Livestock and learning: evaluation of a prototype 3D virtual learning environment for livestock practitioners in India and Bolivia

Livestock are a key asset for the global poor. However, access to relevant information is a critical issue for both the poor and the practitioners who serve them. Therefore, the authors describe a web-based Virtual Learning Environment to disseminate educational materials on priority animal health constraints in Bolivia and India. The aim was to explore demand for 3D among development practitioners in the South. Two wider arguments from the ICT4D literature framed the analysis: the concept of 3D as a ‘lead technology’ and the relevance of Internet skills to the adoption of a 3D format. The results illustrated that neither construct influenced demand. Rather, study participants were ready adopters but desired greater levels of interaction and thereby, a more collaborative learning environment. Therefore, 3D has a number of potential benefits to enhance knowledge sharing among community practitioners in the Global South.

Auditing technology uses within a global voluntary organisation

WAGGGS, the World Association of Girl Guides and Girl Scouts, is the umbrella organization for Member Organizations from 145 countries around the world. As such one of its remits is to provide programmes that promote leadership development and opportunities for girls and young women to advocate on issues they care about. One of the ways WAGGGS is exploring to do this more widely and efficiently is through the use of digital technologies. This paper presents the results of an audit undertaken of the technologies already used by potential participants in online communities and courses and investigates the challenges faced in using technology to facilitate learning, within this context.

Enhancing fieldwork learning with technology: practitioner's perspectives

This research paper reports the findings from an international survey of fieldwork practitioners on their use of technology to enhance fieldwork teaching and learning. It was found that there was high information technology usage before and after time in the field, but some were also using portable devices such as smartphones and global positioning system whilst out in the field. The main pedagogic reasons cited for the use of technology were the need for efficient data processing and to develop students' technological skills. The influencing factors and barriers to the use of technology as well as the importance of emerging technologies are discussed.

Experience of international students from developing countries in negotiating technology for learning

International students are important economically and culturally, bringing diversity and an international perspective enriching learning experiences in classrooms. With the global transformations eLearning has become an important element of students’ higher education experience in developed countries. Although students of developed countries have digital exposure at an early age, many students from developing countries, on the journey of becoming international students, are inadequately prepared for eLearning. The lack of digital skills, prior experience, cultural differences and language barriers together with the drastic changes in learning environments require international students to not only adapt to the host environment but also to negotiate technology for learning. The scarcity of research exploring the eLearning experiences of international students from developing countries and the benefits of this understanding is discussed in an effort to promote research in this area.

Learning CSR for sustainable corporate advantage

Corporate Social Responsibility (CSR) has become a strategic and operational reality of the business and academic world. Not that the principles of CSR are always respected or that its practice is consistently applied. Bearing in mind the multi-faceted nature of both CSR and the corporate environment, as well as the paradox of what is taught in Higher Education and what is practised within its own walls, this paper provides a learning cyclical pathway to sustainable CSR implementation and progress review. As well as highlighting the role that Higher Education has to play, the paper emphasises that in order to embed CSR within the corporate environment, questions need to be raised concerning on-going CSR improvement in order to both protect and engage a wide range of stakeholders towards sustainable corporate advantage.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Problem-based learning and masculinities : unique opportunities for Mr. Fix It? Well, maybe

I watched a smoker of 30 years being admitted to the Coronary Care unit following an acute Myocardial Infarction (heart attack). The message from the male clinician was simple, accurate, but somewhat behaviourist: " the death of part of your heart muscle is the result of your smoking, if you don’t stop smoking the damage will continue and you will die." A global, proactive and humanistic consultation demonstrating an understanding of the man’s addiction to a legal and accessible drug and illuminating prevention strategies may have been more appropriate. Maybe the interaction was about competing masculinities, the risk taker and the problem solver. The irony? As I left the hospital that night I observed the same clinician strategically positioned in a secluded hospital doorway drawing heavily on a cigarette. Hypocrite? No, invincible late 20’s male? Maybe. Smoking was someone else’s problem – at least today.

In my 16 years as a clinician such scenarios are common. Clinical practice based predominantly on problem solving potentiates hegemonic masculine approaches to treating men in clinical practice, often justified by limited health resources and increasing patient acuity. Ironically, Problem-based Learning (PBL) curriculums commonly used in health sciences higher education encourages, nurtures and rewards such problem solving approaches. As a teaching academic with current clinical practice it occurs to me that health science education and PBL has an opportunity if not obligation to empower clinicians to establish holistic approaches to male health presentations.

This paper explores the interconnections of Problem-based Learning (PBL) curriculums, health promotion, male nurses’ health-related behaviours and the implications and specificities of masculinity. The pilot study offers an insight into the perceptions of three male nurses that completed undergraduate nursing studies in PBL curriculums. The data obtained introduces some connections that could be illuminated by further research.

Combining extension matrix and integer programming for optimal concept learning

This paper proposes two integer programming models and their GA-based solutions for optimal concept learning. The models are built to obtain the optimal concept description in the form of propositional logic formulas from examples based on completeness, consistency and simplicity. The simplicity of the propositional rules is selected as the objective function of the integer programming models, and the completeness and consistency of the concept are used as the constraints. Considering the real-world problems that certain level of noise is contained in data set, the constraints in model 11 are slacked by adding slack-variables. To solve the integer programming models, genetic algorithm is employed to search the global solution space. We call our approach IP-AE. Its effectiveness is verified by comparing the experimental results with other well- known concept learning algorithms: AQ15 and C4.5.

Restructuring educational leadership in changing contexts: a local/global account of restructuring in Australia

The 1990s saw considerable structural reform in school education in many Anglophone nation states, marked by trends towards school-based, site-based, self-managing and self-governing schools. This article illustrates through a case study of educational restructuring in Victoria, Australia, how leadership, as a discursive practice, is redefined in the context of spatial and cultural restructuring. Restructuring produced a spatial redistribution of educational provision and individual opportunities as a result of structural adjustment reforms. These same policy moves towards post-welfarism also produced cultural shifts in attitudes to education with the rise of the new instrumentalism and entrepeneurialism. For school principals at the forefront of self managing schools, this meant shifts in resource distribution through new policy mechanisms of managerial and market accountability, and also new priorities impacting on leadership practices with a move from dialogic to decisional modes of management. The question is how recent policy moves towards learning networks and reinventing systematic support with a focus on locational disadvantage are addressing what were increased educational disparities between schools and students. Does this provide scope for more equity-driven leadership practices?

Global optimisation of neural networks using a deterministic hybrid approach

Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. In this paper we introduce a recently developed cutting angle method (a deterministic technique) for global optimization of connection weights. Neural networks are initially trained using the cutting angle method and later the learning is fine-tuned (meta-learning) using conventional gradient descent or other optimization techniques. Experiments were carried out on three time series benchmarks and a comparison was done using evolutionary neural networks. Our preliminary experimentation results show that the proposed deterministic approach could provide near optimal results much faster than the evolutionary approach.