Evaluating the development of wearable devices, personal data assistants and the use of other mobile devices in further and higher education institutions

This report presents technical evaluation and case studies of the use of wearable and mobile computing mobile devices in further and higher education. The first section provides technical evaluation of the current state of the art in wearable and mobile technologies and reviews several innovative wearable products that have been developed in recent years. The second section examines three scenarios for further and higher education where wearable and mobile devices are currently being used. The three scenarios include: (i) the delivery of lectures over mobile devices, (ii) the augmentation of the physical campus with a virtual and mobile component, and (iii) the use of PDAs and mobile devices in field studies. The first scenario explores the use of web lectures including an evaluation of IBM's Web Lecture Services and 3Com's learning assistant. The second scenario explores models for a campus without walls evaluating the Handsprings to Learning projects at East Carolina University and ActiveCampus at the University of California San Diego . The third scenario explores the use of wearable and mobile devices for field trips examining San Francisco Exploratorium's tool for capturing museum visits and the Cybertracker field computer. The third section of the report explores the uses and purposes for wearable and mobile devices in tertiary education, identifying key trends and issues to be considered when piloting the use of these devices in educational contexts

Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

The visual sociogram in qualitative and mixed-methods research

The paper investigates the place of visual tools in mixed-methods research on social networks, arguing that they can not only improve the communicability of results, but also support research at the data gathering and analysis stages. Three examples from the authors’ own research experience illustrate how sociograms can be integrated in multiple ways with other analytical tools, both quantitative and qualitative, positioning visualization at the intersection of varied methods and channelling substantive ideas as well as network insight in a coherent way. Visualization also facilitates the participation of a broad range of stakeholders, including among others, study participants and non-specialist researchers. It can support the capacity of qualitative and mixed-methods research to reach out to areas of the social that are difficult to circumscribe, such as hidden populations and informal organisations. On this basis, visualization appears as a unique opportunity for mixing methods in the study of social networks, emphasizing both structure and process at the same time

A Multiorganisational Study of the Drivers and Barriers of Enterprise Collaboration Systems-Enabled Change

Enterprise Collaboration Systems (ECS) are emerging as the de facto technology platform for the digital workplace. This paper presents findings from an in-depth, multiorganisational study that examines the drivers and barriers of ECS-enabled change from two perspectives: i) the company initiating and driving the project and ii) key practitioners responsible for delivering the change. Data is collected from ECS using companies via a survey and face-to-face workshops, analysed using qualitative content analysis methods to identify categories of change and then synthesised to provide a rich classification and visualisation of the drivers, barriers, motivations and pain points (DBMP) to ECS-enabled change. This is followed by a discussion of the similarities and differences between drivers and barriers from both personal and company perspectives. The paper concludes by exploring the potential of the research and visualisation methods used in this work to provide the foundation for the longitudinal study of ECS-enabled change

Multimodal Egocentric Analysis of Focused Interactions

Continuous detection of social interactions from wearable sensor data streams has a range of potential applications in domains, including health and social care, security, and assistive technology. We contribute an annotated, multimodal data set capturing such interactions using video, audio, GPS, and inertial sensing. We present methods for automatic detection and temporal segmentation of focused interactions using support vector machines and recurrent neural networks with features extracted from both audio and video streams. The focused interaction occurs when the co-present individuals, having the mutual focus of attention, interact by first establishing the face-to-face engagement and direct conversation. We describe an evaluation protocol, including framewise, extended framewise, and event-based measures, and provide empirical evidence that the fusion of visual face track scores with audio voice activity scores provides an effective combination. The methods, contributed data set, and protocol together provide a benchmark for the future research on this problem

The Spatio-Temporal Analysis of the Use and Usability Problems of EV Workplace Charging Facilities

With the worldwide calls to meet greenhouse gas targets and policy objectives by 2030, finding an electric vehicle (EV) on the way to work every day has become less surprising. Adapting to owning an EV is challenging to all potential users. Current users tend to rely on domestic charging for a more certain and less hassle charging opportunity. The demand is shifting towards workplace charging (WPC) as a cheap and convenient solution due to the relatively long time the car is parked there. WPC fills a critical gap in EV charging infrastructure needs by extending electric miles and building range confidence. This chapter reports on the social practice of using one of the WPC facilities in the UK. It investigates the use and usability problems that are faced (n = 12) by EV users at workplace environment in one of the UK public sector employer

The social sciences and the web : From ‘Lurking’ to interdisciplinary ‘Big Data’ research

Acknowledgements This research is supported by the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub (award reference: EP/G066051/1) and the UK Economic & Social Research Council (ESRC) (award reference: ES/M001628/1).Peer reviewedPublisher PD