How can exploratory learning with games and simulations within the curriculum be most effectively evaluated?

There have been few attempts to introduce frameworks that can help support tutors evaluate educational games and simulations that can be most effective in their particular learning context and subject area. The lack of a dedicated framework has produced a significant impediment for uptake of games and simulations particularly in formal learning contexts. This paper aims to address this shortcoming by introducing a four-dimensional framework for helping tutors to evaluate the potential of using games- and simulation- based learning in their practice, and to support more critical approaches to this form of games and simulations. The four-dimensional framework is applied to two examples from practice to test its efficacy and structure critical reflection upon practice

Offline and online detection of damage using autoregressive models and artificial neural networks

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Baryons and confining strings

The subleading term of the heavy quark potential (the analogue of the Luscher term) is computed in a string model for the case of three quarks. It turns out to be positive in 2+1 dimensions, making the potential non-concave as a function of the scale for fixed geometry. The results are compared to numerical simulations of the lattice gauge theory.Comment: Lattice2003(topology), 3 pages, 2 figure

A time series distance measure for efficient clustering of input output signals by their underlying dynamics

Starting from a dataset with input/output time series generated by multiple deterministic linear dynamical systems, this paper tackles the problem of automatically clustering these time series. We propose an extension to the so-called Martin cepstral distance, that allows to efficiently cluster these time series, and apply it to simulated electrical circuits data. Traditionally, two ways of handling the problem are used. The first class of methods employs a distance measure on time series (e.g. Euclidean, Dynamic Time Warping) and a clustering technique (e.g. k-means, k-medoids, hierarchical clustering) to find natural groups in the dataset. It is, however, often not clear whether these distance measures effectively take into account the specific temporal correlations in these time series. The second class of methods uses the input/output data to identify a dynamic system using an identification scheme, and then applies a model norm-based distance (e.g. H2, H-infinity) to find out which systems are similar. This, however, can be very time consuming for large amounts of long time series data. We show that the new distance measure presented in this paper performs as good as when every input/output pair is modelled explicitly, but remains computationally much less complex. The complexity of calculating this distance between two time series of length N is O(N logN).Comment: 6 pages, 4 figures, sent in for review to IEEE L-CSS (CDC 2017 option

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

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Prediction of seismic-induced structural damage using artificial neural networks

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