mLearning: the classroom in your pocket?

This paper reports the findings of a 1 year project which focussed solely on the potential of handheld computers for teacher professional development. The paper considers the fit between theory and practice, viewing the developing literature on mLearning as it might apply to teacher professional development, in the light of research evidence from project teachers using handheld computers. The teachers themselves used the analytical framework for teacher professional knowledge developed by Banks, Leach and Moon to consider their own experiences with the handheld computers. The study finds that handheld digital tools hold a number of pedagogic and pragmatic advantages over laptop or desktop computers for teachers, especially in rural communities; however, further technical development is required to fully orient the devices to classroom rather than office practices

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

Enforcing public data archiving policies in academic publishing: A study of ecology journals

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.Comment: 35 pages, 1 figure, 1 tabl

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe