On Being Too Nice: Message Interaction in an Asynchronous Learning Network

The interact system model (ISM) is used to examine the interactions between messages submitted during online discussions related to a graduate education course in curriculum theory. Interactions are analyzed using complexity science and conclusions are drawn concerning structures that could enhance discussion and support the construction of collective understandings.

Learn-Nett : Learning network for teachers. 'Learn-Nett : Red de aprendizaje para profesores'.

Learn-nett es uno de los proyectos enmarcados dentro del programa Socrates de la Comunidad Económica Europea, un programa que subvenciona y apoya proyectos de investigación y desarrollo de nuestra comunidad tanto en el campo de la educación como en cualquier area de conocimiento. Learn-nett, es un proyecto de participación conjunta entre diferentes universidades europeas y que tiene como objetivo la formación, la experimentación y la reflexión sobre la utilización de las nuevas herramientas de la información y la comunicación en la educación.

Supporting an open learning network

Stephen Downes, investigador del Canada's National Research Council, presenta su visión personal sobre la educación y los recursos libres. Los temas principales de su presentación son: Free and Open Source Software, Open Knowledge, Education and Technology

Developing and asynchronous learning network

Deakin University, Australia, has committed resources over a number of years to developing the use of information and communication technologies in all aspects of teaching and learning. This paper focuses on the development over a four year period of an Asynchronous Learning Network (ALN) for distance education students studying undergraduate introductory macroeconomics. The research is based on quantitative and qualitative data gained from student evaluations, academic staff interviews, participation levels and an analysis of the online communication. Key findings from the research relate to the quality of the learning environment, the level of communication, and the role of academic staff in the learning experience. Strategies discussed for the successful use of an ALN include the nurturing of a collaborative learning environment, the adaptation of curriculum and pedagogy, the role of assessment, and the role of academic staff training and development.

Knowledge mobilization on decision making for school improvement : Canadian principals learning network.

Principals duties have expanded beyond instructional leadership. Roles now include being curriculum leader, supervisor, manager, head of finance, administration, compliance, and legal matters, and so on. These additional responsibilities impact their decision-making in relation to teaching, learning and school improvement in general. How, and on what basis, they make these decisions is crucial both to their development as instructional leaders and to educational reform processes. To contribute to knowledge on principals’ decision making skills, we have created a strategic knowledge mobilization initiative called 'Canadian Principals Learning Network (CPLN)'. Through a variety of face-to-face and online activities, it collaboratively links together an international group of practicing principals and university-based researchers with related expertise. This paper describes the initiative and outcomes.

Adaptation to climate change in megacities of Latin America: Regional Learning Network of the research project ClimateAdaptationSantiago (CAS)

The main objective of this publication is to document the current state of urban climate change adaptation practice in Latin America. It is a summary of the three workshops of the Regional Learning Network that was set up under the ClimateAdaptationSantiago project (CAS), encompassing six large Latin American cities (Bogotá, Buenos Aires, Lima, Mexico City, São Paulo and Santiago). It aims to synthesize information on the manifestations and impacts of climate change in those Latin American cities that participated in the network, and above all, governance in the form of concrete actions. The publication is based on information obtained from the participants in the three workshops, but also includes additional scientific input and reflections by the editors. All of this information makes a major contribution to highlighting the different paths these six cities are pursuing in response to climate change. To that end, the publication discusses the various courses of action on climate change adaptation, with the aim of learning from these cases and highlighting practical examples.

Deschooling society? A lifelong learning network for sustainable communities, urban regeneration and environmental technologies

The complexity and multifaceted nature of sustainable lifelong learning can be effectively addressed by a broad network of providers working co-operatively and collaboratively. Such a network involving the third, public and private sector bodies must realise the full potential of accredited flexible and blended formal learning, contextual opportunities offered by enablers of informal and non formal learning and the affordances derived from the various loose and open spaces that can make social learning effective. Such a conception informs the new Lifelong Learning Network Consortium on Sustainable Communities, Urban Regeneration and Environmental Technologies established and led by the Lifelong Learning Centre at Aston University. This paper offers a radical, reflective and political evaluation of its first year in development arguing that networked learning of this type could prefigure a new model for lifelong learning and sustainable education that renders the city itself a creative medium for transformative learning and sustainability.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

The importance of a new kind of learning in collaborative networks

There is wide agreement that in order to manage the increasingly complex and uncertain tasks of business, government and community, organizations can no longer operate in supreme isolation, but must develop a more networked approach. Networks are not ‘business as usual’. Of particular note is what has been referred to as collaborative networks. Collaborative networks now constitute a significant part of our institutional infrastructure. A key driver for the proliferation of these multiorganizational arrangements is their ability to facilitate the learning and knowledge necessary to survive or to respond to increasingly complex social issues In this regard the emphasis is on the importance of learning in networks. Learning applies to networks in two different ways. These refer to the kinds of learning that occur as part of the interactive processes of networks. This paper looks at the importance of these two kinds of learning in collaborative networks. The first kind of learning relates to networks as learning networks or communities of practice. In learning networks people exchange ideas with each other and bring back this new knowledge for use in their own organizations. The second type of learning is referred to as network learning. Network learning refers to how people in collaborative networks learn new ways of communicating and behaving with each other. Network learning has been described as transformational in terms of leading to major systems changes and innovation. In order to be effective, all networks need to be involved as learning networks; however, collaborative networks must also be involved in network learning to be effective. In addition to these two kinds of learning in collaborative networks this paper also focuses on the importance of how we learn about collaborative networks. Maximizing the benefits of working through collaborative networks is dependent on understanding their unique characteristics and how this impacts on their operation. This requires a new look at how we specifically teach about collaborative networks and how this is similar to and/or different from how we currently teach about interorgnizational relations.

Personalising teacher professional development : strategies enabling effective learning for educators of 21st century students

In the Knowledge Society, new demands are placed on teachers as they strive to empower young people to be global citizens, ready for the 21st century. Systemic shifts need to be made, however, to build capacity across the workforce to practise new ways of teaching and learning, including the personalisation of teacher professional development. This article argues new strategies and approaches for effective adult learning, including an individualised focus, context-based learning and an empowerment of teachers to develop their own personal learning networks. This article concludes with an analysis of the challenges facing professional development leaders in moving towards personalised teacher learning.