Teachers' professional development in a community

The goal of this article was to study teachers' professional development related to web-based learning in the context of the teacher community. The object was to learn in what kind of networks teachers share the knowledge of web-based learning and what are the factors in the community that support or challenge teachers professional development of web-based learning. The findings of the study revealed that there are teachers who are especially active, called the central actors in this study, in the teacher community who collaborate and share knowledge of web-based learning. These central actors share both technical and pedagogical knowledge of web-based learning in networks that include both internal and external relations in the community and involve people, artefacts and a variety of media. Furthermore, the central actors appear to bridge different fields of teaching expertise in their community. According to the central actors' experiences the important factors that support teachers' professional development of web-based learning in the community are; the possibility to learn from colleagues and from everyday working practices, an emotionally safe atmosphere, the leader's personal support and community-level commitment. Also, the flexibility in work planning, challenging pupils, shared lessons with colleagues, training events in an authentic work environment and colleagues' professionalism are considered meaningful for professional development. As challenges, the knowledge sharing of web-based learning in the community needs mutual interests, transactive memory, time and facilities, peer support, a safe atmosphere and meaningful pedagogical practices. On the basis of the findings of the study it is suggested that by intensive collaboration related to web-based learning it may be possible to break the boundaries of individual teachership and create such sociocultural activities which support collaborative professional development in the teacher community. Teachers' in-service training programs should be more sensitive to the culture of teacher communities and teachers' reciprocal relations. Further, teacher trainers should design teachers' in-service training of web-based learning in co-evolution with supporting networks which include the media and artefacts as well as people.

Teachers' professional development in a community

The goal of this article was to study teachers' professional development related to web-based learning in the context of the teacher community. The object was to learn in what kind of networks teachers share the knowledge of web-based learning and what are the factors in the community that support or challenge teachers professional development of web-based learning. The findings of the study revealed that there are teachers who are especially active, called the central actors in this study, in the teacher community who collaborate and share knowledge of web-based learning. These central actors share both technical and pedagogical knowledge of web-based learning in networks that include both internal and external relations in the community and involve people, artefacts and a variety of media. Furthermore, the central actors appear to bridge different fields of teaching expertise in their community. According to the central actors' experiences the important factors that support teachers' professional development of web-based learning in the community are; the possibility to learn from colleagues and from everyday working practices, an emotionally safe atmosphere, the leader's personal support and community-level commitment. Also, the flexibility in work planning, challenging pupils, shared lessons with colleagues, training events in an authentic work environment and colleagues' professionalism are considered meaningful for professional development. As challenges, the knowledge sharing of web-based learning in the community needs mutual interests, transactive memory, time and facilities, peer support, a safe atmosphere and meaningful pedagogical practices. On the basis of the findings of the study it is suggested that by intensive collaboration related to web-based learning it may be possible to break the boundaries of individual teachership and create such sociocultural activities which support collaborative professional development in the teacher community. Teachers' in-service training programs should be more sensitive to the culture of teacher communities and teachers' reciprocal relations. Further, teacher trainers should design teachers' in-service training of web-based learning in co-evolution with supporting networks which include the media and artefacts as well as people.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.

Improving memory in elderly adults by instructing them in professional actors' learning strategies

This study examined a new type of cognitive intervention. For four weeks, participants (ages 65 to 82) were instructed in professional acting techniques, followed by rehearsal and performance of theatrical scenes. Although the training was not targeted in any way to the tasks used in pre- and post-testing, participants produced significantly higher recall and recognition scores after the intervention. It is suggested that the cognitive effort involved in analyzing and adopting theatrical characters' motivations (and then experiencing those characters' mental/emotional states during performance) is responsible for the observed improvement. A secondary strand of this study showed that participants who were given annotated scripts in which the implied goals of the characters were made explicit demonstrated significantly faster access to the stored material, as measured by a computer latency task.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Learning Bayesian networks from data by the incremental compilation of new network polynomials

Probabilistic graphical models are a huge research field in artificial intelligence nowadays. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of the main research topics related to this area focus on performing inference over graphical models and on learning graphical models from data. Traditionally, the inference process and the learning process have been treated separately, but given that the learned models structure marks the inference complexity, this kind of strategies will sometimes produce very inefficient models. With the purpose of learning thinner models, in this master thesis we propose a new model for the representation of network polynomials, which we call polynomial trees. Polynomial trees are a complementary representation for Bayesian networks that allows an efficient evaluation of the inference complexity and provides a framework for exact inference. We also propose a set of methods for the incremental compilation of polynomial trees and an algorithm for learning polynomial trees from data using a greedy score+search method that includes the inference complexity as a penalization in the scoring function.

Computer Networks Virtualization with GNS3: Evaluating a solution to optimize resources and achieve a distance learning

Designing educational resources allow students to modify their learning process. In particular, on-line and downloadable educational resources have been successfully used in engineering education the last years [1]. Usually, these resources are free and accessible from web. In addition, they are designed and developed by lecturers and used by their students. But, they are rarely developed by students in order to be used by other students. In this work-in-progress, lecturers and students are working together to implement educational resources, which can be used by students to improve the learning process of computer networks subject in engineering studies. In particular, network topologies to model LAN (Local Area Network) and MAN (Metropolitan Area Network) are virtualized in order to simulate the behavior of the links and nodes when they are interconnected with different physical and logical design.

Fostering professional development through integrating work and formal learning

The Undergraduate Site Learning Program (USLP) is an innovative work-based learning program that addresses the call to develop a broader set ofattributes in engineering graduates. Unlike cooperative education programs, site learning can give students full academic credit for their placement without extending the duration of the degree through the use of an innovative learning alignment model. A cenrralpart ofthis program is a unique course entitled Professional Development in which students articulate and reflect upon the lessons they leom while on placement in industry. Students spend the bulk ofa semester on-site often in remote locations, which requires a flexible approach to course operation and fosters independent learning. Thus the USLP challenges both staff and students and produces outcomes that bofh the alumni and industry value.