Browsing by Subject "apps"

Now showing 1 - 10 of 11
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    Beyond Words in Word: Tablets and the New World of Writing
    (2012-02) Lamb, Annette; Johnson, Larry
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    Disparity between the Programmatic Views and the User Perceptions of Mobile Apps
    (IEEE, 2017-12) Chowdhury, Nahida Sultana; Raje, Rajeev R.; Computer and Information Science, School of Science
    User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty - B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives.
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    An Eclectic Mix for Educators and Students
    (2014-12) Lamb, Annette
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    A Holistic Ranking Scheme for Apps
    (IEEE, 2018-12) Chowdhury, Nahida Sultana; Raje, Rajeev R.; Computer and Information Science, School of Science
    App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view.
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    Nurturing a New Breed of Reader: Five Real-world Issues
    (2011-10) Lamb, Annette; Johnson, Larry
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    Resources and Tools for the Classroom
    (2015-02) Lamb, Annette
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    Resources and Tools for the Classroom
    (2015-04) Lamb, Annette
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    Resources for the Library, Classroom, and Beyond
    (2015-06) Lamb, Annette