Motivation and mobile devices: exploring the role of appropriation and coping strategies

There has been interest recently in how mobile devices may be motivating forces in the right contexts: for example, one of the themes for the IADIS International Conference on Mobile Learning in 2007 was ‘Affective Factors in Learning with Mobile Devices’ (http://www.mlearningconf. org). The authors have previously proposed six aspects of learning with mobile devices in informal contexts that might be motivating: control over learners’ goals, ownership, fun, communication, learning-in-context and continuity between contexts. How do these motivational features relate to theoretical accounts of what motivates people to use mobile devices and learn in technology- rich contexts? In this exploratory paper we consider two different candidates for such theoretical approaches. One is technology appropriation—the process by which technology or particular technological artefacts are adopted and shaped in use. Two different approaches to technology appropriation are discussed in order to explore the relationship between the different aspects of appropriation and motivation; that of Carroll et al. and that of Waycott. Both appropriation frameworks have been developed in the context of using mobile devices, but neither has a specific focus on learning. By contrast, the second theoretical approach is Järvelä et al.’s model of coping strategies, which is specifically concerned with learning with technologies, although not with mobile technologies in particular. The paper draws on case-study data in order to illustrate and discuss the extent to which these two approaches are helpful in informing our understanding of the motivating features of using mobile devices for informal learning

Variable domain transformation for linear PAC analysis of mixed-signal systems

This paper describes a method to perform linear AC analysis on mixed-signal systems which appear strongly nonlinear in the voltage domain but are linear in other variable domains. Common circuits like phase/delay-locked loops and duty-cycle correctors fall into this category, since they are designed to be linear with respect to phases, delays, and duty-cycles of the input and output clocks, respectively. The method uses variable domain translators to change the variables to which the AC perturbation is applied and from which the AC response is measured. By utilizing the efficient periodic AC (PAC) analysis available in commercial RF simulators, the circuit’s linear transfer function in the desired variable domain can be characterized without relying on extensive transient simulations. Furthermore, the variable domain translators enable the circuits to be macromodeled as weakly-nonlinear systems in the chosen domain and then converted to voltage-domain models, instead of being modeled as strongly-nonlinear systems directly

Fast, non-monte-carlo estimation of transient performance variation due to device mismatch

This paper describes an efficient way of simulating the effects of device random mismatch on circuit transient characteristics, such as variations in delay or in frequency. The proposed method models DC random offsets as equivalent AC pseudo-noises and leverages the fast, linear periodically time-varying (LPTV) noise analysis available from RF circuit simulators. Therefore, the method can be considered as an extension to DC match analysis and offers a large speed-up compared to the traditional Monte-Carlo analysis. Although the assumed linear perturbation model is valid only for small variations, it enables easy ways to estimate correlations among variations and identify the most sensitive design parameters to mismatch, all at no additional simulation cost. Three benchmarks measuring the variations in the input offset voltage of a clocked comparator, the delay of a logic path, and the frequency of an oscillator demonstrate the speed improvement of about 100-1000x compared to a 1000-point Monte-Carlo method

An Analysis Framework for Mobility Metrics in Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) have inherently dynamic topologies. Under these difficult circumstances, it is essential to have some dependable way of determining the reliability of communication paths. Mobility metrics are well suited to this purpose. Several mobility metrics have been proposed in the literature, including link persistence, link duration, link availability, link residual time, and their path equivalents. However, no method has been provided for their exact calculation. Instead, only statistical approximations have been given. In this paper, exact expressions are derived for each of the aforementioned metrics, applicable to both links and paths. We further show relationships between the different metrics, where they exist. Such exact expressions constitute precise mathematical relationships between network connectivity and node mobility. These expressions can, therefore, be employed in a number of ways to improve performance of MANETs such as in the development of efficient algorithms for routing, in route caching, proactive routing, and clustering schemes

Three-Dimensional Simulations of the Parker Instability in a Uniformly-rotating Disk

We investigate the nonlinear effects of uniform rotation on the Parker instability in an exponentially-stratified disk through high-resolution simulations. During the linear stage, the speed of gas motion is subsonic and the evolution with the rotation is not much different from that without the rotation. This is because the Coriolis force is small. During the nonlinear stage, oppositely-directed supersonic flows near a magnetic valley are under the influence of the Coriolis force with different directions, resulting in twisted magnetic field lines near the valley. Sheet-like structures, which are tilted with respect to the initial field direction, are formed with an 1.5 enhancement of column density with respect to its initial value. Even though uniform rotation doesn't give much impact on density enhancement, it generates helically twisted field lines, which may become an additional support mechanism of clouds.Comment: 3 pages, uses rmaa.cls, to appear in Proc. of the Conference on "Astrophysical Plasmas: Codes, Models and Observations", Eds. J. Franco, J. Arthur, N. Brickhouse, Rev.Mex.AA Conf. Serie

Harnessing Technology: new modes of technology-enhanced learning: a case study series

This report presents the outcomes and conclusions from a series of 18 case studies exploring the innovative use of technology for learning and teaching using new modes of technology

A Package for the Automated Classification of Periodic Variable Stars

We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. In order to assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if the number of data points is larger than 80 and the duration is more than a few weeks. The classifier software of the subclass model is available from the GitHub repository (https://goo.gl/xmFO6Q).Comment: 16 pages, 11 figures, accepted for publication in A&

Assessment of stochastic and deterministic models of 6304 quasar lightcurves from SDSS Stripe 82

The optical light curves of many quasars show variations of tenths of a magnitude or more on time scales of months to years. This variation often cannot be described well by a simple deterministic model. We perform a Bayesian comparison of over 20 deterministic and stochastic models on 6304 QSO light curves in SDSS Stripe 82. We include the damped random walk (or Ornstein-Uhlenbeck [OU] process), a particular type of stochastic model which recent studies have focused on. Further models we consider are single and double sinusoids, multiple OU processes, higher order continuous autoregressive processes, and composite models. We find that only 29 out of 6304 QSO lightcurves are described significantly better by a deterministic model than a stochastic one. The OU process is an adequate description of the vast majority of cases (6023). Indeed, the OU process is the best single model for 3462 light curves, with the composite OU process/sinusoid model being the best in 1706 cases. The latter model is the dominant one for brighter/bluer QSOs. Furthermore, a non-negligible fraction of QSO lightcurves show evidence that not only the mean is stochastic but the variance is stochastic, too. Our results confirm earlier work that QSO light curves can be described with a stochastic model, but place this on a firmer footing, and further show that the OU process is preferred over several other stochastic and deterministic models. Of course, there may well exist yet better (deterministic or stochastic) models which have not been considered here.Comment: accepted by AA, 12 pages, 11 figures, 4 table

A Markov Switching Model of Congressional Partisan Regimes

Studies of development and change in partisan fortunes in the US emphasize epochs of partisan stability, separated by critical events or turning points. A major empirical issue that has plagued the study of American political development is the estimation of the critical moments and durations of these partisan regimes. In this paper we introduce a fresh approach to the study of partisan regimes. Our method is based in the method of Markov switching, introduced by James Hamilton. We apply Hamilton’s approach to the size of party coalitions in the US House of Representatives from 1854 to the present. Our model assumes that the political system is either in a state of domination by one party or it is not (in which case the other party dominates). The Markov switching approach also yields estimated state probabilities that allow us to make inferences about periods of empirical party balance. Roughly speaking, when the Republicans constitute the dominant partisan coalition, they can expect to capture 60 percent of House seats in any given election. The Democrats can expect 59 percent when dominant. Our method also allows the estimation of critical transition points between Republican and Democratic partisan coalitions. The periods we identify as governed by a being Republican coalition are roughly 1860 through 1872, 1894 through 1906, and 1918 through 1928.