Social networking and open educational resources: updating quality assurance for e-learning excellence

Quality assurance approaches in higher education are well-established, but it is important to develop methods which are applicable to the domain of e-learning. The E-xcellence methodology (EADTU, 2009a) was therefore designed to assess the quality of e-learning in distance learning and blended learning contexts. The methodology is based around a set of benchmarks, supported by a practitioner handbook and a web-based ‘QuickScan’ self-evaluation tool. Experience shows that the E-xcellence methodology is particularly valuable for the process of improvement through collaborative internal review. E-learning has evolved since the E-xcellence methodology was first developed. In particular, there is increasing awareness and use of open education resources (OERs) and social networking. However, these aspects were not explicit in the original E-xcellence resources. The E-xcellence Next project was therefore established to update the resources, incorporating these developments. To begin this process, a consultation was carried out among E-xcellence Next project members, followed by a participatory workshop on the themes of social networking and OERs. The E-xcellence resources were also used in a series of self-evaluation seminars held at European higher education institutions. Experience and feedback from these activities has been used to update the manual, the benchmarks and the QuickScan tool. The result is a set of quality assurance resources which encompass social networking, OERs and other recent developments in e-learning

Parallel Cross-Entropy Optimization

The cross-entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are highly complex and can take weeks or even longer to solve using current optimization techniques. This paper presents a general method for designing parallel CE algorithms for multiple instruction multiple data (MIVID) distributed memory machines using the message passing interface (MPI) library routines. We provide examples of its performance for two well-known test-cases: the (discrete) Max-Cut problem and (continuous) Rosenbrock problem. Speedup factors and a comparison to sequential CE methods are reported

Enabling Multi-Stakeholder Cooperative Modelling in Automotive Software Development and Implications for Model Driven Software Development

One of the motivations for a model driven approach to software development is to increase the involvement for a range of stakeholders in the requirements phases. This inevitably leads to a greater diversity of roles being involved in the production of models, and one of the issues with such diversity is that of providing models which are both accessible and appropriate for the phenomena being modelled. Indeed, such accessibility issues are a clear focus of this workshop. However, a related issue when producing models across multiple parties,often at dierent sites, or even dierent organisations is the management of such model artefacts. In particular, different parties may wish to experiment with model choices. For example, this idea of prototypingprocesses by experimenting with variants of models is one which has been used for many years by business process modellers, in order to highlight the impact of change, and thus improve alignment of process and supporting software specications. The problem often occurs when such variants needed to be merged, for example, to be used within a shared repository. This papers reports upon experiences and ndings of this merging problem as evaluated at Bosch Automotive. At Bosch we have dierent sites where modellers will make changes to shared models, and these models will subsequently require merging into a common repository. Currently, this work has concentrated on one type of diagram, the class diagram. However, it seems clear that the issue of how best to merge models where collaborative multi-party working takes places is one which has a significant potential impact upon the entire model driven process, and, given the diversity of stakeholders, could be particularly problematic for the requirements phase. In fact, class diagrams can also be used for information or data models created in the system analysis step. Hence, we believe that the lessons learned from this work will be valuable in tackling the realities of a commercially viable model driven process

A Heuristic Framework for Next-Generation Models of Geostrophic Convective Turbulence

Many geophysical and astrophysical phenomena are driven by turbulent fluid dynamics, containing behaviors separated by tens of orders of magnitude in scale. While direct simulations have made large strides toward understanding geophysical systems, such models still inhabit modest ranges of the governing parameters that are difficult to extrapolate to planetary settings. The canonical problem of rotating Rayleigh-B\'enard convection provides an alternate approach - isolating the fundamental physics in a reduced setting. Theoretical studies and asymptotically-reduced simulations in rotating convection have unveiled a variety of flow behaviors likely relevant to natural systems, but still inaccessible to direct simulation. In lieu of this, several new large-scale rotating convection devices have been designed to characterize such behaviors. It is essential to predict how this potential influx of new data will mesh with existing results. Surprisingly, a coherent framework of predictions for extreme rotating convection has not yet been elucidated. In this study, we combine asymptotic predictions, laboratory and numerical results, and experimental constraints to build a heuristic framework for cross-comparison between a broad range of rotating convection studies. We categorize the diverse field of existing predictions in the context of asymptotic flow regimes. We then consider the physical constraints that determine the points of intersection between flow behavior predictions and experimental accessibility. Applying this framework to several upcoming devices demonstrates that laboratory studies may soon be able to characterize geophysically-relevant flow regimes. These new data may transform our understanding of geophysical and astrophysical turbulence, and the conceptual framework developed herein should provide the theoretical infrastructure needed for meaningful discussion of these results.Comment: 36 pages, 8 figures. CHANGES: in revision at Geophysical and Astrophysical Fluid Dynamic

Bayesian Inference in Estimation of Distribution Algorithms

Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAG c