978 resultados para mobile apps


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Background The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond “star”-ratings. Objective The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. Methods A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. Results There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). Conclusions The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.

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The increasing importance of employability in Higher Education curricula and the prevalence of using mobile devices for fieldbased learning prompted an investigation into student awareness of the relationship between the use of mobile apps for learning and the development of graduate attributes (GAs) (and the link to employability). The results from post-fieldwork focus groups from four field courses indicated that students could make clear links between the use of a variety of mobile apps and graduate attribute development. The study suggests a number of mobile apps can align simultaneously with more than one graduate attribute. Furthermore, prior experience and the context of use can influence students’ perceptions of an app and its link with different GAs.

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It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

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A new definition of m-learning has been presented in the first chapter. It was modified from a definition presented by Sharples, Taylor, and Vavoula in 2007. In Chapter 1, Crompton, Muilenburg, and Berge defined m-learning as "learning across multiple contexts, through social and content interactions, using personal electronic devices" (Crompton, 2013, p. 4). This chapter departs from this definition and focuses on mobile applications (apps) and technologies in this context.

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Mobile applications (in short: mobile apps) are software applications designed to run on mobile devices such as smart phones and tablet computers. These technologies have come a long way since the opening of the Apple App Store in 2008, especially in their applications for business, information, communication, health and gaming. Most mobile devices are capable of processing and disseminating information in an efficient way. Smart devices, such as the iPhone or iPad, are equipped with innovative functionalities and unique features for easy delivery of content, information sharing, and collaborative work over the Internet. This is the basis for the use of these technologies in the education industry.

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Mobile smart phones and Mobile applications (apps) can be seen as an added opportunity for education and for teachers in particular, this can be true if educational institutions are willing to enable this type of delivery mechanism. With the recent development of app technologies in today’s society and the shift towards mobile technologies (devices and applications), it seems hard to imagine better devices and tools that can grant students easy and on demand access to learning content. These smart devices and technologies are multitasking and can offer variety of educational apps that teachers and students are able to integrate into their daily learning activities and can carry with them anywhere. In this paper mobile apps and their integration in education are discussed, a case study on the use of three mobile apps (e-Lecture-Producer, Dropbox and QR Code) in an educational setting in a Business e-commerce course is described. Results and outcomes are discussed based on the results from the case study.

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Social networks offer horizontal integration for any mobile platform providing app users with a convenient single sign-on point. Nonetheless, there are growing privacy concerns regarding its use. These vulnerabilities trigger alarm among app developers who fight for their user base: While they are happy to act on users’ information collected via social networks, they are not always willing to sacrifice their adoption rate for this goal. So far, understanding of this trade-off has remained ambiguous. To fill this gap, we employ a discrete choice experiment to explore the role of Facebook Login and investigate the impact of accompanying requests for different information items / actions in the mobile app adoption process. We quantify users’ concerns regarding these items in monetary terms. Beyond hands-on insights for providers, our study contributes to the theoretical discourse on the value of privacy in the growing world of Social Media and mobile web.

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The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

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This paper demonstrates a multi-view framework for Rapid APPlication Tool (RAPPT). RAPPT enables rapid development of mobile applications. It employs a multilevel approach to mobile application development: a Domain Specific Visual Language to define the high level structure of mobile apps, a Domain Specific Textual Language to define behavioural concepts, and concrete source code for fine grained improvements.

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Emergencies and disasters are different types of crisis events which can affect students and staff in their on-campus roles and off-campus activities. In such events, mobile technologies, mobile software applications (apps), and mobile social networks are becoming increasingly relied upon to communicate, to swiftly send and received information and images, to deliver learning moments, and to check the safety of colleagues and friends. This paper investigates the intersection of mlearning, mobile social media, mobile apps, and crisis informatics in times of emergencies and disasters, using the recent Gipplsand earthquake in south-eastern Australia as an exemplar. It also discusses proactive preparation for educational resilience during emergencies and disasters.

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In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

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