QuizPower: a mobile app with app inventor and XAMPP service integration

This paper details the development of a mobile app for the Android operating system using MIT App Inventor language and development platform. The app, Quiz Power, provides students a way to study course material in an engaging and effective manner. At its current stage the app is intended strictly for use in a mobile app with App Inventor course, although it provides the facility to be adapted for other courses by simply changing the web data store. Development occurred during the spring semester of 2013. Students in the course played a vital role in providing feedback on course material, which would be the basis for the structure of the quiz as well as the questions. The significance of the project is the integration of the MIT App Inventor service with a web service implemented and managed by the department

RCSB PDB Mobile: iOS and Android mobile apps to provide data access and visualization to the RCSB Protein Data Bank.

SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org)

Mobile Device Use Among Rural, Low-Income Families and the Feasibility of an App to Encourage Preschoolers' Physical Activity: Qualitative Study.

BackgroundAs mobile devices are becoming ubiquitous, technology-based interventions provide a promising strategy to positively influence health behaviors of families with young children. However, questions remain about the feasibility and acceptability of intervention delivery via mobile apps in low-income, rural settings and among families with preschoolers.ObjectiveThe aims of this study were to understand the content and context of mobile device use for preschoolers; explore parent beliefs on this topic, including the acceptability of intervention delivery via mobile devices; and test a prototype of an app to encourage preschoolers' physical activity with both parents and children.MethodsParents (n=29) were recruited from 5 preschool centers in eastern, rural Colorado to complete a semistructured telephone interview regarding preschoolers' mobile device use. A second sample of parents (n=31) was recruited from the same preschool centers to view the app prototype independently and provide feedback. A third sample of preschool children (n=24) was videotaped using the app in small groups to measure engagement and record their responses to the app.ResultsFive key content areas emerged from the telephone interviews: (1) mobile devices are an important part of families' everyday routines, and parents have parameters governing their use; (2) parents often use mobile devices as a tool for behavior management; (3) parents clearly distinguish between mobile device use for learning versus entertainment; (4) parents have an overarching desire for balance in regard to their child's mobile device use; and (5) parents were generally supportive of the idea of using mobile apps for intervention delivery. From the app prototype testing with parents, participants reacted positively to the app and felt that it would be useful in a variety of situations. Testing with preschoolers showed the children were highly engaged with the app and a majority remained standing and/or actively moving through the entire length of the app.ConclusionsMobile devices are already integrated into most families' daily routines and appear to be an acceptable method of intervention delivery in low-income families in rural Colorado. The physical activity app represents an innovative way to reach these families and, with further improvements based on participant feedback, will provide children with a unique opportunity to practice key movement skills

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Using the Turnitin mobile app

Using the Turnitin mobile app. with iPad

Going Rogue: Mobile Research Applications and the Right to Privacy

This Article investigates whether nonsectoral state laws may serve as a viable source of privacy and security standards for mobile health research participants and other health data subjects until new federal laws are created or enforced. In particular, this Article (1) catalogues and analyzes the nonsectoral data privacy, security, and breach notification statutes of all fifty states and the District of Columbia; (2) applies these statutes to mobile-app-mediated health research conducted by independent scientists, citizen scientists, and patient researchers; and (3) proposes substantive amendments to state law that could help protect the privacy and security of all health data subjects, including mobile-app-mediated health research participants

Suggestopedic mobile language learning

The use of suggestopedic teaching methods has been shown to be effective in the domain of language learning. Suggestopaedia is a classroom teaching method that employs certain strategies to enable learners to relax in order to enable more effective learning both consciously and subconsciously. The use of mobile technologies to support language learning has also become very useful and popular. This paper proposes the amalgamation of the two approaches to enable a mobile suggestopedic platform and demonstrates empirical evidence linked to the success of this approach on languge learning. The design of a Suggestopedic mobile language learning app is discussed together with different target groups of learners that can benefit from this type of teaching. Design, development and evaluation of this app forms our future work