Gamers never play alone: An interface-centred analysis of online video gaming

Online gaming involves a complex and multidimensional set of practices. This article proposes understanding online video gaming based on an interface-centred approach that goes beyond the classic study of the “graphic user interface”. In this theoretical and analytical framework, the interface is considered the place where human, institutional and technological actors relate to each other and different processes are carried out. The article draws the data from empirical research with teens carried out in eight countries. It analyses the teenagers’ online playing experience as an interface, understood as a ‘network of actors’ that goes beyond the single video gaming device (console, PC, etc.). This work also presents a map of actors, relationships and processes of the online video gaming interface, paying particular attention to the tensions and critical issues that arise, from a perspective that, in further studies, could be expanded to other practices.

PENGUJIAN USER EXPERIENCE PADA APLIKASI SMART HOME MENGGUNAKAN USE QUESTIONNAIRE

Penelitian ini dilakukan untuk membuat desain user interface Aplikasi Smart Home berbasis android yang mudah dipahami oleh pengguna untuk mengontrol sistem dan menguji User Experience terhadap desain aplikasi smart home. Metode dalam mengumpulkan data yang digunakan ialah studi pustaka, angket, dan pengamatan (observasi). Dalam metode pengembangan sistem menggunakan Linear Sequential Model. Aplikasi yang digunakan dalam pembuatan desain dan aplikasi smart home antara lain Android Studio, Sublime, Xampp, Adobe Xd, dan Adobe Illustrator. Metode pengujian yang di gunakan antara lain Usability testing dan Pengukuran Usability menggunakan USE Questionnaire. Dalam penelitian ini menghasilkan sebuah desain Aplikasi Smart Home yang dapat digunakan pengguna dengan mudah dalam mengontrol sistem.

Redesign the User Interface of the Purwakarta Disdukcapil Mobile Application Using Kansei Engineering

The function and usability of a mobile application are the main reasons for making the application. In addition, the User Interface (UI) design factor is also an important consideration in making a mobile application. Good UI design is the main attraction for the application to use. There are many ways to make a good UI design. Kansei Engineering (KE) is one of the methods that can be used in the UI design process. Since the creation of the Mobile Disdukcapil application, there has never been a study on the application's design interface. This research aims to make recommendations on design elements desired by users. The KE method can detect the user's feelings towards an interface. So that the KE method will produce a UI design for the Disdukcapil mobile application that is liked by the user. The methodology used refers to the Kansei Engineering Type I methodology. This research uses Kansei Words to represent the emotional factors of the user when viewing a product specimen. Kansei Word used as many as 10 words related to the UI display on the mobile application. The mobile application specimens used were 5 specimens, which were taken from various similar applications. This study involved 80 participants to fill out the questionnaire. The results of the questionnaire were processed using multivariate statistical analysis, namely Cronbach's Alpha, Coefficient Correlation Analysis (CCA), Principal Component Analysis (PCA), Factor Analysis (FA), and Partial Least Square (PLS). The results of this study are in the form of recommendations for UI design elements based on the most dominant emotional factors. Based on the results of data processing, the dominant emotional factors are "Colorful" and "Simple".

Navigation by Revealing Trade-offs for Content-Based Recommendations

Conversational recommender systems have been introduced to provide users the opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. Tourism is a complex domain for recommender systems because of high cost of recommending a wrong item and often relatively few ratings to learn user preferences. In a scenario such as recommending a city to visit, conversational content-based recommendation may be advantageous, since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface which focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and find that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians’ decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool’s user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Why-type Question to Query Reformulation for efficient Document Retrieval

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.