Learning transfer in multinational companies:Explaining inter-organisation variation

This article addresses the extent to which multinational companies (MNCs) in Ireland deploy practices aimed at the transfer of learning in their operations and the factors explaining inter-organisation variation in so doing. Using data from 260 MNCs, we find that comparatively large numbers of firms deploy practices to transfer learning in their Irish operations. Most notably, we find that almost half of all MNCs have a formal policy on organisational learning, while more than six in every ten MNCs in Ireland utilise three or more learning transfer mechanisms. In investigating inter-organisation variation with respect to these, we test a number of hypotheses involving nationality, sectoral, MNC (e.g. organisation structure) and HR factors. Our results show that the presence of international HR structures is a significant factor in explaining learning transfer in MNCs. We also find support that employment size, sector and integration between the MNC's global operations are useful variables in explaining variation in the deployment of practices on learning transfer in MNCs. © 2009 Blackwell Publishing Ltd.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

What is the learning in games-based learning?

This chapter explores the nature of “learning” in games-based learning and the cognitive and motivational processes that might underpin that learning by drawing on psychological theories and perspectives. Firstly, changing conceptions of learning over the last few decades are reviewed. This is described in relation to the changes in formal learning theories and connections made between learning theory and GBL. Secondly, the chapter reviews empirical research on the learning outcomes that have been identified for GBL, with specific focus on cognitive benefits, school attainment, collaborative working, and the motivational and engaging appeal of games. Finally, an overview of the dominant theoretical perspectives/findings mostly associated with GBL is presented in an attempt to broaden understanding of the potential for GBL in the classroom.

Efficient Structure Learning of Bayesian Networks using Constraints

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with state-of-the-art methods and with the new algorithm, which is able to handle larger data sets than before.

Learning Experience - TADD Programme

The rationale for the transatlantic programme is that the additional degree will increase the student’s skills and knowledge, affording them greater awareness of health care provision and culture within a global perspective.  This in turn, will facilitate professional and employment mobility and lifelong learning.

English Language Education at University: Trends and Challenges in Teaching and Learning Academic Discourse in the UK

This chapter discusses English Language Education at university and highlights a number of trends and their associated challenges in teaching and learning academic discourse. Academic discourse refers to the ways in which language is used by participants in academia. It encompasses written discourse, from article and book publishing, PhD theses to course assignments; spoken discourse, from study groups, tutorials, conference presentations to inaugural lectures; and more recently, computer-mediated discourse, from asynchronous text-based conferencing to academic blogs. The role of English language educators in preparing students and academics for successful participation in these academic events, or the academy, in English is not to be underestimated. Academic communication is not only vital to an individual’s success at university, but to the maintenance and creation of academic communities and to scientific progress itself (Hyland, 2009). This chapter presents an overview of academic discourse and discusses recent issues which have an impact on teaching and learning English at university and discusses their associated challenges: first, the increasing internationalisation of universities. Second, the emergence of a mobile academe in its broadest sense, in which students and academics move across traditional geopolitical, institutional and disciplinary boundaries, is discussed. Third, the growth of UK transnational higher education is examined as a trend which sees academics and students vicariously or otherwise involved in English language teaching and learning. Fourth, the chapter delves into the rapid and ongoing development in technology assisted and online learning. While responding to trends can be difficult, they can also inspire ingenuity. Furthermore, such trends and challenges will not emerge in the same manner in different contexts. The discussion in this chapter is illustrated with examples from a UK context but the implications of the trends and challenges are such that they reach beyond borders.

Sector analysis provides additional spatial information on the permanent prostate brachytherapy learning curve

PURPOSE: To evaluate the permanent prostate brachytherapy (PPB) learning curve using postimplant multisector dosimetric analysis and to assess the correlation between sector -specific dosimetry and patient-reported outcome measures (PROMs).

METHODS AND METHODS: First 200 patients treated with (125)I PPB monotherapy (145 Gy) at a single institution were assessed. Postimplant dosimetry (PID) using CT was evaluated for whole prostate (global) and 12 sectors, assessing minimum dose to 90% of prostate (D90) and dose to 0.1 cm(3) of rectum (D0.1cc). Global and sector PID results were evaluated to investigate changes in D90 with case number. Urinary and bowel PROMs were assessed using the International Prostate Symptom Score and the Expanded Prostate Cancer Index Composite questionnaire. The correlation between global and individual sector PID and urinary/bowel PROMs was also evaluated.

RESULTS: Linear regression confirmed a significant improvement in global D90 with case number (r(2) = 0.20; p = 0.001) at a rate of 0.11 Gy/case. Postimplant D90 of base sectors increased at a rate of 0.11-0.15 Gy/case (p = 0.0001) and matched global improvement. The regression lines of midgland and apex sectors were significantly different from global D90 (p = 0.01). Posterior midgland sectors showed a significant reduction in D90 with case number at a rate of 0.13-0.19 Gy/case (p = 0.01). Dose to posterior midgland sectors correlated with rectal D0.1cc dose but not bowel PROMs. Dose to posterior midgland sectors correlated with urinary International Prostate Symptom Score change, which was not apparent when global D90 alone was considered.

CONCLUSIONS: Sector analysis provided increased spatial information regarding the PPB learning curve. Furthermore, sector analysis correlated with urinary PROMs and rectal dose.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

Using Audio-visual Presentation to Teach Global Mindedness in Social Work Education

Direct experience of social work in another country is making an increasingly important contribution to internationalising the social work academic curriculum together with the cultural competency of students. However at present this opportunity is still restricted to a limited number of students. The aim of this paper is to describe and reflect on the production of an audio-visual presentation as representing the experience of three students who participated in an exchange with a social work programme in Pune, India. It describes and assesses the rationale, production and use of video to capture student learning from the Belfast/Pune exchange. We also describe the use of the video in a classroom setting with a year group of 53 students from a younger cohort. This exercise aimed to stimulate students’ curiosity about international dimensions of social work and add to their awareness of poverty, social justice, cultural competence and community social work as global issues. Written classroom feedback informs our discussion of the technical as well as the pedagogical benefits and challenges of this approach. We conclude that some benefit of audio-visual presentation in helping students connect with diverse cultural contexts, but that a complementary discussion challenging stereotyped viewpoints and unconscious professional imperialism is also crucial.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.

Education for global citizenship in Scotland: reciprocal partnership or politics of benevolence?

Links between schools in the United Kingdom and partner schools in developing countries are an increasingly popular approach to teaching global citizenship. This study addresses the limited empirical research to date on the influence of such links on pupils' learning and understanding. Following an overview of the curricular theme of global citizenship in the Scottish curriculum and in the context of a partnership between Scotland and Malawi, challenges and potential pitfalls of teaching global citizenship are illustrated by the voices of pupils at four schools. Data is analysed through the themes of knowledge and understanding, concerns about fairness, and giving and helping. We reflect on whether our study indicates the intended reciprocal partnership or a 'politics of benevolence'.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.