Hartmann, Andreas; Dorée, André

2015-01-01

Although learning from projects has gained much importance in research and practice, progress in understanding and improving inter-project learning appears to be slight. We argue that the adoption of a sender/receiver approach limits the learning effectiveness in project-based organisations. Drawing upon the notion of learning as a social activity embedded in an organisational context, we develop the argument that learning from projects takes place within projects rooted in the historical, or...

A Decomposition Algorithm for Learning Bayesian Network Structures from Data

DEFF Research Database (Denmark)

Zeng, Yifeng; Cordero Hernandez, Jorge

2008-01-01

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

Cascade Error Projection Learning Algorithm

Science.gov (United States)

Duong, T. A.; Stubberud, A. R.; Daud, T.

1995-01-01

A detailed mathematical analysis is presented for a new learning algorithm termed cascade error projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters.

Learning Bayesian Networks with Incomplete Data by Augmentation

OpenAIRE

Adel, Tameem; de Campos, Cassio P.

2016-01-01

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

THE IMPACTS OF SOCIAL NETWORKING SITES IN HIGHER LEARNING

OpenAIRE

Mohd Ishak Bin Ismail; Ruzaini Bin Abdullah Arshah

2016-01-01

Social networking sites, a web-based application have permeated the boundary between personal lives and student lives. Nowadays, students in higher learning used social networking site such as Facebook to facilitate their learning through the academic collaboration which it further enhances students’ social capital. Social networking site has many advantages to improve students’ learning. To date, Facebook is the leading social networking sites at this time which it being widely used by stude...

Project-based faculty development for e-learning.

Science.gov (United States)

Vyas, Rashmi; Faith, Minnie; Selvakumar, Dhayakani; Pulimood, Anna; Lee, Mary

2016-12-01

The Christian Medical College, Vellore, in collaboration with Tufts University, Boston, conducted an advanced workshop in e-learning for medical faculty members in India. E-learning can enhance educational reforms for today's computer-literate generation, and keep faculty members up to speed in a rapidly changing world. The purpose of this paper is to report on the design and evaluation of a project-based faculty member development programme focused on developing faculty members as educators and as peer trainers who can use e-learning for educational reforms. During a 2-day workshop, 29 participants in groups of two or three developed 13 e-learning projects for implementation in their institutions. Evaluation of the workshop was through written feedback from the participants at the end of the workshop and by telephone interview with one participant from each project group at the end of one year. Content analysis of qualitative data was perfomed. The participants reported that they were motivated to implement e-learning projects and recognised the need for and usefulness of e-learning. The majority of projects (10 out of 13) that were implemented 'to some extent' or 'to a great extent' faced challenges with a lack of resources and administrative support, but faculty members were able to overcome them. E-learning can enhance educational reforms for today's computer-literate generation IMPLICATIONS: Designing feasible e-learning projects in small groups and obtaining hands-on experience with e-learning tools enhance the effectiveness of subsequent implementation. To successfully incorporate e-learning when designing educational reforms, faculty member training, continuing support and infrastructure facilities are essential. © 2016 John Wiley & Sons Ltd.

Learning dynamic Bayesian networks with mixed variables

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

A pilot project on non-conventional learning

OpenAIRE

Fernandes, Sara; Cerone, Antonio; Barbosa, L. S.

2013-01-01

This poster presents a pilot project on non-conventional learning strategies based on students’ active participation in real-life FLOSS projects. The aim of the project is to validate the hypothesis that the peer-production model, which underlies most FLOSS projects, can enhance the learning-teaching process based on extensive and systematic collaborative practices.

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Science.gov (United States)

Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

2016-01-01

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

Identifying Gatekeepers in Online Learning Networks

Science.gov (United States)

Gursakal, Necmi; Bozkurt, Aras

2017-01-01

The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

Robust Learning of Fixed-Structure Bayesian Networks

OpenAIRE

Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

2016-01-01

We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

Statistical and machine learning approaches for network analysis

CERN Document Server

Dehmer, Matthias

2012-01-01

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

Information Flows in Networked Engineering Design Projects

DEFF Research Database (Denmark)

Parraguez, Pedro; Maier, Anja

Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....

Learning Latent Structure in Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

Thermodynamic efficiency of learning a rule in neural networks

Science.gov (United States)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

Science.gov (United States)

Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

2016-01-01

This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

Creating a Project-Based Learning Environment to Improve Project Management Skills of Graduate Students

Science.gov (United States)

Arantes do Amaral, Joao Alberto; Gonçalves, Paulo; Hess, Aurélio

2015-01-01

This article describes the project-based learning environment created to support project management graduate courses. The paper will focus on the learning context and procedures followed for 13 years, in 47 project-based learning MBA courses, involving approximately 1,400 students and 34 community partners.

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

Science.gov (United States)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

Teachers’ motives for learning in networks : costs, rewards and community interest

NARCIS (Netherlands)

van den Beemt, A.A.J.; Ketelaar, E.; Diepstraten, I.; de Laat, M.

2018-01-01

Background: This paper discusses teachers’ perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers’ learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of

Project based learning in organizations: towards a methodology for learning in groups

NARCIS (Netherlands)

Poell, R.F.; Krogt, F.J. van der

2003-01-01

This article introduces a methodology for employees in organizations to set up and carry out their own group learning projects. It is argued that employees can use project-based learning to make their everyday learning more systematic at times, without necessarily formalizing it. The article

Project-based learning in organizations : Towards a methodology for learning in groups

NARCIS (Netherlands)

Poell, R.F.; van der Krogt, F.J.

2003-01-01

This article introduces a methodology for employees in organizations to set up and carry out their own group learning projects. It is argued that employees can use project-based learning to make their everyday learning more systematic at times, without necessarily formalizing it. The article

Social network analysis of a project-based introductory physics course

Science.gov (United States)

Oakley, Christopher

2016-03-01

Research suggests that students benefit from peer interaction and active engagement in the classroom. The quality, nature, effect of these interactions is currently being explored by Physics Education Researchers. Spelman College offers an introductory physics sequence that addresses content and research skills by engaging students in open-ended research projects, a form of Project-Based Learning. Students have been surveyed at regular intervals during the second semester of trigonometry-based course to determine the frequency of interactions in and out of class. These interactions can be with current or past students, tutors, and instructors. This line of inquiry focuses on metrics of Social Network analysis, such as centrality of participants as well as segmentation of groups. Further research will refine and highlight deeper questions regarding student performance in this pedagogy and course sequence.

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Evolving autonomous learning in cognitive networks.

Science.gov (United States)

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.

A Survey of Technologies Supporting Virtual Project Based Learning

DEFF Research Database (Denmark)

Dirckinck-Holmfeld, Lone

2002-01-01

This paper describes a survey of technologies and to what extent they support virtual project based learning. The paper argues that a survey of learning technologies should be related to concrete learning tasks and processes. Problem oriented project pedagogy (POPP) is discussed, and a framework...... for evaluation is proposed where negotiation of meaning, coordination and resource management are identified as the key concepts in virtual project based learning. Three e-learning systems are selected for the survey, Virtual-U, Lotus Learningspace and Lotus Quickplace, as each system offers different strategies...... for e-learning. The paper concludes that virtual project based learning may benefit from facilities of all these systems....

Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

Science.gov (United States)

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-01-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning,…

Efficient learning strategy of Chinese characters based on network approach.

Directory of Open Access Journals (Sweden)

Xiaoyong Yan

Full Text Available We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming; Zhang, Jian

2009-01-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Tamara Gvozdenović

2007-06-01

Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

The Role of Electronic Learning Technology in Networks Systems

International Nuclear Information System (INIS)

Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.

2016-01-01

Recently, Electronic Learning Technology (ELT) has been widely spread as one of the new technologies in the world through using Information and Communication Technology (ICT). One of the strategies of ELT is Simulation, for instance Military and Medical simulations that are used to avoid risks and reduce Costs. A wireless communication network refers to any network not physically connected by cables, which enables the desired convenience and mobility for the user. Wireless communication networks have been useful in areas such as commerce, education and defense. According to the nature of a particular application, they can be used in home-based and industrial systems or in commercial and military environments. Historically, Mobile Ad-hoc Networks (MANET) have primarily been used for tactical military network related applications to improve battlefield communications/ survivability. MANET is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Mobility in wireless networks basically refers to nodes changing its point of attachment to the network. Also, how the end terminals can move, there are many mobility models described the movement of nodes, many researchers use the Random Way point Mobility Model (RWPM). In this paper, a Graphical User Interface (GUI) for RWPM simulation is introduced as a proposal to be used through ELT Project. In the research area of computer and communications networks, simulation is a very useful technique for the behavior of networks

How Can Procurement Contribute to Network Performance? Streamlining Network, Project and Procurement Objectives

NARCIS (Netherlands)

Leendertse, W.; Arts, J.; De Ridder, H.

2012-01-01

The core business of governmental organizations like Rijkswaterstaat in the Netherlands is the optimal management of road-end waterway networks. The coming years many maintenance, renewal and extension projects will be executed in these networks. Projects give a disturbance in functionality of the

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Blended learning in situated contexts: 3-year evaluation of an online peer review project.

Science.gov (United States)

Bridges, S; Chang, J W W; Chu, C H; Gardner, K

2014-08-01

Situated and sociocultural perspectives on learning indicate that the design of complex tasks supported by educational technologies holds potential for dental education in moving novices towards closer approximation of the clinical outcomes of their expert mentors. A cross-faculty-, student-centred, web-based project in operative dentistry was established within the Universitas 21 (U21) network of higher education institutions to support university goals for internationalisation in clinical learning by enabling distributed interactions across sites and institutions. This paper aims to present evaluation of one dental faculty's project experience of curriculum redesign for deeper student learning. A mixed-method case study approach was utilised. Three cohorts of second-year students from a 5-year bachelor of dental surgery (BDS) programme were invited to participate in annual surveys and focus group interviews on project completion. Survey data were analysed for differences between years using multivariate logistical regression analysis. Thematic analysis of questionnaire open responses and interview transcripts was conducted. Multivariate logistic regression analysis noted significant differences across items over time indicating learning improvements, attainment of university aims and the positive influence of redesign. Students perceived the enquiry-based project as stimulating and motivating, and building confidence in operative techniques. Institutional goals for greater understanding of others and lifelong learning showed improvement over time. Despite positive scores, students indicated global citizenship and intercultural understanding were conceptually challenging. Establishment of online student learning communities through a blended approach to learning stimulated motivation and intellectual engagement, thereby supporting a situated approach to cognition. Sociocultural perspectives indicate that novice-expert interactions supported student development of

Learning Bayesian network structure: towards the essential graph by integer linear programming tools

Czech Academy of Sciences Publication Activity Database

Studený, Milan; Haws, D.

2014-01-01

Roč. 55, č. 4 (2014), s. 1043-1071 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * integer linear programming * characteristic imset * essential graph Subject RIV: BA - General Mathematics Impact factor: 2.451, year: 2014 http://library.utia.cas.cz/separaty/2014/MTR/studeny-0427002.pdf

Multi-modal Social Networks: A MRF Learning Approach

Science.gov (United States)

2016-06-20

Network forensics: random infection vs spreading epidemic , Proceedings of ACM Sigmetrics. 11-JUN-12, London, UK. : , TOTAL: 4 06/09/2016 Received Paper...Multi-modal Social Networks A MRF Learning Approach The work primarily focused on two lines of research. 1. We propose new greedy algorithms...Box 12211 Research Triangle Park, NC 27709-2211 social networks , learning and inference REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT

An empirical comparison of popular structure learning algorithms with a view to gene network inference

Czech Academy of Sciences Publication Activity Database

Djordjilović, V.; Chiogna, M.; Vomlel, Jiří

2017-01-01

Roč. 88, č. 1 (2017), s. 602-613 ISSN 0888-613X R&D Projects: GA ČR(CZ) GA16-12010S Institutional support: RVO:67985556 Keywords : Bayesian networks * Structure learning * Reverse engineering * Gene networks Subject RIV: JD - Computer Applications, Robotics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.845, year: 2016 http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0477168.pdf

Learning between projects: More than sending messages in bottles

NARCIS (Netherlands)

Hartmann, Andreas; Dorée, André

2015-01-01

Although learning from projects has gained much importance in research and practice, progress in understanding and improving inter-project learning appears to be slight. We argue that the adoption of a sender/receiver approach limits the learning effectiveness in project-based organisations. Drawing

Contexts of Learning: The PATOIS project and Internet-based teaching and learning in Higher Education

Directory of Open Access Journals (Sweden)

William Kilbride

2002-10-01

Full Text Available This article is a reflection on the problems, challenges and strengths of network-based distance learning in archaeology. Based on the experience of one project - the PATOIS (Publications and Archives Teaching with Online Information Systems Project - it looks at how archaeologists might best respond (and by implication how they ought not to respond to the use of information technology in teaching. The PATOIS project is an attempt on behalf of a consortium of UK higher education institutions and allied research bodies to tell students about the information tools that are emerging in archaeology, and which are changing the culture of scholarship. Funded by the Joint Information Systems Committee (JISC and led by the Archaeology Data Service (ADS, PATOIS presents students with these new research tools and novel forms of academic literacy by direct exposure to 'primary' datasets. The PATOIS project is producing a set of Internet-based tutorials that lead students through different datasets and show how they may be deployed in research. This article describes the institutional and intellectual background to the project, and reports on the content of the tutorials themselves. Perhaps more importantly, it looks at the process through which PATOIS was developed, reviewing the challenges and constraints that the development team faced. Thereafter, we turn to the implementation of PATOIS in real teaching scenarios and look at how and when these have been successful as well as the challenges that remain unanswered. The project is not yet complete, so at this stage we can come to no firm conclusions about the long-term impact of PATOIS in facilitating change in undergraduate research training. Nonetheless, from the perspective of development work, the project has largely been completed, so those conclusions that may be drawn are most appropriately addressed to developers hoping or planning to undertake similar work in the future, or academics looking to

Globally Networked Collaborative Learning in Industrial Design

Science.gov (United States)

Bohemia, Erik; Ghassan, Aysar

2012-01-01

This article explores project-based cross-cultural and cross-institutional learning. Using Web 2.0 technologies, this project involved more than 240 students and eighteen academic staff from seven international universities. The focus of this article relates to a project-based learning activity named "The Gift". At each institution the…

Follow-groups, Enhancing Learning Potential at Project Exams

DEFF Research Database (Denmark)

Tollestrup, Christian H. T.

2016-01-01

In the Problem Based, Project Oriented Learning Program of Industrial Design Engineering at AAU students work and are examined/evaluated in groups. Following a period of a 6 years of ban on group-based exams by the government, the return of the group-based exam at Universities in 2014 has...... and the supervisor. Having the group based exam re-introduced sparked the interest for even further utilizing the exam situation for enhancing the learning outcome for each project and at the same time promote a more open atmosphere. Can the students learn even more and/or put their own project learning...... into perspective by seeing other project exams? So in order to investigate whether there was a possibility to further enhance the learning potential and understanding of the learning outcome the study board for the Architecture & Design program opened for a trial period for 2 semesters for voluntarily organizing...

Machine Learning Topological Invariants with Neural Networks

Science.gov (United States)

Zhang, Pengfei; Shen, Huitao; Zhai, Hui

2018-02-01

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

Balancing Design Project Supervision and Learning Facilitation

DEFF Research Database (Denmark)

Nielsen, Louise Møller

2012-01-01

experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......’s balance between the roles: 1) Design Project Supervisor – and 2) Learning Facilitator – with the aim to understand when to apply the different roles, and what to be aware of when doing so. This paper represents the first pilot-study of a larger research effort. It is based on a Lego Serious Play workshop......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

Functionality for learning networks: lessons learned from social web applications

NARCIS (Netherlands)

Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

2007-01-01

Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

Social networks and performance in distributed learning communities

OpenAIRE

Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

On-line learning in radial basis functions networks

OpenAIRE

Freeman, Jason; Saad, David

1997-01-01

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Adaptive Learning in Weighted Network Games

NARCIS (Netherlands)

Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

2017-01-01

This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

Service-learning and learning communities: two innovative school projects that are mutually enriched

Directory of Open Access Journals (Sweden)

Carmen ÁLVAREZ ÁLVAREZ

2015-12-01

Full Text Available This article reflects on the interrelationships that exist between two educational projects of today: service-learning (ApS and learning communities (CdA. The ApS is an educational methodology applied worldwide where a single project combines a learning based on experience with the implementation of a service to the community. CdA is a school transformation project to achieve that the information society does not exclude any person, constituting a reality in more than one hundred and ninety schools in Spain and Latin America. Between the two, it is possible to show differences, especially in what refers to its theoretical substrates, but in actual teaching practice in schools there is some harmony, particularly in the so closely that they cultivate both projects with the school community. Therefore, we conclude that service-learning and learning communities can occur as two innovative and relevant today projects which can be mutually enriching: because for both the approach school-community-environment and volunteering is essential.

Interconversion of Fischer and Zig-Zag Projections Learning ...

Indian Academy of Sciences (India)

IAS Admin

Stereochemistry, conforma- tional analysis, hands-on learn- ing, Fischer projections, zig-zag projection, C–C bond rotations. Interconversion of Fischer and Zig-Zag Projections. Learning Stereochemistry with the Help of Hands. Visualization of molecules in three dimensions is an important aspect of organic chemistry.

Networks of Learning

Science.gov (United States)

Bettencourt, Luis; Kaiser, David

2004-03-01

Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

Continuum of eLearning: 2012 Project Summary Report

Science.gov (United States)

2012-10-01

multimedia, and Continuum of eLearning | Purpose and Vision 19 << UNCLASSIFIED>> (limited) situated learning. Future versions of the CoL self-paced...Continuum of eLearning : 2012 Project Summary Report Continuum of eLearning The Next Evolution of Joint Training on JKO October 2012 Joint...Technical Report November 2011 – August 2012 Continuum of eLearning : 2012 Project Summary Report N00140-06-D-0060 David T. Fautua, Sae Schatz, Andrea

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Evolution of individual versus social learning on social networks.

Science.gov (United States)

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Learning oncogenetic networks by reducing to mixed integer linear programming.

Science.gov (United States)

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

ORGANIZATION OF FUTURE ENGINEERS' PROJECT-BASED LEARNING WHEN STUDYING THE PROJECT MANAGEMENT METHODOLOGY

Directory of Open Access Journals (Sweden)

Halyna V. Lutsenko

2015-02-01

Full Text Available The peculiarities of modern world experience of implementation of project-based learning in engineering education have been considered. The potential role and place of projects in learning activity have been analyzed. The methodology of organization of project-based activity of engineering students when studying the project management methodology and computer systems of project management has been proposed. The requirements to documentation and actual results of students' projects have been described in detail. The requirements to computer-aided systems of project management developed by using Microsoft Project in the scope of diary scheduling and resources planning have been formulated.

The LEONARDO-DA-VINCI pilot project "e-learning-assistant" - Situation-based learning in nursing education.

Science.gov (United States)

Pfefferle, Petra Ina; Van den Stock, Etienne; Nauerth, Annette

2010-07-01

E-learning will play an important role in the training portfolio of students in higher and vocational education. Within the LEONARDO-DA-VINCI action programme transnational pilot projects were funded by the European Union, which aimed to improve the usage and quality of e-learning tools in education and professional training. The overall aim of the LEONARDO-DA-VINCI pilot project "e-learning-assistant" was to create new didactical and technical e-learning tools for Europe-wide use in nursing education. Based on a new situation-oriented learning approach, nursing teachers enrolled in the project were instructed to adapt, develop and implement e- and blended learning units. According to the training contents nursing modules were developed by teachers from partner institutions, implemented in the project centers and evaluated by students. The user-package "e-learning-assistant" as a product of the project includes two teacher training units, the authoring tool "synapse" to create situation-based e-learning units, a student's learning platform containing blended learning modules in nursing and an open sourced web-based communication centre. Copyright 2009 Elsevier Ltd. All rights reserved.

DEVELOPMENT MODEL OF PATISSERIE PROJECT-BASED LEARNING

OpenAIRE

Ana Ana; Lutfhiyah Nurlaela

2013-01-01

The study aims to find a model of patisserie project-based learning with production approach that can improve effectiveness of patisserie learning. Delphi Technique, Cohen's Kappa and percentages of agreements were used to assess model of patisserie project based learning. Data collection techniques employed in the study were questionnaire, check list worksheet, observation, and interview sheets. Subjects were 13 lectures of expertise food and nutrition and 91 students of Food and Nutrition ...

The Mobile Learning Network: Getting Serious about Games Technologies for Learning

Science.gov (United States)

Petley, Rebecca; Parker, Guy; Attewell, Jill

2011-01-01

The Mobile Learning Network currently in its third year, is a unique collaborative initiative encouraging and enabling the introduction of mobile learning in English post-14 education. The programme, funded jointly by the Learning and Skills Council and participating colleges and schools and supported by LSN has involved nearly 40,000 learners and…

Learning drifting concepts with neural networks

NARCIS (Netherlands)

Biehl, Michael; Schwarze, Holm

1993-01-01

The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

The Use of an Educational Social Networking Site for English Language Learning beyond the Classroom in a Japanese University Setting

Science.gov (United States)

Okumura, Shinji

2016-01-01

This study describes an attempt of using an educational social networking platform, which is called Edmodo, for English language learning outside classrooms at tertiary level. Considering the notion of communicative competence, the instructor incorporated Edmodo into his English classes as a project which is a formal assignment. In the project,…

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Science.gov (United States)

Wenhui, Ma; Yu, Wang

2017-06-01

Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

How Teaching Science Using Project-Based Learning Strategies Affects the Classroom Learning Environment

Science.gov (United States)

Hugerat, Muhamad

2016-01-01

This study involved 458 ninth-grade students from two different Arab middle schools in Israel. Half of the students learned science using project-based learning strategies and the other half learned using traditional methods (non-project-based). The classes were heterogeneous regarding their achievements in the sciences. The adapted questionnaire…

Maximum entropy methods for extracting the learned features of deep neural networks.

Science.gov (United States)

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Student Learning Projects in Electric Vehicle Engineering

DEFF Research Database (Denmark)

Ritchie, Ewen; Leban, Krisztina Monika

2012-01-01

This paper presents the didactic problem based learning method in general use at Aalborg University as applied to Electric Mobility. Advantage is taken of this method to link student learning to current research projects. This offers advantages to the students and the researchers. The paper...... introduces the subject, presents the research of the Department of Energy Technology and describes the relevant syllabus. It continues to present a range of titles of previous research linked student project projects, and to fill in some of the detail, an example of such a student project. The paper...

How to Trigger Emergence and Self-Organisation in Learning Networks

Science.gov (United States)

Brouns, Francis; Fetter, Sibren; van Rosmalen, Peter

The previous chapters of this section discussed why the social structure of Learning Networks is important and present guidelines on how to maintain and allow the emergence of communities in Learning Networks. Chapter 2 explains how Learning Networks rely on social interaction and active participations of the participants. Chapter 3 then continues by presenting guidelines and policies that should be incorporated into Learning Network Services in order to maintain existing communities by creating conditions that promote social interaction and knowledge sharing. Chapter 4 discusses the necessary conditions required for knowledge sharing to occur and to trigger communities to self-organise and emerge. As pointed out in Chap. 4, ad-hoc transient communities facilitate the emergence of social interaction in Learning Networks, self-organising them into communities, taking into account personal characteristics, community characteristics and general guidelines. As explained in Chap. 4 community members would benefit from a service that brings suitable people together for a specific purpose, because it will allow the participant to focus on the knowledge sharing process by reducing the effort or costs. In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.

Teachers' Motives for Learning in Networks: Costs, Rewards and Community Interest

Science.gov (United States)

van den Beemt, Antoine; Ketelaar, Evelien; Diepstraten, Isabelle; de Laat, Maarten

2018-01-01

Background: This paper discusses teachers' perspectives on learning networks and their motives for participating in these networks. Although it is widely held that teachers' learning may be developed through learning networks, not all teachers participate in such networks. Purpose: The theme of reciprocity, central to studies in the area of…

The Effectiveness of Project Based Learning in Trigonometry

Science.gov (United States)

Gerhana, M. T. C.; Mardiyana, M.; Pramudya, I.

2017-09-01

This research aimed to explore the effectiveness of Project-Based Learning (PjBL) with scientific approach viewed from interpersonal intelligence toward students’ achievement learning in mathematics. This research employed quasi experimental research. The subjects of this research were grade X MIPA students in Sleman Yogyakarta. The result of the research showed that project-based learning model is more effective to generate students’ mathematics learning achievement that classical model with scientific approach. This is because in PjBL model students are more able to think actively and creatively. Students are faced with a pleasant atmosphere to solve a problem in everyday life. The use of project-based learning model is expected to be the choice of teachers to improve mathematics education.

Growing the Data Refuge Project into a Local Libraries+ Network Node

Science.gov (United States)

Janz, M.

2017-12-01

The Data Refuge project began as concerns were raised about the vulnerability of federal climate and environmental data. The concern stemmed from the idea that if the federal agency that curates the data lost funding, and with it staff and infrastructure, that the data could be lost. The team worked to determine what factors contributed to the vulnerability of these data and how we might mitigate their risks. After speaking with many partners and collaborators around the country who all had different roles and perspectives working with these data, we saw the landscape of government data in new ways. We began seeing potential in various initiatives to ensure continued access to these data regardless of political, technological, or other risks. One recurring theme in our assessment was that libraries would be natural backup stewards for federal data.From and with our partners, we learned just how complicated the problem of creating networks of backup stewards for government data would be, but also how important it is to make the effort. As Data Refuge moves into its next stages into the Libraries+ Network, we're making plans to work with our partners on federal, state, and local projects that address different aspects of the problem space. These projects are examples of ways to approach this problem in concert with a variety of stakeholders.

Project-Based Learning Not Just for STEM Anymore

Science.gov (United States)

Duke, Nell K.; Halvorsen, Anne-Lise; Strachan, Stephanie L.

2016-01-01

The popularity of project-based learning has been driven in part by a growing number of STEM schools and programs. But STEM subjects are not the only fertile ground for project-based learning (PBL). Social studies and literacy content, too, can be adapted into PBL units to benefit teaching and learning, the authors argue. They review key studies…

Lessons Learned from Client Projects in an Undergraduate Project Management Course

Science.gov (United States)

Pollard, Carol E.

2012-01-01

This work proposes that a subtle combination of three learning methods offering "just in time" project management knowledge, coupled with hands-on project management experience can be particularly effective in producing project management students with employable skills. Students were required to apply formal project management knowledge to gain…

Fuzzy comprehensive evaluation model of interuniversity collaborative learning based on network

Directory of Open Access Journals (Sweden)

Wenhui Ma

2017-06-01

Full Text Available Learning evaluation is an effective method, which plays an important role in the network education evaluation system. But most of the current network learning evaluation methods still use traditional university education evaluation system, which do not take into account of web-based learning characteristics, and they are difficult to fit the rapid development of interuniversity collaborative learning based on network. Fuzzy comprehensive evaluation method is used to evaluate interuniversity collaborative learning based on the combination of fuzzy theory and analytic hierarchy process. Analytic hierarchy process is used to determine the weight of evaluation factors of each layer and to carry out the consistency check. According to the fuzzy comprehensive evaluation method, we establish interuniversity collaborative learning evaluation mathematical model. The proposed scheme provides a new thought for interuniversity collaborative learning evaluation based on network.

Learning from project experiences using a legacy-based approach

Science.gov (United States)

Cooper, Lynne P.; Majchrzak, Ann; Faraj, Samer

2005-01-01

As project teams become used more widely, the question of how to capitalize on the knowledge learned in project teams remains an open issue. Using previous research on shared cognition in groups, an approach to promoting post-project learning was developed. This Legacy Review concept was tested on four in tact project teams. The results from those test sessions were used to develop a model of team learning via group cognitive processes. The model and supporting propositions are presented.

Learning Physics through Project-Based Learning Game Techniques

Science.gov (United States)

Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

2018-01-01

The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…

Collaborative Project-Based Learning: An Integrative Science and Technological Education Project

Science.gov (United States)

Baser, Derya; Ozden, M. Yasar; Karaarslan, Hasan

2017-01-01

Background: Blending collaborative learning and project-based learning (PBL) based on Wolff (2003) design categories, students interacted in a learning environment where they developed their technology integration practices as well as their technological and collaborative skills. Purpose: The study aims to understand how seventh grade students…

Social networks as ICT collaborative and supportive learning media ...

African Journals Online (AJOL)

... ICT collaborative and supportive learning media utilisation within the Nigerian educational system. The concept of ICT was concisely explained vis-à-vis the social network concept, theory and collaborative and supportive learning media utilisation. Different types of social network are highlighted among which Facebook, ...

Accounting Early for Life Long Learning: The AcE Project.

Science.gov (United States)

University Coll. Worcester (England). Centre for Research in Early Childhood Education.

Building upon the work of the Effective Early Learning (EEL) Project in raising the quality of early learning for young children in the United Kingdom, the 3-year Accounting Early for Life Long Learning Project (AcE Project) focuses on enhancing in 3- to 6-year-olds those attitudes and dispositions that are important to life-long learning. This…

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

Science.gov (United States)

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

Problem-based and project-oriented learning

DEFF Research Database (Denmark)

Blaabjerg, Frede; Teodorescu, Remus; Chen, Zhe

2005-01-01

. Generally, the content of the curriculum should be more expanded without extra study time. This paper presents a teaching approach, which makes it possible very fast for the students to obtain in-depth skills into new research areas, and this method is the problem-oriented and project-based learning....... In this paper the necessary skills for power electronic engineers are outlined that is followed up by a description on how the problem-oriented and project-based learning are implemented. A complete curriculum in power electronics and drives at Aalborg University is presented where different power electronics...... related projects at different study levels also are presented....

Brentwood Lessons Learned Project Report

Energy Technology Data Exchange (ETDEWEB)

Rivkin, Carl H. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Caton, Melanie C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Ainscough, Christopher D. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Marcinkoski, Jason [Dept. of Energy (DOE), Washington DC (United States)

2017-09-26

The purpose of this report is to document lessons learned in the installation of the hydrogen fueling station at the National Park Service Brentwood site in Washington, D.C., to help further the deployment of hydrogen infrastructure required to support hydrogen and other fuel cell technologies. Hydrogen fueling is the most difficult infrastructure component to build and permit. Hydrogen fueling can include augmenting hydrogen fueling capability to existing conventional fuel fueling stations as well as building brand new hydrogen fueling stations. This report was produced as part of the Brentwood Lessons Learned project. The project consisted of transplanting an existing modular hydrogen fueling station from Connecticut to the National Park Service Brentwood site. This relocation required design and construction at the Brentwood site to accommodate the existing station design as well as installation and validation of the updated station. One of the most important lessons learned was that simply moving an existing modular station to an operating site was not necessarily straight-forward - performing the relocation required significant effort and cost. The station has to function at the selected operating site and this functionality requires a power supply, building supports connecting to an existing alarm system, electrical grounding and lighting, providing nitrogen for purging, and providing deionized water if an electrolyzer is part of the station package. Most importantly, the station has to fit into the existing site both spatially and operationally and not disrupt existing operations at the site. All of this coordination and integration requires logistical planning and project management. The idea that a hydrogen fueling station can be simply dropped onto a site and made immediately operational is generally not realistic. Other important lessons learned include that delineating the boundaries of the multiple jurisdictions that have authority over a project for

Exploring Practice-Research Networks for Critical Professional Learning

Science.gov (United States)

Appleby, Yvon; Hillier, Yvonne

2012-01-01

This paper discusses the contribution that practice-research networks can make to support critical professional development in the Learning and Skills sector in England. By practice-research networks we mean groups or networks which maintain a connection between research and professional practice. These networks stem from the philosophy of…

STAR Library Education Network: a hands-on learning program for libraries and their communities

Science.gov (United States)

Dusenbery, P.

2010-12-01

Science and technology are widely recognized as major drivers of innovation and industry (e.g. Rising above the Gathering Storm, 2006). While the focus for education reform is on school improvement, there is considerable research that supports the role that out-of-school experiences can play in student achievement and public understanding of STEM disciplines. Libraries provide an untapped resource for engaging underserved youth and their families in fostering an appreciation and deeper understanding of science and technology topics. Designed spaces, like libraries, allow lifelong, life-wide, and life-deep learning to take place though the research basis for learning in libraries is not as developed as other informal settings like science centers. The Space Science Institute’s National Center for Interactive Learning (NCIL) in partnership with the American Library Association (ALA), the Lunar and Planetary Institute (LPI), and the National Girls Collaborative Project (NGCP) have received funding from NSF to develop a national education project called the STAR Library Education Network: a hands-on learning program for libraries and their communities (or STAR-Net for short). STAR stands for Science-Technology, Activities and Resources. The overarching goal of the project is to reach underserved youth and their families with informal STEM learning experiences. This project will deepen our knowledge of informal/lifelong learning that takes place in libraries and establish a learning model that can be compared to the more established free-choice learning model for science centers and museums. The project includes the development of two STEM hands-on exhibits on topics that are of interest to library staff and their patrons: Discover Earth and Discover Tech. In addition, the project will produce resources and inquiry-based activities that libraries can use to enrich the exhibit experience. Additional resources will be provided through partnerships with relevant

LeaRN: A Collaborative Learning-Research Network for a WLCG Tier-3 Centre

Science.gov (United States)

Pérez Calle, Elio

2011-12-01

The Department of Modern Physics of the University of Science and Technology of China is hosting a Tier-3 centre for the ATLAS experiment. A interdisciplinary team of researchers, engineers and students are devoted to the task of receiving, storing and analysing the scientific data produced by the LHC. In order to achieve the highest performance and to develop a knowledge base shared by all members of the team, the research activities and their coordination are being supported by an array of computing systems. These systems have been designed to foster communication, collaboration and coordination among the members of the team, both face-to-face and remotely, and both in synchronous and asynchronous ways. The result is a collaborative learning-research network whose main objectives are awareness (to get shared knowledge about other's activities and therefore obtain synergies), articulation (to allow a project to be divided, work units to be assigned and then reintegrated) and adaptation (to adapt information technologies to the needs of the group). The main technologies involved are Communication Tools such as web publishing, revision control and wikis, Conferencing Tools such as forums, instant messaging and video conferencing and Coordination Tools, such as time management, project management and social networks. The software toolkit has been deployed by the members of the team and it has been based on free and open source software.

LeaRN: A Collaborative Learning-Research Network for a WLCG Tier-3 Centre

International Nuclear Information System (INIS)

Calle, Elio Pérez

2011-01-01

The Department of Modern Physics of the University of Science and Technology of China is hosting a Tier-3 centre for the ATLAS experiment. A interdisciplinary team of researchers, engineers and students are devoted to the task of receiving, storing and analysing the scientific data produced by the LHC. In order to achieve the highest performance and to develop a knowledge base shared by all members of the team, the research activities and their coordination are being supported by an array of computing systems. These systems have been designed to foster communication, collaboration and coordination among the members of the team, both face-to-face and remotely, and both in synchronous and asynchronous ways. The result is a collaborative learning-research network whose main objectives are awareness (to get shared knowledge about other's activities and therefore obtain synergies), articulation (to allow a project to be divided, work units to be assigned and then reintegrated) and adaptation (to adapt information technologies to the needs of the group). The main technologies involved are Communication Tools such as web publishing, revision control and wikis, Conferencing Tools such as forums, instant messaging and video conferencing and Coordination Tools, such as time management, project management and social networks. The software toolkit has been deployed by the members of the team and it has been based on free and open source software.

Logarithmic learning for generalized classifier neural network.

Science.gov (United States)

Ozyildirim, Buse Melis; Avci, Mutlu

2014-12-01

Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards a Social Networks Model for Online Learning & Performance

Science.gov (United States)

Chung, Kon Shing Kenneth; Paredes, Walter Christian

2015-01-01

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

WEB 2.0 SERVICES AS A TECHNOLOGICAL FOUNDATION OF A NETWORK PROJECT

Directory of Open Access Journals (Sweden)

Tatiana Ivanovna Kanyanina

2015-02-01

Full Text Available In the light of the requirements of the federal state educational standard increases the value of the network design based on the use of Web 2.0 services. The article outlines the range of technological challenges faced by the developer of the network project, such as the choice of the network platform of the project, design tools and tools for project results placement, project coordination and organization of communication lines, the choice of instruments for the promotion of the project on the Internet. The main attention is focused on the examples of network services that address these challenges, and describe their features. The authors rely on the specific network projects implemented in Nizhny Novgorod region. The paper analyzes the possible project’s network sites (network environment. The article gives a general description of each environment, marks its distinctive features, advantages and disadvantages. The article presents project tasks’ examples, designed on the basis of the variety of Web 2.0 services that functionally meet the requirements of a particular task, examples of services to represent the project products and to summarize its results. Attention is paid to the organization of the lines of communication of the project participants and organizers and to the role of network services in the project promotion on the Internet.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Science.gov (United States)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Mudigonda, Mayur; Prabhat; Spentzouris, Panagiotis; Spiropoulou, Maria; Tsaris, Aristeidis; Vlimant, Jean-Roch; Zheng, Stephan

2017-08-01

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Directory of Open Access Journals (Sweden)

Farrell Steven

2017-01-01

Full Text Available Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM and convolutional neural networks to find and fit tracks in toy detector data.

Network theory-based analysis of risk interactions in large engineering projects

International Nuclear Information System (INIS)

Fang, Chao; Marle, Franck; Zio, Enrico; Bocquet, Jean-Claude

2012-01-01

This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project. - Highlights: ► The method addresses the modeling of complexity in project risk analysis. ► Network theory indicators enable other risks than classical criticality analysis to be highlighted. ► This topological analysis improves project manager's understanding of risks and risk interactions. ► This helps project manager to make decisions considering the position in the risk network. ► An application to a real tramway implementation project in a city is provided.

Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

Science.gov (United States)

Margolis, Alvaro; Parboosingh, John

2015-01-01

Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

Stochastic project networks temporal analysis, scheduling and cost minimization

CERN Document Server

Neumann, Klaus

1990-01-01

Project planning, scheduling, and control are regularly used in business and the service sector of an economy to accomplish outcomes with limited resources under critical time constraints. To aid in solving these problems, network-based planning methods have been developed that now exist in a wide variety of forms, cf. Elmaghraby (1977) and Moder et al. (1983). The so-called "classical" project networks, which are used in the network techniques CPM and PERT and which represent acyclic weighted directed graphs, are able to describe only projects whose evolution in time is uniquely specified in advance. Here every event of the project is realized exactly once during a single project execution and it is not possible to return to activities previously carried out (that is, no feedback is permitted). Many practical projects, however, do not meet those conditions. Consider, for example, a production process where some parts produced by a machine may be poorly manufactured. If an inspection shows that a part does no...

Network anomaly detection a machine learning perspective

CERN Document Server

Bhattacharyya, Dhruba Kumar

2013-01-01

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Social Networking Sites and Language Learning

Science.gov (United States)

Brick, Billy

2011-01-01

This article examines a study of seven learners who logged their experiences on the language leaning social networking site Livemocha over a period of three months. The features of the site are described and the likelihood of their future success is considered. The learners were introduced to the Social Networking Site (SNS) and asked to learn a…

Promoting Collaboration in a Project-Based E-Learning Context

Science.gov (United States)

Papanikolaou, Kyparisia; Boubouka, Maria

2011-01-01

In this paper we investigate the value of collaboration scripts for promoting metacognitive knowledge in a project-based e-learning context. In an empirical study, 82 students worked individually and in groups on a project using the e-learning environment MyProject, in which the life cycle of a project is inherent. Students followed a particular…

Generalized projective synchronization of two coupled complex networks of different sizes

International Nuclear Information System (INIS)

Li Ke-Zan; He En; Zeng Zhao-Rong; Chi, K. Tse

2013-01-01

We investigate a new generalized projective synchronization between two complex dynamical networks of different sizes. To the best of our knowledge, most of the current studies on projective synchronization have dealt with coupled networks of the same size. By generalized projective synchronization, we mean that the states of the nodes in each network can realize complete synchronization, and the states of a pair of nodes from both networks can achieve projective synchronization. Using the stability theory of the dynamical system, several sufficient conditions for guaranteeing the existence of the generalized projective synchronization under feedback control and adaptive control are obtained. As an example, we use Chua's circuits to demonstrate the effectiveness of our proposed approach

Learning and forgetting on asymmetric, diluted neural networks

International Nuclear Information System (INIS)

Derrida, B.; Nadal, J.P.

1987-01-01

It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks

Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

NARCIS (Netherlands)

Koper, Rob; Sloep, Peter

2003-01-01

Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit

Disseminating Innovations in Teaching Value-Based Care Through an Online Learning Network.

Science.gov (United States)

Gupta, Reshma; Shah, Neel T; Moriates, Christopher; Wallingford, September; Arora, Vineet M

2017-08-01

A national imperative to provide value-based care requires new strategies to teach clinicians about high-value care. We developed a virtual online learning network aimed at disseminating emerging strategies in teaching value-based care. The online Teaching Value in Health Care Learning Network includes monthly webinars that feature selected innovators, online discussion forums, and a repository for sharing tools. The learning network comprises clinician-educators and health system leaders across North America. We conducted a cross-sectional online survey of all webinar presenters and the active members of the network, and we assessed program feasibility. Six months after the program launched, there were 277 learning community members in 22 US states. Of the 74 active members, 50 (68%) completed the evaluation. Active members represented independently practicing physicians and trainees in 7 specialties, nurses, educators, and health system leaders. Nearly all speakers reported that the learning network provided them with a unique opportunity to connect with a different audience and achieve greater recognition for their work. Of the members who were active in the learning network, most reported that strategies gleaned from the network were helpful, and some adopted or adapted these innovations at their home institutions. One year after the program launched, the learning network had grown to 364 total members. The learning network helped participants share and implement innovations to promote high-value care. The model can help disseminate innovations in emerging areas of health care transformation, and is sustainable without ongoing support after a period of start-up funding.

A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks

NARCIS (Netherlands)

De Jong, Tim; Fuertes, Alba; Schmeits, Tally; Specht, Marcus; Koper, Rob

2008-01-01

De Jong, T., Fuertes, A., Schmeits, T., Specht, M., & Koper, R. (2009). A Contextualised Multi-Platform Framework to Support Blended Learning Scenarios in Learning Networks. In D. Goh (Ed.), Multiplatform E-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (pp.

Support Vector Machines with Manifold Learning and Probabilistic Space Projection for Tourist Expenditure Analysis

Directory of Open Access Journals (Sweden)

Xin Xu

2009-03-01

Full Text Available The significant economic contributions of the tourism industry in recent years impose an unprecedented force for data mining and machine learning methods to analyze tourism data. The intrinsic problems of raw data in tourism are largely related to the complexity, noise and nonlinearity in the data that may introduce many challenges for the existing data mining techniques such as rough sets and neural networks. In this paper, a novel method using SVM- based classification with two nonlinear feature projection techniques is proposed for tourism data analysis. The first feature projection method is based on ISOMAP (Isometric Feature Mapping, which is a class of manifold learning approaches for dimension reduction. By making use of ISOMAP, part of the noisy data can be identified and the classification accuracy of SVMs can be improved by appropriately discarding the noisy training data. The second feature projection method is a probabilistic space mapping technique for scale transformation. Experimental results on expenditure data of business travelers show that the proposed method can improve prediction performance both in terms of testing accuracy and statistical coincidence. In addition, both of the feature projection methods are helpful to reduce the training time of SVMs.

Stochastic sensitivity analysis and Langevin simulation for neural network learning

International Nuclear Information System (INIS)

Koda, Masato

1997-01-01

A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method

Exploring an experiential learning project through Kolb's Learning Theory using a qualitative research method

Science.gov (United States)

Yuk Chan, Cecilia Ka

2012-08-01

Experiential learning pedagogy is taking a lead in the development of graduate attributes and educational aims as these are of prime importance for society. This paper shows a community service experiential project conducted in China. The project enabled students to serve the affected community in a post-earthquake area by applying their knowledge and skills. This paper documented the students' learning process from their project goals, pre-trip preparations, work progress, obstacles encountered to the final results and reflections. Using the data gathered from a focus group interview approach, the four components of Kolb's learning cycle, the concrete experience, reflection observation, abstract conceptualisation and active experimentation, have been shown to transform and internalise student's learning experience, achieving a variety of learning outcomes. The author will also explore how this community service type of experiential learning in the engineering discipline allowed students to experience deep learning and develop their graduate attributes.

Reinforcement learning account of network reciprocity.

Science.gov (United States)

Ezaki, Takahiro; Masuda, Naoki

2017-01-01

Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

Quantity versus Quality in Project Based Learning Practices

NARCIS (Netherlands)

A. Keegan (Anne); J.R. Turner (Rodney)

2000-01-01

textabstractIn the midst of the turbulence wrought by the global economy, it has become common to see projects as an essential medium for achieving change. However, project based learning practices - as a subset of organizational learning practices- have not kept pace with this development. To

Boltzmann learning of parameters in cellular neural networks

DEFF Research Database (Denmark)

Hansen, Lars Kai

1992-01-01

The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

Science.gov (United States)

López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth

2015-04-15

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Flexible Processes in Project-Centred Learning

NARCIS (Netherlands)

Ceri, Stefano; Matera, Maristella; Raffio, Alessandro; Spoelstra, Howard

2007-01-01

Ceri, S., Matera, M., Raffio, A. & Spoelstra, H. (2007). Flexible Processes in Project-Centred Learning. In E. Duval, R. Klamma, and M. Wolpers (Eds.), European Conference on Technology Enhanced Learning, Lecture Notes in Computer Science, Vol. 4753, pp. 463-468. Berlin Heidelberg: Springer-Verlag

Collaborative Supervised Learning for Sensor Networks

Science.gov (United States)

Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

2011-01-01

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Projective simulation for artificial intelligence

Science.gov (United States)

Briegel, Hans J.; de Las Cuevas, Gemma

2012-05-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.

Fundraising Strategies Developed by MBA students in Project-Based Learning Courses

Directory of Open Access Journals (Sweden)

Joao Alberto Arantes do Amaral

2016-12-01

Full Text Available The ability to raise funds is a skill that most modern project managers need. While a good deal of literature exists on the strategies NGOs employ to raise funds for their operations, less attention has been paid to the strategies used by students involved in Project-Based Learning courses that often partner with NGOs. Fundraising is an important skill that not only provides students with opportunities for creativity, but also helps them develop the communication skills they will need in the work they do after they graduate.In this paper, we discuss the fundraising strategies developed by MBA students in 204 social projects completed between 2002 and 2014. The projects were done in partnership with 39 community partners in Sao Paulo, Brazil (NGOs and Public Institutions. In our study, we followed quantitative and qualitative research methods, analyzing data and documents from the projects’ databases. We identified six different fundraising strategies: organizing raffles, soliciting donations from private corporations, organizing paying events, utilizing online social networks developing crowdfunding, and soliciting individual donations.

Learning State Space Dynamics in Recurrent Networks

Science.gov (United States)

Simard, Patrice Yvon

Fully recurrent (asymmetrical) networks can be used to learn temporal trajectories. The network is unfolded in time, and backpropagation is used to train the weights. The presence of recurrent connections creates internal states in the system which vary as a function of time. The resulting dynamics can provide interesting additional computing power but learning is made more difficult by the existence of internal memories. This study first exhibits the properties of recurrent networks in terms of convergence when the internal states of the system are unknown. A new energy functional is provided to change the weights of the units in order to the control the stability of the fixed points of the network's dynamics. The power of the resultant algorithm is illustrated with the simulation of a content addressable memory. Next, the more general case of time trajectories on a recurrent network is studied. An application is proposed in which trajectories are generated to draw letters as a function of an input. In another application of recurrent systems, a neural network certain temporal properties observed in human callosally sectioned brains. Finally the proposed algorithm for stabilizing dynamics around fixed points is extended to one for stabilizing dynamics around time trajectories. Its effects are illustrated on a network which generates Lisajous curves.

The e-Learning Conceptual Framework Project. Leaflet

OpenAIRE

Sangrà Morer, Albert

2011-01-01

Aquest projecte es basa en la hipòtesi que en l'actualitat no existeix un concepte únic d'e-learning -acceptat per la majoria de la comunitat científica o, és més, pels professionals. Per tant, l'objectiu és contribuir a la consolidació i l'especificació del concepte d'e-learning tant des del punt de vista científic com des del punt de vista del camp de l'activitat educativa. This project is based on the hypothesis that currently there is no single concept of e-learning -accepted by the ma...

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Science.gov (United States)

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Problems in the Deployment of Learning Networks In Small Organizations

NARCIS (Netherlands)

Shankle, Dean E.; Shankle, Jeremy P.

2006-01-01

Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

A Self-Organizing Incremental Neural Network based on local distribution learning.

Science.gov (United States)

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter diagnostics of phases and phase transition learning by neural networks

Science.gov (United States)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Reinforcement learning account of network reciprocity.

Directory of Open Access Journals (Sweden)

Takahiro Ezaki

Full Text Available Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

Renewable Resources: a national catalog of model projects. Volume 4. Western Solar Utilization Network Region

Energy Technology Data Exchange (ETDEWEB)

None

1980-07-01

This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Western Solar Utilization Network Region. (WHK)

Project Management Approaches for Online Learning Design

Science.gov (United States)

Eby, Gulsun; Yuzer, T. Volkan

2013-01-01

Developments in online learning and its design are areas that continue to grow in order to enhance students' learning environments and experiences. However, in the implementation of new technologies, the importance of properly and fairly overseeing these courses is often undervalued. "Project Management Approaches for Online Learning Design"…

Project Oriented Immersion Learning: Method and Results

DEFF Research Database (Denmark)

Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

2005-01-01

A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

Learning and digital inclusion: the ELAMP project

Directory of Open Access Journals (Sweden)

Kate D'Arcy

2012-12-01

Full Text Available The Electronic Learning and Mobility Project (ELAMP was a nationally funded project by the Department for Children, Schools and Families, which ran from 2004 to 2010. The main aim of ELAMP was to improve the education of Traveller children, particularly highly mobile learners. ELAMP focussed upon the use of mobile technology and distance learning to support, enhance and extend young Travellers’ educational and vocational opportunities. This article will reflect upon the learning and technological experiences and opportunities that the ELAMP project provided for Traveller children, young people and their families. In doing so it will critically consider the value of information technology in working with Traveller communities and advancing their educational opportunities. Reviewing ELAMP work will also demonstrate how the use of mobile technology can improve educational outcomes and Traveller families’ digital inclusion. Now that the project has ended, this article will question why we are not using what we learnt from ELAMP to move forward. The author was a tutor on the project who also evaluated the Strand B, Wider Key Skills element of ELAMP for The University of Sheffield between 2008 and 2010, which is the main focus of this particular article.

The Relationships Between Policy, Boundaries and Research in Networked Learning

DEFF Research Database (Denmark)

Ryberg, Thomas; Sinclair, Christine

2016-01-01

the books that include a selection of reworked and peer-reviewed papers from the conference. The 2014 Networked Learning Conference which was held in Edinburgh was characterised by animated dialogue on emergent influences affecting networked teaching and learning building on work established in earlier...

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

Science.gov (United States)

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

Science.gov (United States)

Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

2016-09-14

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

PROJECT BASED LEARNING BERMUATAN ETNOMATEMATIKA DALAM PEMBELAJAR MATEMATIKA

Directory of Open Access Journals (Sweden)

I Wayan Eka Mahendra

2017-03-01

Full Text Available This study aims to determine differences simultaneously in motivation and mathematics learning outcomes between students taking project based learningmodel charged ethnomathematics and students who followed the conventional learning modelon the class VIII SMP Negeri 3 Abiansemalyear 2016/2017. It was a quasi experiment with a sample of 71 student obtain by using simple random sampling. The data were analyzed by one-way multivariate analysis (Manova.The results of this study indicate that there are differences in simultaneously in learning motivation and learning outcomes between students taking mathematics model project based learning charged ethnomathematics and students who followed the conventional learning model on the class VIII SMP Negeri 3 Abiansemal year 2016/2017. Besed on the research findings, junior high school teachers are suggested to improve their student learning outcome for mathematics. Teachers also need to use a learning models accurately and correctly.

The Dynamics of Project-Based Learning Extension Courses: The "Laboratory of Social Projects" Case Study

Science.gov (United States)

Arantes do Amaral, Joao Alberto

2017-01-01

In this case study we discuss the dynamics that drive a free-of-charge project-based learning extension course. We discuss the lessons learned in the course, "Laboratory of Social Projects." The course aimed to teach project management skills to the participants. It was conducted from August to November of 2015, at Federal University of…

Learning through a Game - Exploring Fun and Learning in a Project Management Game

OpenAIRE

Hansen, Daniel Sollie; Storjord, David

2016-01-01

The goal of this thesis is to explore the teaching capabilities of games by motivating players through fun. We do this by first exploring perspectives of fun and learning in games; project management concepts and previous games. From these findings we implement our own game prototype where the player learns project management concepts simultaneously as they learn the game. This prototype is then evaluated through a number of experiments. Finally we discuss the results of the experiments and c...

A theoretical design for learning model addressing the networked society

DEFF Research Database (Denmark)

Levinsen, Karin; Nielsen, Janni; Sørensen, Birgitte Holm

2010-01-01

The transition from the industrial to the networked society produces contradictions that challenges the educational system and force it to adapt to new conditions. In a Danish virtual Master in Information and Communication Technologies and Learning (MIL) these contradictions appear as a field of...... which enables students to develop Networked Society competencies and maintain progression in the learning process also during the online periods. Additionally we suggest that our model contributes to the innovation of a networked society's design for learning....... is continuously decreasing. We teach for deep learning but are confronted by students' cost-benefit strategies when they navigate through the study programme under time pressure. To meet these challenges a Design for Learning Model has been developed. The aim is to provide a scaffold that ensures students......' acquisition of the subject matter within a time limit and at a learning quality that support their deep learning process during a subsequent period of on-line study work. In the process of moving from theory to application the model passes through three stages: 1) Conceptual modelling; 2) Orchestration, and 3...

CoCoRaHS (The Community Collaborative Rain, Hail and Snow Network): Analysis of Participant Survey Data to Uncover Learning through Participation

Science.gov (United States)

Holzer, M. A.; Zimmerman, T.; Doesken, N. J.; Reges, H. W.; Newman, N.; Turner, J.; Schwalbe, Z.

2010-12-01

CoCoRaHS (The Community Collaborative Rain, Hail and Snow network) is based out of Fort Collins Colorado and is an extremely successful citizen science project with over 15,000 volunteers collecting valuable precipitation data. Forecasters and scientists use data from this dense network to illuminate and illustrate the high small-scale variability of precipitation across the nation. This presentation will discuss the results of a survey of CoCoRaHS participants as related to 1) citizen scientists’ motivation and learning; 2) the challenges of identifying how people learn science in citizen science projects; and 3) a potential research-based framework for how people learn through engaging in the data collection within in a citizen science project. A comprehensive survey of 14,500 CoCoRaHS observers was recently conducted to uncover participant perceptions of numerous aspects of the CoCoRaHS program, including its goal of increasing climate literacy. The survey yielded a response rate of over 50%, and included measures of motivation, engagement and learning. In relationship to motivation and learning, the survey revealed that most (57.1%) observers would make precipitation observations regardless of being a CoCoRaHS volunteer, therefore their motivation is related to their inherent level of interest in weather. Others are motivated by their desire to learn more about weather and climate, they want to contribute to a scientific project, they think its fun, and/or it provides a sense of community. Because so many respondents already had knowledge and interest in weather and climate, identifying how and what people learn through participating was a challenge. However, the narrow project focus of collecting and reporting of local precipitation assisted in identifying aspects of learning. For instance, most (46.4%) observers said they increased their knowledge about the local variability in precipitation even though they had been collecting precipitation data for many

Collaboration Networks in Applied Conservation Projects across Europe.

Science.gov (United States)

Nita, Andreea; Rozylowicz, Laurentiu; Manolache, Steluta; Ciocănea, Cristiana Maria; Miu, Iulia Viorica; Popescu, Viorel Dan

2016-01-01

The main funding instrument for implementing EU policies on nature conservation and supporting environmental and climate action is the LIFE Nature programme, established by the European Commission in 1992. LIFE Nature projects (>1400 awarded) are applied conservation projects in which partnerships between institutions are critical for successful conservation outcomes, yet little is known about the structure of collaborative networks within and between EU countries. The aim of our study is to understand the nature of collaboration in LIFE Nature projects using a novel application of social network theory at two levels: (1) collaboration between countries, and (2) collaboration within countries using six case studies: Western Europe (United Kingdom and Netherlands), Eastern Europe (Romania and Latvia) and Southern Europe (Greece and Portugal). Using data on 1261 projects financed between 1996 and 2013, we found that Italy was the most successful country not only in terms of awarded number of projects, but also in terms of overall influence being by far the most influent country in the European LIFE Nature network, having the highest eigenvector (0.989) and degree centrality (0.177). Another key player in the network is Netherlands, which ensures a fast communication flow with other network members (closeness-0.318) by staying connected with the most active countries. Although Western European countries have higher centrality scores than most of the Eastern European countries, our results showed that overall there is a lower tendency to create partnerships between different organization categories. Also, the comparisons of the six case studies indicates significant differences in regards to the pattern of creating partnerships, providing valuable information on collaboration on EU nature conservation. This study represents a starting point in predicting the formation of future partnerships within LIFE Nature programme, suggesting ways to improve transnational

Project network-oriented materials management policy for complex projects

DEFF Research Database (Denmark)

Dixit, Vijaya; Srivastava, Rajiv K; Chaudhuri, Atanu

2015-01-01

This work devises a materials management policy integrated with project network characteristics of complex projects. It proposes a relative quantitative measure, overall criticality (OC), for prioritisation of items based on penalty incurred due to their non-availability. In complex projects...... managerial tacit knowledge which provides them enough flexibility to provide information in real form. Computed OC values can be used for items prioritisation and as shortage cost coefficient in inventory models. The revised materials management policy was applied to a shipbuilding project. OC values were......, practicing managers find it difficult to measure OC of items because of the subjective factors and intractable nature of penalties involved. However, using their experience, they can linguistically identify the antecedents and relate them to consequent OC. This work adopts Fuzzy Set Theory to capture...

Comparison between extreme learning machine and wavelet neural networks in data classification

Science.gov (United States)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Validating module network learning algorithms using simulated data.

Science.gov (United States)

Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves

2007-05-03

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network

Gamification of learning deactivates the Default Mode Network

Directory of Open Access Journals (Sweden)

Paul Alexander Howard-Jones

2016-01-01

Full Text Available We hypothesised that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN and deactivation of Default Mode Network (DMN regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer, Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards. DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

Gamification of Learning Deactivates the Default Mode Network.

Science.gov (United States)

Howard-Jones, Paul A; Jay, Tim; Mason, Alice; Jones, Harvey

2015-01-01

We hypothesized that embedding educational learning in a game would improve learning outcomes, with increased engagement and recruitment of cognitive resources evidenced by increased activation of working memory network (WMN) and deactivation of default mode network (DMN) regions. In an fMRI study, we compared activity during periods of learning in three conditions that were increasingly game-like: Study-only (when periods of learning were followed by an exemplar question together with its correct answer), Self-quizzing (when periods of learning were followed by a multiple choice question in return for a fixed number of points) and Game-based (when, following each period of learning, participants competed with a peer to answer the question for escalating, uncertain rewards). DMN hubs deactivated as conditions became more game-like, alongside greater self-reported engagement and, in the Game-based condition, higher learning scores. These changes did not occur with any detectable increase in WMN activity. Additionally, ventral striatal activation was associated with responding to questions and receiving positive question feedback. Results support the significance of DMN deactivation for educational learning, and are aligned with recent evidence suggesting DMN and WMN activity may not always be anti-correlated.

SONG-China Project: A Global Automated Observation Network

Science.gov (United States)

Yang, Z. Z.; Lu, X. M.; Tian, J. F.; Zhuang, C. G.; Wang, K.; Deng, L. C.

2017-09-01

Driven by advancements in technology and scientific objectives, data acquisition in observational astronomy has been changed greatly in recent years. Fully automated or even autonomous ground-based network of telescopes has now become a tendency for time-domain observational projects. The Stellar Observations Network Group (SONG) is an international collaboration with the participation and contribution of the Chinese astronomy community. The scientific goal of SONG is time-domain astrophysics such as asteroseismology and open cluster research. The SONG project aims to build a global network of 1 m telescopes equipped with high-precision and high-resolution spectrographs, and two-channel lucky-imaging cameras. It is the Chinese initiative to install a 50 cm binocular photometry telescope at each SONG node sharing the network platform and infrastructure. This work is focused on design and implementation in technology and methodology of SONG/50BiN, a typical ground-based network composed of multiple sites and a variety of instruments.

Learning, memory, and the role of neural network architecture.

Directory of Open Access Journals (Sweden)

Ann M Hermundstad

2011-06-01

Full Text Available The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Science.gov (United States)

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Hasan A. A. Al-Rawi

2014-01-01

Full Text Available Cognitive radio (CR enables unlicensed users (or secondary users, SUs to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs. Reinforcement learning (RL is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs.

A Newton-type neural network learning algorithm

International Nuclear Information System (INIS)

Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.

1993-01-01

First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab

Kinship--king's social harmonisation project. Pilot phase of a social network for use in higher education (HE).

Science.gov (United States)

John, B A

2013-05-08

Students entering Higher Education are increasingly information and communications technology literate. Many students (graduates and undergraduates) arrive as "digital residents", who are adept with social media and technologically fluent. The informal use of social media for learning is becoming increasingly evident, along with the potentially detrimental effects of a poor digital profile on employment prospects. This paper describes the creation of Kinship (King's Social Harmonisation Project), a university hosted, members only social network, which is currently being piloted in the Medical School at King's College London. Along with a number of other teaching and learning resources, it is intended to use Kinship to establish an informal code of conduct by modelling and moderating appropriate professional online behaviour. Kinship was developed using an open source Elgg platform, thanks to funding of £20,000 from the College Teaching Fund under the mentorship of Brighton University (1). This educational research project, led by Medicine, was proposed to select, customise and evaluate a social networking platform in order to provide functionality that would enhance new and existing e-learning resources, support group interaction, participation and sharing and meet the diverse needs of three academic schools: Medicine, the Dental Institute and two separate Departments, the Modern Languages Centre and the Department of English from Arts & Humanities, as a pilot for wider College deployment. Student involvement is central to the project, from conducting the evaluation to moulding and customising the functionality and look of Kinship, in order to ensure that the site is authentic and evolves in response to their wishes and requirements. Formal evaluation of Kinship commences summer 2012.

Introduction to the TD2005 build project network

International Nuclear Information System (INIS)

He Fucai

1997-01-01

TD2005 build project network includes three layers. the first layer is backbone network. The second layer has m level and each level has horizontal subnetwork composed of n cluster bin ND2002-n. The third layer is equipment. When the second layer has only 1 level, the number of the equipment entered is 28 x n (n =3D 1,2,3,......63), which are allowed linking to horizontal subnetwork of TD2005 network. The number of the equipment of per cluster bin ND2002-n is 28. TD2005 network is used for P300 project. It has highest running reliability and long-term stability as well as powerful suitability and flexibility. It has many tie points, strong real-time ability and excellent interference suppression. Through testing, all kinds of characteristics are satisfactory. It is an ideal selection for industrial use

Learning in innovation networks: Some simulation experiments

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Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

2007-05-01

According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

Impact of censoring on learning Bayesian networks in survival modelling.

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Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

Neural Network Machine Learning and Dimension Reduction for Data Visualization

Science.gov (United States)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Project Learning and Virtual Collaboration

DEFF Research Database (Denmark)

Fibiger, Bo; Nielsen, Janni; Sorensen, Elsebeth

2005-01-01

to the modularity and flexibility that characterize the study and allow admission of part-time students, full-time students and students who only sign up for one accredited module. The methodology will be illustrated through empirical snapshots from selected modules in the start-up phase, and the focus...... will be directed towards problems experienced by the students. From an analytical perspective, the paper will identify and discuss fundamental problems related to the organization, flexibility, and implementation of project pedagogy online. MIL is organized around ICT and Learning and the study theme focuses...... on ICT and Learning. In addition, MIL provides a learning space where practice is under constant negotiation and reconstruction as an inherent, integrated part of the learning process. Consequently, we argue that MIL may be seen as an example of best practice in blended learning....

Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

NARCIS (Netherlands)

Rajagopal, Kamakshi

2016-01-01

This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through

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

DEFF Research Database (Denmark)

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

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

Learning-induced pattern classification in a chaotic neural network

International Nuclear Information System (INIS)

Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

2012-01-01

In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

Change Project-Based Learning in Teacher Education in Botswana ...

African Journals Online (AJOL)

Environmental education (EE) and education for sustainable development (ESD) pedagogies are intricate, and to enhance learning, teacher education has to be innovative in teaching approach. This article investigates how the change project approach enhances project-based learning in practice. The investigation is ...

Smart Social Networking: 21st Century Teaching and Learning Skills

Directory of Open Access Journals (Sweden)

Helen B. Boholano

2017-06-01

Full Text Available Education in the 21st century highlights globalization and internationalization. Preservice teachers in the 21st century are technology savvy. To effectively engage and teach generation Z students, preservice teachers will help the educational system meet this requirement. The educational systems must be outfitted with a prerequisite of ICT resources both hardware and software, and curricula must be designed to promote a collaborative learner-centered environment to which students will relate and respond. This study determines the 21st century skills possessed by the pre-service teachers in terms of social networking. Pre-service teachers use computers in very advanced ways, but educators must remember that they still need guidance to use technology safely and effectively. Through social media the pre-service teachers can use a multitude of applications, including Web 2.0, for their projects. Smart social networking requires critical-thinking skills and the ability to integrate and evaluate real-world scenarios and authentic learning skills for validation.

Learning of N-layers neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2005-01-01

Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

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Gardner, Brian; Sporea, Ioana; Grüning, André

2015-12-01

Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

Learning the Structure of Bayesian Network from Small Amount of Data

Directory of Open Access Journals (Sweden)

Bogdan COCU

2009-12-01

Full Text Available Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.

The Practices of Student Network as Cooperative Learning in Ethiopia

Science.gov (United States)

Reda, Weldemariam Nigusse; Hagos, Girmay Tsegay

2015-01-01

Student network is a teaching strategy introduced as cooperative learning to all educational levels above the upper primary schools (grade 5 and above) in Ethiopia. The study was, therefore, aimed at investigating to what extent the student network in Ethiopia is actually practiced in line with the principles of cooperative learning. Consequently,…

"Getting Practical" and the National Network of Science Learning Centres

Science.gov (United States)

Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

2011-01-01

The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

Mimicking Nature´s way of organizing in industry: a network learning perspective

DEFF Research Database (Denmark)

Ulhøi, John Parm; Madsen, Henning

to reconsider organisational learning as being both an internal as well as an external phenomenon. By bringing network learning into an existing interorganisational setting (such as industrial ecology) new potentials for increased learning emerge for the participating companies. The concept of network learning...

Flash Study Analysis and the Music Learning Pro-Files Project

Science.gov (United States)

Cremata, Radio; Pignato, Joseph; Powell, Bryan; Smith, Gareth Dylan

2016-01-01

This paper introduces the Music Learning Profiles Project, and its methodological approach, flash study analysis. Flash study analysis is a method that draws heavily on extant qualitative approaches to education research, to develop broad understandings of music learning in diverse contexts. The Music Learning Profiles Project (MLPP) is an…

Network of anatomical texts (NAnaTex), an open-source project for visualizing the interaction between anatomical terms.

Science.gov (United States)

Momota, Ryusuke; Ohtsuka, Aiji

2018-01-01

Anatomy is the science and art of understanding the structure of the body and its components in relation to the functions of the whole-body system. Medicine is based on a deep understanding of anatomy, but quite a few introductory-level learners are overwhelmed by the sheer amount of anatomical terminology that must be understood, so they regard anatomy as a dull and dense subject. To help them learn anatomical terms in a more contextual way, we started a new open-source project, the Network of Anatomical Texts (NAnaTex), which visualizes relationships of body components by integrating text-based anatomical information using Cytoscape, a network visualization software platform. Here, we present a network of bones and muscles produced from literature descriptions. As this network is primarily text-based and does not require any programming knowledge, it is easy to implement new functions or provide extra information by making changes to the original text files. To facilitate collaborations, we deposited the source code files for the network into the GitHub repository ( https://github.com/ryusukemomota/nanatex ) so that anybody can participate in the evolution of the network and use it for their own non-profit purposes. This project should help not only introductory-level learners but also professional medical practitioners, who could use it as a quick reference.

Changing Network Support for Drinking: Network Support Project 2-Year Follow-up

Science.gov (United States)

Litt, Mark D.; Kadden, Ronald M.; Kabela-Cormier, Elise; Petry, Nancy M.

2009-01-01

The Network Support Project was designed to determine whether a treatment could lead patients to change their social network from one that supports drinking to one that supports sobriety. This study reports 2-year posttreatment outcomes. Alcohol-dependent men and women (N = 210) were randomly assigned to 1 of 3 outpatient treatment conditions:…

Project-Based Learning in Scottish Prisons

Science.gov (United States)

Sams, Kirsten

2014-01-01

The article describes the development of a project-based approach to learning in seven Scottish prisons. It argues that the project-based approach is ideally suited to prison education due to its flexibility and ability to enrich the relatively narrow prison curriculum and create meaningful links with wider society, reducing the isolation of…

Finite time convergent learning law for continuous neural networks.

Science.gov (United States)

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

ICT in Problem- and Project-based Learning

DEFF Research Database (Denmark)

Andreasen, Lars Birch; Lerche Nielsen, Jørgen

2012-01-01

The paper discusses how teaching and learning practices at universities can implement new information technologies, inspired by the traditions of problem- and project-based learning. The changing roles in the teacher-student relationship, and students’ development of information literacy are disc...

Biologically-inspired On-chip Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

Science.gov (United States)

Harding, Ansie; Engelbrecht, Johann

2015-01-01

"Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

Dialogue, Language and Identity: Critical Issues for Networked Management Learning

Science.gov (United States)

Ferreday, Debra; Hodgson, Vivien; Jones, Chris

2006-01-01

This paper draws on the work of Mikhail Bakhtin and Norman Fairclough to show how dialogue is central to the construction of identity in networked management learning. The paper is based on a case study of a networked management learning course in higher education and attempts to illustrate how participants negotiate issues of difference,…

Scaffolding Project-Based Learning with the Project Management Body of Knowledge (PMBOK[R])

Science.gov (United States)

van Rooij, Shahron Williams

2009-01-01

This paper reports the results of a study of the extent to which processes and procedures from the discipline of project management can scaffold online project-based learning in a graduate-level instructional technology course, by facilitating intra-team interaction, enhancing project outcomes and promoting a positive project team experience. With…

Project management support tool implementation. DynaLearn, EC FP7 STREP project 231526, Deliverable D1.1

NARCIS (Netherlands)

Bredeweg, B.; Liem, J.

2009-01-01

Different project management tools have been evaluated. We have chosen several well-known, flexible and mature tools to support management activities and communication between the DynaLearn project participants. We have created a DynaLearn website for stakeholders outside the DynaLearn website. An

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

Science.gov (United States)

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Project- Based Learning and Problem-Based Learning: Are They Effective to Improve Student's Thinking Skills?

OpenAIRE

Anazifa, R. D; Djukri, D

2017-01-01

The study aims at finding (1) the effect of project-based learning and problem-based learning on student's creativity and critical thinking and (2) the difference effect of project-based learning and problem-based learning on student's creativity and critical thinking. This study is quasi experiment using non-equivalent control-group design. Research population of this study was all classes in eleventh grade of mathematics and natural science program of SMA N 1 Temanggung. The participants we...

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

Science.gov (United States)

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

2018-03-15

Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

The algorithm for duration acceleration of repetitive projects considering the learning effect

Science.gov (United States)

Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan

2018-03-01

Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

Science.gov (United States)

Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

2016-01-01

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

Science.gov (United States)

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Network Enabled - Unresolved Residual Analysis and Learning (NEURAL)

Science.gov (United States)

Temple, D.; Poole, M.; Camp, M.

Since the advent of modern computational capacity, machine learning algorithms and techniques have served as a method through which to solve numerous challenging problems. However, for machine learning methods to be effective and robust, sufficient data sets must be available; specifically, in the space domain, these are generally difficult to acquire. Rapidly evolving commercial space-situational awareness companies boast the capability to collect hundreds of thousands nightly observations of resident space objects (RSOs) using a ground-based optical sensor network. This provides the ability to maintain custody of and characterize thousands of objects persistently. With this information available, novel deep learning techniques can be implemented. The technique discussed in this paper utilizes deep learning to make distinctions between nightly data collects with and without maneuvers. Implementation of these techniques will allow the data collected from optical ground-based networks to enable well informed and timely the space domain decision making.

Facilitative Components of Collaborative Learning: A Review of Nine Health Research Networks.

Science.gov (United States)

Leroy, Lisa; Rittner, Jessica Levin; Johnson, Karin E; Gerteis, Jessie; Miller, Therese

2017-02-01

Collaborative research networks are increasingly used as an effective mechanism for accelerating knowledge transfer into policy and practice. This paper explored the characteristics and collaborative learning approaches of nine health research networks. Semi-structured interviews with representatives from eight diverse US health services research networks conducted between November 2012 and January 2013 and program evaluation data from a ninth. The qualitative analysis assessed each network's purpose, duration, funding sources, governance structure, methods used to foster collaboration, and barriers and facilitators to collaborative learning. The authors reviewed detailed notes from the interviews to distill salient themes. Face-to-face meetings, intentional facilitation and communication, shared vision, trust among members and willingness to work together were key facilitators of collaborative learning. Competing priorities for members, limited funding and lack of long-term support and geographic dispersion were the main barriers to coordination and collaboration across research network members. The findings illustrate the importance of collaborative learning in research networks and the challenges to evaluating the success of research network functionality. Conducting readiness assessments and developing process and outcome evaluation metrics will advance the design and show the impact of collaborative research networks. Copyright © 2017 Longwoods Publishing.

Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

Science.gov (United States)

Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

2017-05-01

The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

Application of Technology in Project-Based Distance Learning

Directory of Open Access Journals (Sweden)

Ali Mehrabian

2008-06-01

Full Text Available Present technology and the accessibility of internet have made distance learning easier, more efficient, and more convenient for students. This technology allows instructors and students to communicate asynchronously, at times and locations of their own choosing, by exchanging printed or electronic information. The use of project-based approach is being recognized in the literature as a potential component of courses in the faculties of engineering, science, and technology. Instructors may have to restructure their course differently to accommodate and facilitate the effectiveness of distance learning. A project-based engineering course, traditionally taught in a classroom settings using live mode at the College of Engineering and Computer Sciences at the University of Central Florida (UCF has been transformed to a distance course taught using distance modes. In this case, pedagogical transitions and adjustments are required, in particular for obtaining an optimal balance between the course material and the project work. Project collaboration in groups requires communication, which is possible with extensive utilization of new information and communication technology, such as virtual meetings. This paper discusses the course transition from live to distance modes and touches on some issues as they relate to the effectiveness of this methodology and the lessons learned from its application within different context. More specifically, this discussion includes the benefit of implementing project-based work in the domain of the distance learning courses.

QFD Application to a Software - Intensive System Development Project

Science.gov (United States)

Tran, T. L.

1996-01-01

This paper describes the use of Quality Function Deployment (QFD), adapted to requirements engineering for a software-intensive system development project, and sysnthesizes the lessons learned from the application of QFD to the Network Control System (NCS) pre-project of the Deep Space Network.

The Fernald Closure Project: Lessons Learned

International Nuclear Information System (INIS)

Murphy, Cornelius M.; Carr, Dennis

2008-01-01

For nearly 37 years, the U.S. Department of Energy site at Fernald - near Cincinnati, Ohio - produced 230,000 metric tons (250,000 short tons) of high-purity, low-enriched uranium for the U.S. Defense Program, generating more than 5.4 million metric tons (6 million short tons) of liquid and solid waste as it carried out its Cold War mission. The facility was shut down in 1989 and clean up began in 1992, when Fluor won the contract to clean up the site. Cleaning up Fernald and returning it to the people of Ohio was a $4.4 billion mega environmental-remediation project that was completed in October 2006. Project evolved through four phases: - Conducting remedial-investigation studies to determine the extent of damage to the environment and groundwater at, and adjacent to, the production facilities; - Selecting cleanup criteria - final end states that had to be met that protect human health and the environment; - Selecting and implementing the remedial actions to meet the cleanup goals; - Executing the work in a safe, compliant and cost-effective manner. In the early stages of the project, there were strained relationships - in fact total distrust - between the local community and the DOE as a result of aquifer contamination and potential health effects to the workers and local residents. To engage citizens and interested stakeholders groups in the decision-making process, the DOE and Fluor developed a public-participation strategy to open the channels of communication with the various parties: site leadership, technical staff and regulators. This approach proved invaluable to the success of the project, which has become a model for future environmental remediation projects. This paper will summarize the history and shares lessons learned: the completion of the uranium-production mission to the implementation of the Records of Decision defining the cleanup standards and the remedies achieved. Lessons learned fall into ten categories: - Regulatory approach with end

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

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

2015-12-01

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

The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

Science.gov (United States)

Drexler, Wendy

This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

Inferring monopartite projections of bipartite networks: an entropy-based approach

Science.gov (United States)

Saracco, Fabio; Straka, Mika J.; Di Clemente, Riccardo; Gabrielli, Andrea; Caldarelli, Guido; Squartini, Tiziano

2017-05-01

Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain a (monopartite) projection on the layer of interest, which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any two nodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, here we consider a set of four null models, defined within the exponential random graph framework. Our algorithm outputs a matrix of link-specific p-values, from which a validated projection is straightforwardly obtainable, upon running a multiple hypothesis testing procedure. Finally, we test our method on an economic network (i.e. the countries-products World Trade Web representation) and a social network (i.e. MovieLens, collecting the users’ ratings of a list of movies). In both cases non-trivial communities are detected: while projecting the World Trade Web on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on the films layer allows clusters of movies whose affinity cannot be fully accounted for by genre similarity to be individuated.

Celebrating the Tenth Networked Learning Conference: Looking Back and Moving Forward

DEFF Research Database (Denmark)

de Laat, Maarten; Ryberg, Thomas

2018-01-01

conferences with the aim to describe some general trends and developments in networked learning research as they emerge and fade out over the years. In order to do so the authors use the proceedings of each networked learning conference (from 1998 till 2016) as a compiled dataset. This dataset forms a text...... corpus that has been analysed with Voyant tools (Sinclair and Rockwell 2016) specifically designed for analysing digital texts. Voyant tools are used to generate a set of word clouds (Cirrus) in order to visualise networked learning research-related terms that feature most frequently in each set...

Teacher and Student Intrinsic Motivation in Project-Based Learning

Science.gov (United States)

Lam, Shui-fong; Cheng, Rebecca Wing-yi; Ma, William Y. K.

2009-01-01

In this study we examined the relationship between teacher and student intrinsic motivation in project-based learning. The participants were 126 Hong Kong secondary school teachers and their 631 students who completed evaluation questionnaires after a semester-long project-based learning program. Both teachers and students were asked to indicate…

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

Science.gov (United States)

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks

Directory of Open Access Journals (Sweden)

Foroogh Ghasemi

2018-05-01

Full Text Available An organization’s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio’s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause–effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause–effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio’s projects and making strategic decisions.

The Effectiveness of Project-based E-learning to Improve Ict Literacy

OpenAIRE

Eliana, E. D. S; Senam, Senam; Wilujeng, I; Jumadi, Jumadi

2016-01-01

This study aims to reveal the effectiveness of science teaching based on project-based learning to improve ICT literacy learners in the junior high school with the category of high, medium and low. This research uses descriptive method to describe the students’ equipness of ICT literacy in the science learning based on the project-based learning that is integrated with e-learning. All of the population in this study are junior high school of curriculum pilot project in 2013 in Singkawang. The...

SUSTAIN: a network model of category learning.

Science.gov (United States)

Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

2004-04-01

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

Artificial neuron-glia networks learning approach based on cooperative coevolution.

Science.gov (United States)

Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

2015-06-01

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

A smart-pixel holographic competitive learning network

Science.gov (United States)

Slagle, Timothy Michael

Neural networks are adaptive classifiers which modify their decision boundaries based on feedback from externally- or internally-generated error signals. Optics is an attractive technology for neural network implementation because it offers the possibility of parallel, nearly instantaneous computation of the weighted neuron inputs by the propagation of light through the optical system. Using current optical device technology, system performance levels of 3 × 1011 connection updates per second can be achieved. This thesis presents an architecture for an optical competitive learning network which offers advantages over previous optical implementations, including smart-pixel-based optical neurons, phase- conjugate self-alignment of a single neuron plane, and high-density, parallel-access weight storage, interconnection, and learning in a volume hologram. The competitive learning algorithm with modifications for optical implementation is described, and algorithm simulations are performed for an example problem. The optical competitive learning architecture is then introduced. The optical system is simulated using the ``beamprop'' algorithm at the level of light propagating through the system components, and results showing competitive learning operation in agreement with the algorithm simulations are presented. The optical competitive learning requires a non-linear, non-local ``winner-take-all'' (WTA) neuron function. Custom-designed smart-pixel WTA neuron arrays were fabricated using CMOS VLSI/liquid crystal technology. Results of laboratory tests of the WTA arrays' switching characteristics, time response, and uniformity are then presented. The system uses a phase-conjugate mirror to write the self-aligning interconnection weight holograms, and energy gain is required from the reflection to minimize erasure of the existing weights. An experimental system for characterizing the PCM response is described. Useful gains of 20 were obtained with a polarization

Developing Communities of Practice around e-Learning and Project Management

Science.gov (United States)

Laxton, Ruth; Applebee, Andrelyn Cheryl

2010-01-01

In 2007-8 the Australian Catholic University (ACU National), undertook a project to develop new resources to provide training and support in eLearning for staff and students. The project was undertaken by a multidisciplinary team drawn from all six campuses and was led by an externally contracted Project Manager/eLearning specialist. This…

Project-based Collaborative learning in distance education

DEFF Research Database (Denmark)

Knudsen, Morten; Bajard, Christine; Helbo, Jan

2004-01-01

) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education....

Identifying and localizing network problems using the PuNDIT project

International Nuclear Information System (INIS)

Batista, Jorge; McKee, Shawn; Dovrolis, Constantine; Lee, Danny

2015-01-01

In today's world of distributed collaborations of scientists, there are many challenges to providing effective infrastructures to couple these groups of scientists with their shared computing and storage resources. The Pythia Network Diagnostic InfrasTructure (PuNDIT[1]) project is integrating and scaling research tools and creating robust code suitable for operational needs addressing the difficult challenge of automating the detection and location of network problems.PuNDIT is building upon the de-facto standard perfSONAR[2] network measurement infrastructure deployed in Open Science Grid(OSG)[3] and the Worldwide LHC Computing Grid(WLCG)[4]to gather and analyze complex real-world network topologies coupled with their corresponding network metrics to identify possible signatures of network problems from a set of symptoms. The PuNDIT Team is working closely with the perfSONAR developers from ESnet and Internet2 to integrate PuNDIT components as part of the perfSONAR Toolkit. A primary goal for PuNDIT is to convert complex network metrics into easily understood diagnoses in an automated way. We will report on the project progress to-date in working with the OSG and WLCG communities, describe the current implementation including some initial results and discuss future plans and the project timeline. (paper)

Learning in Networks for Sustainable Development

NARCIS (Netherlands)

Lansu, Angelique; Boon, Jo; Sloep, Peter; Van Dam-Mieras, Rietje

2010-01-01

The didactic model of remote internships described in this study provides the flexibility needed to support networked learners, i.e. to facilitate the development and subsequent assessment of their competences. The heterogeneity of the participants (students, employers, tutors) in the learning

Enhancing Integrative Motivation: The Japanese-American Collaborative Learning Project

Science.gov (United States)

Kato, Fumie

2016-01-01

The Collaborative Learning Project is a language exchange program in which American and Japanese university students have the opportunity to interact with native speakers over the course of a three-week period. This paper reports the outcomes of the Collaborative Learning Project in terms of its effectiveness in fulfilling student expectations and…

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Science.gov (United States)

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

SONG China project - participating in the global network

Science.gov (United States)

Deng, Licai; Xin, Yu; Zhang, Xiaobin; Li, Yan; Jiang, Xiaojun; Wang, Guomin; Wang, Kun; Zhou, Jilin; Yan, Zhengzhou; Luo, Zhiquan

2013-01-01

SONG (Stellar Observations Network Goup) is a low-cost ground based international collaboration aimed at two cutting edge problems in contemporary astrophysics in the time-domain: 1) Direct diagnostics of the internal structure of stars and 2) looking for and studying extra solar planets, possibly in the habitable zone. The general plan is to set up a network of 1m telescopes uniformly distributed in geographic latitude (in both hemispheres). China jointed the collaboration (initiated by Danish astronomers) at the very beginning. In addition to SONG's original plan (http://song.phys.au.dk), the Chinese team proposed a parallel photometry subnet work in the northern hemisphere, namely 50BiN (50cm Binocular Network, previously known as mini-SONG), to enable a large field photometric capability for the network, therefore maximising the potential of the network platform. The network will be able to produce nearly continuous time series observations of a number of selected objects with high resolution spectroscopy (SONG) and accurate photometry (50BiN), and to produce ultra-high accuracy photometry in dense field to look for micro-lensing events caused by planetary systems. This project has great synergy with Chinese Astronomical activities in Antarctica (Dome A), and other similar networks (e.g. LCOGT). The plan and current status of the project are overviewed in this poster.

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

Science.gov (United States)

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

Mobile Learning Projects - a critical analysis of the state of the art

OpenAIRE

Frohberg, D; Göth, C; Schwabe, G

2009-01-01

A brief narrative description of the journal article, document, or resource. This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control, communication, subject and objective. Although a significant number of projects hav...

Contextual Student Learning through Authentic Asteroid Research Projects using a Robotic Telescope Network

Science.gov (United States)

Hoette, Vivian L.; Puckett, Andrew W.; Linder, Tyler R.; Heatherly, Sue Ann; Rector, Travis A.; Haislip, Joshua B.; Meredith, Kate; Caughey, Austin L.; Brown, Johnny E.; McCarty, Cameron B.; Whitmore, Kevin T.

2015-11-01

Skynet is a worldwide robotic telescope network operated by the University of North Carolina at Chapel Hill with active observing sites on 3 continents. The queue-based observation request system is simple enough to be used by middle school students, but powerful enough to supply data for research scientists. The Skynet Junior Scholars program, funded by the NSF, has teamed up with professional astronomers to engage students from middle school to undergraduates in authentic research projects, from target selection through image analysis and publication of results. Asteroid research is a particularly fruitful area for youth collaboration that reinforces STEM education standards and can allow students to make real contributions to scientific knowledge, e.g., orbit refinement through astrometric submissions to the Minor Planet Center. We have created a set of projects for youth to: 1. Image an asteroid, make a movie, and post it to a gallery; 2. Measure the asteroid’s apparent motion using the Afterglow online image processor; and 3. Image asteroids from two or more telescopes simultaneously to demonstrate parallax. The apparent motion and parallax projects allow students to estimate the distance to their asteroid, as if they were the discoverer of a brand new object in the solar system. Older students may take on advanced projects, such as analyzing uncertainties in asteroid orbital parameters; studying impact probabilities of known objects; observing time-sensitive targets such as Near Earth Asteroids; and even discovering brand new objects in the solar system.Images are acquired from among seven Skynet telescopes in North Carolina, California, Wisconsin, Canada, Australia, and Chile, as well as collaborating observatories such as WestRock in Columbus, Georgia; Stone Edge in El Verano, California; and Astronomical Research Institute in Westfield, Illinois.

Facilitating Problem Framing in Project-Based Learning

Science.gov (United States)

Svihla, Vanessa; Reeve, Richard

2016-01-01

While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated…

Factors that influence cooperation in networks for innovation and learning

NARCIS (Netherlands)

Sie, Rory; Bitter-Rijpkema, Marlies; Stoyanov, Slavi; Sloep, Peter

2018-01-01

Networked cooperation fails if the available partnerships remain opaque. A literature review and Delphi study uncovered the elements of a fruitful partnership. They relate to personality, diversity, cooperation, and management. Innovation networks and learning networks share the same cooperative

Continued multidisciplinary project-based learning - implementation in health informatics.

Science.gov (United States)

Wessel, C; Spreckelsen, C

2009-01-01

Problem- and project-based learning are approved methods to train students, graduates and post-graduates in scientific and other professional skills. The students are trained on realistic scenarios in a broader context. For students specializing in health informatics we introduced continued multidisciplinary project-based learning (CM-PBL) at a department of medical informatics. The training approach addresses both students of medicine and students of computer science. The students are full members of an ongoing research project and develop a project-related application or module, or explore or evaluate a sub-project. Two teachers guide and review the students' work. The training on scientific work follows a workflow with defined milestones. The team acts as peer group. By participating in the research team's work the students are trained on professional skills. A research project on a web-based information system on hospitals built the scenario for the realistic context. The research team consisted of up to 14 active members at a time, who were scientists and students of computer science and medicine. The well communicated educational approach and team policy fostered the participation of the students. Formative assessment and evaluation showed a considerable improvement of the students' skills and a high participant satisfaction. Alternative education approaches such as project-based learning empower students to acquire scientific knowledge and professional skills, especially the ability of life-long learning, multidisciplinary team work and social responsibility.

Nuclear power plant monitoring using real-time learning neural network

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

1994-01-01

In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

Dearfield Dream Project: Developing an Interdisciplinary Historical/Cultural Research Network

Directory of Open Access Journals (Sweden)

Robert Brunswig

2013-08-01

Full Text Available The Dearfield Dream Project is a collaborative research initiative to conduct historical, cultural, archaeological, and environmental studies on the early 20th Century African-American colony site of Dearfield, Colorado, USA. Because the breadth and significance of the Dearfield Project requires an interdisciplinary research team, a network of research collaborators has been assembled. This research network seeks to discover, preserve, and disseminate knowledge of the site and its surrounding farmsteads’ economic, social, political, and environmental history for better understanding and interpretation of its contributions to Colorado and U.S. history. Herein, we detail progress that has been made on this important historical/cultural research project. Further, we outline the future of the Dearfield research network along with our current and anticipated subjects of inquiry.

Learning Word Subsumption Projections for the Russian Language

Directory of Open Access Journals (Sweden)

Ustalov Dmitry

2016-01-01

Full Text Available The semantic relations of hypernymy and hyponymy are widely used in various natural language processing tasks for modelling the subsumptions in common sense reasoning. Since the popularisation of the distributional semantics, a significant attention is paid to applying word embeddings for inducing the relations between words. In this paper, we show our preliminary results on adopting the projection learning technique for computing hypernyms from hyponyms using word embeddings. We also conduct a series of experiments on the Russian language and release the open source software for learning hyponym-hypernym projections using both CPUs and GPUs, implemented with the TensorFlow machine learning framework.

Implementation of a Framework for Collaborative Social Networks in E-Learning

Science.gov (United States)

Maglajlic, Seid

2016-01-01

This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

Towards the creation of a European Network of Earth Observation Networks within GEO. The ConnectinGEO project.

Science.gov (United States)

Masó, Joan; Serral, Ivette; Menard, Lionel; Wald, Lucien; Nativi, Stefano; Plag, Hans-Peter; Jules-Plag, Shelley; Nüst, Daniel; Jirka, Simon; Pearlman, Jay; De Maziere, Martine

2015-04-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations" is a new H2020 Coordination and Support Action with the primary goal of linking existing Earth Observation networks with science and technology (S&T) communities, the industry sector, the Group on Earth Observations (GEO), and Copernicus. ConnectinGEO aims to facilitate a broader and more accessible knowledge base to support the needs of GEO, its Societal Benefit Areas (SBAs) and the users of the Global Earth Observing System of Systems (GEOSS). A broad range of subjects from climate, natural resources and raw materials, to the emerging UN Sustainable Development Goals (SDGs) will be addressed. The project will generate a prioritized list of critical gaps within available observation data and models to translate observations into practice-relevant knowledge, based on stakeholder consultation and systematic analysis. Ultimately, it will increase coherency of European observation networks, increase the use of Earth observations for assessments and forecasts and inform the planning for future observation systems. ConnectinGEO will initiate a European Network of Earth Observation Networks (ENEON) that will encompass space-based, airborne and in-situ observations networks. ENEON will be composed by project partners representing thematic observation networks along with the GEOSS Science and Technology Stakeholder Network, GEO Communities of Practices, Copernicus services, Sentinel missions and in-situ support data representatives, representatives of the space-based, airborne and in-situ observations European networks (e.g. EPOS, EMSO and GROOM, etc), representatives of the industry sector and European and national funding agencies, in particular those participating in the future ERA-PlaNET. At the beginning, the ENEON will be created and managed by the project. Then the management will be transferred to the network itself to ensure

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

Science.gov (United States)

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

Science.gov (United States)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.

Social Networks and Performance in Distributed Learning Communities

Science.gov (United States)

Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

DEFF Research Database (Denmark)

Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

2017-01-01

it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...... associations quickly and over long periods of time. The results in this paper demonstrate that the ENTM is able to perform one-shot learning in reinforcement learning tasks without catastrophic forgetting of previously stored associations. Additionally, we introduce a new ENTM default jump mechanism that makes...

Teaching and learning in the international classroom: quality principles and lessons learned from the IntlUni project

DEFF Research Database (Denmark)

Lauridsen, Karen M.; Cozart, Stacey Marie

2015-01-01

, and expectations about the teaching and learning processes and outcomes. Certainly, many teachers in these settings are meeting the challenges of this diversity, and some are leveraging it to improve student learning and intercultural competence. Nevertheless, the work of IntlUni, an Erasmus Academic Network (2012......As higher education in Europe becomes increasingly internationalized, many higher education institutions are facing new diversity issues as well as opportunities arising from educational settings where students and teachers often have different first languages, cultural backgrounds...... of principles for quality teaching and learning in the international classroom, developed by the network, as well as a number of the important lessons learned...

Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

Science.gov (United States)

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel; Alberdi, Mikel

2016-01-01

The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

Project-Based Collaborative Learning in Distance Education

DEFF Research Database (Denmark)

Knudsen, Morten; Bajard, C.; Helbo, Jan

2003-01-01

This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII)......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education.......) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII...

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

Science.gov (United States)

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Problem based Learning versus Design Thinking in Team based Project work

DEFF Research Database (Denmark)

Denise J. Stokholm, Marianne

2014-01-01

project based learning issues, which has caused a need to describe and compare the two models; in specific the understandings, approaches and organization of learning in project work. The PBL model viewing the process as 3 separate project stages including; problem analysis, problem solving and project......All educations at Aalborg University has since 1974 been rooted in Problem Based Learning (PBL). In 1999 a new education in Industrial design was set up, introducing Design Based Learning (DBL). Even though the two approaches have a lot in common they also hold different understandings of core...... report, with focus on problem solving through analysis. Design Based Learning viewing the process as series of integrated design spaces including; alignment, research, mission, vision, concept, product and process report, with focus on innovative ideation though integration. There is a need of renewing...

Campus-Wide Networks: Three State-of-the-Art Demonstration Projects.

Science.gov (United States)

Chapman, Dale T.

1986-01-01

During the 1980's, the educational community has been keeping its eye hopefully on several campus-wide networking projects. Included are reports on progress in networks and networking at Carnegie Mellon University, the Massachussetts Institute of Technology (MIT), and at Brown University. (JN)

Developing 21st century skills through the use of student personal learning networks

Science.gov (United States)

Miller, Robert D.

This research was conducted to study the development of 21st century communication, collaboration, and digital literacy skills of students at the high school level through the use of online social network tools. The importance of this study was based on evidence high school and college students are not graduating with the requisite skills of communication, collaboration, and digital literacy skills yet employers see these skills important to the success of their employees. The challenge addressed through this study was how high schools can integrate social network tools into traditional learning environments to foster the development of these 21st century skills. A qualitative research study was completed through the use of case study. One high school class in a suburban high performing town in Connecticut was selected as the research site and the sample population of eleven student participants engaged in two sets of interviews and learned through the use social network tools for one semester of the school year. The primary social network tools used were Facebook, Diigo, Google Sites, Google Docs, and Twitter. The data collected and analyzed partially supported the transfer of the theory of connectivism at the high school level. The students actively engaged in collaborative learning and research. Key results indicated a heightened engagement in learning, the development of collaborative learning and research skills, and a greater understanding of how to use social network tools for effective public communication. The use of social network tools with high school students was a positive experience that led to an increased awareness of the students as to the benefits social network tools have as a learning tool. The data supported the continued use of social network tools to develop 21st century communication, collaboration, and digital literacy skills. Future research in this area may explore emerging social network tools as well as the long term impact these tools

Networking for Learning The role of Networking in a Lifelong Learner's Professional Development

OpenAIRE

Rajagopal, Kamakshi

2016-01-01

This dissertation discusses the role the social activity of networking plays in lifelong learners’ professional and personal continuous development. The main hypothesis of this thesis is that networking is a learning strategy for lifelong learners, in which conversations are key activities through which they reassess their held thoughts and make sense of their experiences together with others.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

Science.gov (United States)

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Internal evaluation of the European network for health technology assessment project.

Science.gov (United States)

Håheim, Lise Lund; Imaz, Iñaki; Loud, Marlène Läubli; Gasparetto, Teresa; González-Enriquez, Jesús; Dahlgren, Helena; Trofimovs, Igor; Berti, Elena; Mørland, Berit

2009-12-01

The internal evaluation studied the development of the European network for Health Technology Assessment (EUnetHTA) Project in achieving the general objective of establishing an effective and a sustainable network of health technology assessment (HTA) in Europe. The Work Package 3 group was dedicated to this task and performed the work. Information on activities during the project was collected from three sources. First, three yearly cross-sectional studies surveyed the participants' opinions. Responses were by individuals or by institutions. The last round included surveys to the Steering Committee, the Stakeholder Forum, and the Secretariat. Second, the Work Package Lead Partners were interviewed bi-annually, five times in total, to update the information on the Project's progress. Third, additional information was sought in available documents. The organizational structure remained stable. The Project succeeded in developing tools aimed at providing common methodology with intent to establish a standard of conducting and reporting HTA and to facilitate greater collaboration among agencies. The participants/agencies expressed their belief in a network and in maintaining local/national autonomy. The Work Package Leaders expressed a strong belief in the solid base of the Project for a future network on which to build, but were aware of the need for funding and governmental support. Participants and Work Package Leaders have expressed support for a future network that will improve national and international collaboration in HTA based on the experience from the EUnetHTA project.

ANALISIS PENGUASAAN KONSEP DAN KETERAMPILAN BERPIKIR KREATIF SISWA SD MELALUI PROJECT BASED LEARNING

Directory of Open Access Journals (Sweden)

Wa Ode Lidya Arisanti

2017-02-01

Full Text Available Abstract: Learning science is not just memorize the concepts, but learn how to process and mastery of the scientific attitude. Actually, learning is still centered on the teacher, so that students can develop the knowledge and skills of thinking. Knowledge and thinking skills students can experience a change in the proper way, one way that can be done by applying the model of project-based learning. This study aims to determine whether there is a difference mastery of concepts and creative thinking skills between classes that implement learning model project based learning and classroom rather than project-based learning on water recycling materials. The research design used in this study was Quasi Experimental Design devoted no pattern Nonequivalent Control Group Design This design consists of two groups: the experimental group and the control group. Furthermore, each class were given a pretest and posttest study with mastery test questions using the concept of multiple choice questions of 15 questions and test creative thinking skills with five essay questions on each test. Classroom learning experiment treated with a model project based learning and classroom learning with no control got projet models based learning. The results showed in general there are significant differences in mastery of concepts (p = 0.00 between the experimental class learning by applying the model of project-based learning in the learning process (the average N-gain = 0.477 in the medium category, with students learning by applying rather than project-based learning (the average N-gain = 0.290 in the low category. There is no difference in the ability to think creatively (p = 0.22 between the experimental class and control class, with an average N-gain the experimental class 0,075 while the control class is 0.060 which both are in the low category. Keywords: project based learning, mastery of concepts, creative   Abstrak: Belajar IPA bukan hanya menghafal konsep

Project-Based Learning and Design-Focused Projects to Motivate Secondary Mathematics Students

Science.gov (United States)

Remijan, Kelly W.

2017-01-01

This article illustrates how mathematics teachers can develop design-focused projects, related to project-based learning, to motivate secondary mathematics students. With first-hand experience as a secondary mathematics teacher, I provide a series of steps related to the engineering design process, which are helpful to teachers in developing…

Pengembangan Model Outdoor Learning melalui Project Berbasis Local Wisdom dalam Pembelajaran Fisika

Directory of Open Access Journals (Sweden)

Indah kurnia Putri Damayanti

2017-12-01

Full Text Available Abstrak Penelitian ini bertujuan untuk: (1 menghasilkan model outdoor learning melalui project berbasis local wisdom yang layak digunakan dalam pembelajaran fisika, (2 mengetahui keefektifan penggunaan model outdoor learning melalui project berbasis local wisdom. Penelitian pengembangan ini menggunakan metode pengembangan R & D (Research dan Development. Pada tahap Development, peneliti mengadopsi model 4D, yaitu Define, Design, Develop, dan Disseminate. Hasil penelitian menunjukkan bahwa model outdoor learning melalui project berbasis local wisdom yang dikembangkan layak digunakan dari segi produk pendukung pembelajaran yang memenuhi kriteria sangat tinggi menurut para ahli, praktis menurut guru dan peserta didik. Lembar observasi yang memenuhi kriteria valid dan reliabel berdasarkan hasil ICC dan tes hasil belajar yang memenuhi kriteria valid dan reliabel berdasarkan hasil Quest. Selain itu, model outdoor learning melalui project berbasis local wisdom lebih efektif digunakan dalam pembelajaran fisika dilihat dari hasil analisis multivariate dan GLMMDs yang memperoleh nilai signifikansi 0,000 dan MD yang tinggi.   AbstractThis research was aimed to: (1 produce outdoor learning via project based suitable local wisdom model used in physics learning, (2 know the effectiveness in using outdoor learning via project based local wisdom model. This developing research used a R & D method (Research and Development. On Development step, the researcher adopted 4D model, they were Define, Design, Develop, dan Dissemination. The results showed that the developed outdoor learning via project based local wisdom model was suitable to be used in terms of learning support product that was in very high category according expert, practical according teacher and students. In addition the observation sheet was in valid criteria and reliabel based on ICC and the learning outcome test was in valid criteria and reliabel based on Quest. Besides, outdoor learning via

Enhancing Formal E-Learning with Edutainment on Social Networks

Science.gov (United States)

Labus, A.; Despotovic-Zrakic, M.; Radenkovic, B.; Bogdanovic, Z.; Radenkovic, M.

2015-01-01

This paper reports on the investigation of the possibilities of enhancing the formal e-learning process by harnessing the potential of informal game-based learning on social networks. The goal of the research is to improve the outcomes of the formal learning process through the design and implementation of an educational game on a social network…

Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

OpenAIRE

Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

2017-01-01

Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

Science.gov (United States)

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Using Social Networks to Enhance Teaching and Learning Experiences in Higher Learning Institutions

Science.gov (United States)

Balakrishnan, Vimala

2014-01-01

The paper first explores the factors that affect the use of social networks to enhance teaching and learning experiences among students and lecturers, using structured questionnaires prepared based on the Push-Pull-Mooring framework. A total of 455 students and lecturers from higher learning institutions in Malaysia participated in this study.…

Lights, Camera, Project-Based Learning!

Science.gov (United States)

Cox, Dannon G.; Meaney, Karen S.

2018-01-01

A physical education instructor incorporates a teaching method known as project-based learning (PBL) in his physical education curriculum. Utilizing video-production equipment to imitate the production of a televisions show, sixth-grade students attending a charter school invited college students to share their stories about physical activity and…

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

Science.gov (United States)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to

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

Directory of Open Access Journals (Sweden)

Ratna Malawati

2016-06-01

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

Learning Objects, Repositories, Sharing and Reusability

Science.gov (United States)

Koppi, Tony; Bogle, Lisa; Bogle, Mike

2005-01-01

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

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

Science.gov (United States)

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

Electronic Learning in the German Science Project "NAWI-Interaktiv"

Science.gov (United States)

Wegner, Claas; Homann, Wiebke; Strehlke, Friederike

2014-01-01

The German science project "NAWI-Interaktiv" is an example of innovative use of E-Learning and new media education. Since 2009, the learning platform provides learners and teachers with high-quality learning tools, teaching material, useful information and E-learning programs for free. This is to raise the pupils' motivation to learn…

Integrating Project-Based Service-Learning into an Advanced Environmental Chemistry Course

Science.gov (United States)

Draper, Alison J.

2004-02-01

In an advanced environmental chemistry course, the inclusion of semester-long scientific service projects successfully integrated the research process with course content. Each project involved a unique community-based environmental analysis in which students assessed an aspect of environmental health. The projects were due in small pieces at even intervals, and students worked independently or in pairs. Initially, students wrote a project proposal in which they chose and justified a project. Following a literature review of their topic, they drafted sampling and analysis plans using methods in the literature. Samples were collected and analyzed, and all students assembled scientific posters describing the results of their study. In the last week of the semester, the class traveled to a regional professional meeting to present the posters. In all, students found the experience valuable. They learned to be professional environmental chemists and learned the value of the discipline to community health. Students not only learned about their own project in depth, but they were inspired to learn textbook material, not for an exam, but because it helped them understand their own project. Finally, having a community to answer to at the end of the project motivated students to do careful work.

Networked learning in, for, and with the world

DEFF Research Database (Denmark)

Nørgård, Rikke Toft; Mor, Yishay; Bengtsen, Søren Smedegaard

2018-01-01

With the so-called ‘Mode 3’ university as overarching framework (Barnett, 2004; Bengtsen & Nørgård, 2016; Barnett & Bengtsen, 2017; Nørgård, Olesen & Toft-Nielsen, 2018) this chapter considers how traditional forms of and formats for teaching and learning within higher education can be rethought,......’ in higher education. In the following sections, we will describe these transformations of university being, before considering some of the new challenges, opportunities, and potentials of teaching and learning in and through hybrid networks in the Mode 3 institution......., opportunities, and potentials to the teaching and learning that takes place at the university. Through history, and across different present national contexts and cultures, the ‘being’ of the university and its livelihood and mandate has changed (Wright, 2016; Barnett, 2018). Through these transformations where......, reconfigured, and redesigned in order to facilitate valuable, meaningful and relevant hybrid networked learning in, for, and with the world. What it means to ‘be’ a university is changing and the university is a ‘being’ that in itself is changing (Barnett, 2011), something also offering challenges...

A Study of the Communication Behaviors and Members' Roles in the Interaction Process of a Project-based Learning Group

Directory of Open Access Journals (Sweden)

Wei-Jane Lin

2010-06-01

Full Text Available The infusion of information and communication technology into instruction has gained the foothold within many classrooms in higher education by its advantages to enable the variety and accessibility of school teaching and learning. However, to engage students with the technology enhanced learning experiences calls for attentions on more the processes than the mere outcome of technology use. This study examines the common phenomenon in college campus where network technology, group activities and project works are available with the intention to explore how student performance of teamwork and learning is affected by the micro factors of group compositions, members’ roles and their communication behaviors. Results show that the group performed most procedure-, task-, and social-communication behaviors during the execution stage than that of preparation and completion stages. Additionally, members’ roles performed and interfered within these stages positively affected the project performance to different extent. [Article content in Chinese; Extended abstract in English

Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning

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Guangyi Liu

2014-01-01

Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.

Functional networks inference from rule-based machine learning models.

Science.gov (United States)

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

Dimensions of Problem Based Learning--Dialogue and Online Collaboration in Projects

Science.gov (United States)

Andreasen,, Lars Birch; Nielsen, Jørgen Lerche

2013-01-01

The article contributes to the discussions on problem based learning and project work, building on and reflecting the experiences of the authors. Four perspectives are emphasized as central to a contemporary approach to problem- and project-based learning: the exploration of problems, projects as a method, online collaboration, and the dialogic…

RELATION BETWEEN COOPERATION AND ORGANIZATIONAL LEARNING WITH THE COMPETITIVENESS IN AN INTERORGANIZATIONAL NETWORK

Directory of Open Access Journals (Sweden)

Paulo Cesar Zonta

2015-05-01

Full Text Available The study analyzed the relationship between cooperation and organizational learning with competitiveness in a small and medium enterprises (SME network, with business of the groups of the Commercial and Industrial Association of Chapecó (ACIC. The methodology used was quantitative, with the factorial analysis. Currently, ACIC has 14 groups and 236 SME´s nucleated, developing joint activities of economic and social sustainability in Chapecó. The theoretical study raised concepts already endorsed by the scientific community on interorganizational networks, competitiveness, cooperation and organizational learning. The results demonstrated that indicators related to cooperation and learning in horizontal networks are characterized as antecedents of competitiveness in organizational networks, and that there is a positive correlation between the constructs cooperation and organizational learning with competitiveness construct. The study confirms the belief that small businesses associated in networks can increase their competitiveness, thus contributing to regional development.

Project Based Learning in Multi-Grade Class

Science.gov (United States)

Ciftci, Sabahattin; Baykan, Ayse Aysun

2013-01-01

The purpose of this study is to evaluate project based learning in multi-grade classes. This study, based on a student-centered learning approach, aims to analyze students' and parents' interpretations. The study was done in a primary village school belonging to the Centre of Batman, already adapting multi-grade classes in their education system,…

Language Views on Social Networking Sites for Language Learning: The Case of Busuu

Science.gov (United States)

Álvarez Valencia, José Aldemar

2016-01-01

Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

Social Networks: Rational Learning and Information Aggregation

Science.gov (United States)

2009-09-01

predecessor, Gale and Kariv (2003) who generalize the payoff equalization result of Bala and Goyal (1998) in connected social networks (discussed below...requires more notation. Using Bayes’ Rule and the assumption of equal priors on the state θ, we have that the social belief given by observing... Social Networks: Rational Learning and Information Aggregation by Ilan Lobel B.Sc., Pontif́ıcia Universidade Católica do Rio de Janeiro (2004

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

2015-01-01

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

Stochastic Online Learning in Dynamic Networks under Unknown Models

Science.gov (United States)

2016-08-02

The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

Sustainable assessment of learning experiences based on projects

Directory of Open Access Journals (Sweden)

Ignacio TRAVERSO RIBÓN

2016-05-01

Full Text Available In a project-based learning experience, the detailed monitoring of the activities in which team members participate can be useful to evaluate their work. Using learning-oriented assessment procedures, supervisors can assess the teamwork abilities with a formative purpose. Evaluation strategies such as self-assessment, peer assessment and co-assessment are often used to make evaluation formative and sustainable. Conducting an assessment strategy is not easy for team members, since they need before to have a reasonable understanding of the evaluation process and criteria. This paper describes a learning-oriented evaluation methodology and an open data framework that can be applied to collaborative project settings. An evaluation rubric and a series of indicators that provide evidences about the developed skills have been elaborated and applied in a small-scale project-based course. Projects were managed and developed with the help of an open source software forge that contains a ticketing tool for planning and tracking of tasks, a version control repository to save the software outcomes, and using a wiki to host text deliverables. The experience provides evidences in favor of using the assessment method and open data framework to make teamwork evaluation more sustainable.

Promoting system-level learning from project-level lessons

Energy Technology Data Exchange (ETDEWEB)

Jong, Amos A. de, E-mail: amosdejong@gmail.com [Innovation Management, Utrecht (Netherlands); Runhaar, Hens A.C., E-mail: h.a.c.runhaar@uu.nl [Section of Environmental Governance, Utrecht University, Utrecht (Netherlands); Runhaar, Piety R., E-mail: piety.runhaar@wur.nl [Organisational Psychology and Human Resource Development, University of Twente, Enschede (Netherlands); Kolhoff, Arend J., E-mail: Akolhoff@eia.nl [The Netherlands Commission for Environmental Assessment, Utrecht (Netherlands); Driessen, Peter P.J., E-mail: p.driessen@geo.uu.nl [Department of Innovation and Environment Sciences, Utrecht University, Utrecht (Netherlands)

2012-02-15

A growing number of low and middle income nations (LMCs) have adopted some sort of system for environmental impact assessment (EIA). However, generally many of these EIA systems are characterised by a low performance in terms of timely information dissemination, monitoring and enforcement after licencing. Donor actors (such as the World Bank) have attempted to contribute to a higher performance of EIA systems in LMCs by intervening at two levels: the project level (e.g. by providing scoping advice or EIS quality review) and the system level (e.g. by advising on EIA legislation or by capacity building). The aims of these interventions are environmental protection in concrete cases and enforcing the institutionalisation of environmental protection, respectively. Learning by actors involved is an important condition for realising these aims. A relatively underexplored form of learning concerns learning at EIA system-level via project level donor interventions. This 'indirect' learning potentially results in system changes that better fit the specific context(s) and hence contribute to higher performances. Our exploratory research in Ghana and the Maldives shows that thus far, 'indirect' learning only occurs incidentally and that donors play a modest role in promoting it. Barriers to indirect learning are related to the institutional context rather than to individual characteristics. Moreover, 'indirect' learning seems to flourish best in large projects where donors achieved a position of influence that they can use to evoke reflection upon system malfunctions. In order to enhance learning at all levels donors should thereby present the outcomes of the intervention elaborately (i.e. discuss the outcomes with a large audience), include practical suggestions about post-EIS activities such as monitoring procedures and enforcement options and stimulate the use of their advisory reports to generate organisational memory and ensure a better

Promoting system-level learning from project-level lessons

International Nuclear Information System (INIS)

Jong, Amos A. de; Runhaar, Hens A.C.; Runhaar, Piety R.; Kolhoff, Arend J.; Driessen, Peter P.J.

2012-01-01

A growing number of low and middle income nations (LMCs) have adopted some sort of system for environmental impact assessment (EIA). However, generally many of these EIA systems are characterised by a low performance in terms of timely information dissemination, monitoring and enforcement after licencing. Donor actors (such as the World Bank) have attempted to contribute to a higher performance of EIA systems in LMCs by intervening at two levels: the project level (e.g. by providing scoping advice or EIS quality review) and the system level (e.g. by advising on EIA legislation or by capacity building). The aims of these interventions are environmental protection in concrete cases and enforcing the institutionalisation of environmental protection, respectively. Learning by actors involved is an important condition for realising these aims. A relatively underexplored form of learning concerns learning at EIA system-level via project level donor interventions. This ‘indirect’ learning potentially results in system changes that better fit the specific context(s) and hence contribute to higher performances. Our exploratory research in Ghana and the Maldives shows that thus far, ‘indirect’ learning only occurs incidentally and that donors play a modest role in promoting it. Barriers to indirect learning are related to the institutional context rather than to individual characteristics. Moreover, ‘indirect’ learning seems to flourish best in large projects where donors achieved a position of influence that they can use to evoke reflection upon system malfunctions. In order to enhance learning at all levels donors should thereby present the outcomes of the intervention elaborately (i.e. discuss the outcomes with a large audience), include practical suggestions about post-EIS activities such as monitoring procedures and enforcement options and stimulate the use of their advisory reports to generate organisational memory and ensure a better information

Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

Science.gov (United States)

Noor, Ahmed K. (Compiler)

2003-01-01

An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

Language Learning through Social Networks: Perceptions and Reality

Science.gov (United States)

Lin, Chin-Hsi; Warschauer, Mark; Blake, Robert

2016-01-01

Language Learning Social Network Sites (LLSNSs) have attracted millions of users around the world. However, little is known about how people participate in these sites and what they learn from them. This study investigated learners' attitudes, usage, and progress in a major LLSNS through a survey of 4,174 as well as 20 individual case studies. The…

Understanding the Context of Learning in an Online Social Network for Health Professionals' Informal Learning.

Science.gov (United States)

Li, Xin; Gray, Kathleen; Verspoor, Karin; Barnett, Stephen

2017-01-01

Online social networks (OSN) enable health professionals to learn informally, for example by sharing medical knowledge, or discussing practice management challenges and clinical issues. Understanding the learning context in OSN is necessary to get a complete picture of the learning process, in order to better support this type of learning. This study proposes critical contextual factors for understanding the learning context in OSN for health professionals, and demonstrates how these contextual factors can be used to analyse the learning context in a designated online learning environment for health professionals.

Information Resources Usage in Project Management Digital Learning System

Science.gov (United States)

Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

2017-01-01

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…

How to Enhance Interdisciplinary Competence--Interdisciplinary Problem-Based Learning versus Interdisciplinary Project-Based Learning

Science.gov (United States)

Brassler, Mirjam; Dettmers, Jan

2017-01-01

Interdisciplinary competence is important in academia for both employability and sustainable development. However, to date, there are no specific interdisciplinary education models and, naturally, no empirical studies to assess them. Since problem-based learning (PBL) and project-based learning (PjBL) are learning approaches that emphasize…

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Directory of Open Access Journals (Sweden)

Zou Xiaotao

2009-01-01

Full Text Available Abstract A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

Continuous Learning of a Multilayered Network Topology in a Video Camera Network

Directory of Open Access Journals (Sweden)

Xiaotao Zou

2009-01-01

Full Text Available A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level is analyzed both in simulation and in real-life experiments and compared with previous approaches.

Learning based in projects to promote interdisciplinarity in Secondary School

Directory of Open Access Journals (Sweden)

Daniela Boff

2016-02-01

Full Text Available The project-based learning is an active learning strategy that helps break the paradigm of traditional teaching methods. The student is involved in the learning proposal that includes the PiBL, on which one is not passive and becomes the main actor in one's own teaching learning process. Within this learning strategy, the teacher becomes a mediator between theory and practice thus each different subject interact with one another in order to develop a topic that is mutual to all areas because the learning environment is naturally interdisciplinary. The idea of this kind of learning strategy was applied during a workshop that took place with primary and secondary schoolteachers in order to help them approach the strategy in the classroom, contributing with experiences and ideas towards the interdisciplinary based project.

Analog memristive synapse in spiking networks implementing unsupervised learning

Directory of Open Access Journals (Sweden)

Erika Covi

2016-10-01

Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

Science.gov (United States)

Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

2016-01-01

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.