Linking Locations: Storytelling with Pervasive Technology

With online location-aware smart phones in more and more pockets, storytelling is moving to the streets. Simultaneously, an increasing abundance of Linked Data is being made available, complete with geographical information. In this paper, we review the state of the art and suggest approaches to, and issues with, a storytelling system that combines these two technologies

The potential of mobile phones to transform teacher professional development

Futures thinking is used by governments to consider long-term strategic approaches and develop policies and practices that are potentially resilient to future uncertainty. English in Action (EIA), arguably the world’s largest English language teacher professional development (TPD) project, used futures thinking to author possible, probable and preferable future scenarios to solve the project’s greatest technological challenge: how to deliver audio-visual TPD materials and hundreds of classroom audio resources to 75,000 teachers by 2017. Authoring future scenarios and engaging in possibility thinking (PT) provided us with a taxonomy of question-posing and question-responding that assisted the project team in being creative. This process informed the successful pilot testing of a mobile phone-based technology kit to deliver TPD resources within an open distance learning (ODL) platform. Taking the risk and having the foresight to trial mobile phones in remote rural areas with teachers and students led to unforeseen innovation. As a result EIA is currently using a mobile phone-based technology kit with 12,500 teachers to improve the English language proficiency of 700,000 students. As the project scales up in its third and final phase, we are using the new technology kit—known as the ‘trainer in your pocket’—to foster a ‘quiet revolution’ in the provision of teacher professional development at scale to an additional 67,500 teachers and 10 million students

The effect of abrupt weaning of suckler calves on the plasma concentrations of cortisol, catecholamines, leukocyte, acute-phase proteins and in vitro interferon-gamma production

End of project reportThe objective of this study was to examine the effect of abrupt weaning (inclusive of social group disruption and maternal separation) on the physiological mediators of stress and measures of immune function. Thirty-eight male and 38 female continental calves were habituated to handling for two weeks prior to bleeding. Calves were blocked on sex, weight and breed of dam and randomly assigned, within block, to either a control (cows remain with calves) or abruptly weaned group (calves removed from cows). Animals were separated into the respective treatment groups at weaning (0 h). Calves were bled at – 168 h, 6 h (males only), 24 h, 48 h and 168 h post weaning. At each sampling time an observer scored the behavioural reaction of calves to sampling. Blood samples were analysed for cortisol, catecholamine concentrations (not sampled at –168 h) and in vitro interferon-gamma production, neutrophil :lymphocyte ratio and acute phase protein concentrations. All continuous data were analysed using a split-plot ANOVA, except that collected at 6 h, which was analysed using a single factor ANOVA model. The effects of weaning, calf sex and time and respective interactions were described. Disruption of the established social groups at 0 h, increased (p<0.001) the plasma cortisol concentration and neutrophil: lymphocyte ratio and reduced the leukocyte concentration (p<0.001) and the in vitro interferon-gamma response to the mitogen concanavalin-A (p<0.001) and keyhole limpet haemocyanin (p<0.001) for weaned and control animals, when compared with –168h. Plasma adrenaline and noradrenaline concentrations were not affected by group disruption. There was no effect of weaning or sex on calf behavioural reaction to handling during blood sampling. Plasma cortisol and adrenaline concentrations were not affected by weaning or sex. Plasma noradrenaline concentration was influenced by weaning x sex (p<0.05) and time x sex (p<0.05). The response increased for male calves with weaning and increased with each sampling time post weaning. For heifers the response was not affected by weaning and plasma concentrations decreased at 168 h post weaning. There was no effect of weaning or sex on leukocyte concentration. The neutrophils : lymphocyte ration increased post weaning (p<0.01) and was affected by sex (p<0.05). Weaning decreased (p<0.05) the in vitro interferon-gamma response to the antigen KLH. There was a time x weaning x sex (p<0.05) interaction for fibrinogen concentration but no effect of treatment on haptoglobin concentration. Abrupt weaning increased plasma cortisol and nor-adrenaline concentrations, which was accompanied by attenuation of in vitro interferon gamma production to novel mitogen and antigen complexes up to 7 days post weaning.European Union Structural Funds (EAGGF

Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

Efficient First-Order Temporal Logic for Infinite-State Systems

In this paper we consider the specification and verification of infinite-state systems using temporal logic. In particular, we describe parameterised systems using a new variety of first-order temporal logic that is both powerful enough for this form of specification and tractable enough for practical deductive verification. Importantly, the power of the temporal language allows us to describe (and verify) asynchronous systems, communication delays and more complex properties such as liveness and fairness properties. These aspects appear difficult for many other approaches to infinite-state verification.Comment: 16 pages, 2 figure

On the Assessment of Stability and Patterning of Speech Movements

Speech requires the control of complex movements of orofacial structures to produce dynamic variations in the vocal tract transfer function. The nature of the underlying motor control processes has traditionally been investigated by employing measures of articulatory movements, including movement amplitude, velocity, and duration, at selected points in time. An alternative approach, first used in the study of limb motion, is to examine the entire movement trajectory over time. A new approach to speech movement trajectory analysis was introduced in earlier work from this laboratory. In this method, trajectories from multiple movement sequences are time- and amplitude-normalized, and the STI (spatiotemporal index) is computed to capture the degree of convergence of a set of trajectories onto a single, underlying movement template. This research note describes the rationale for this analysis and provides a detailed description of the signal processing involved. Alternative interpolation procedures for time-normalization of kinematic data are also considered

Variational bayes for estimating the parameters of a hidden Potts model

Hidden Markov random field models provide an appealing representation of images and other spatial problems. The drawback is that inference is not straightforward for these models as the normalisation constant for the likelihood is generally intractable except for very small observation sets. Variational methods are an emerging tool for Bayesian inference and they have already been successfully applied in other contexts. Focusing on the particular case of a hidden Potts model with Gaussian noise, we show how variational Bayesian methods can be applied to hidden Markov random field inference. To tackle the obstacle of the intractable normalising constant for the likelihood, we explore alternative estimation approaches for incorporation into the variational Bayes algorithm. We consider a pseudo-likelihood approach as well as the more recent reduced dependence approximation of the normalisation constant. To illustrate the effectiveness of these approaches we present empirical results from the analysis of simulated datasets. We also analyse a real dataset and compare results with those of previous analyses as well as those obtained from the recently developed auxiliary variable MCMC method and the recursive MCMC method. Our results show that the variational Bayesian analyses can be carried out much faster than the MCMC analyses and produce good estimates of model parameters. We also found that the reduced dependence approximation of the normalisation constant outperformed the pseudo-likelihood approximation in our analysis of real and synthetic datasets