66 research outputs found
Mixed Reality Interfaces for Augmented Text and Speech
While technology plays a vital role in human communication, there still remain many significant challenges when using them in everyday life. Modern computing technologies, such as smartphones, offer convenient and swift access to information, facilitating tasks like reading documents or communicating with friends. However, these tools frequently lack adaptability, become distracting, consume excessive time, and impede interactions with people and contextual information. Furthermore, they often require numerous steps and significant time investment to gather pertinent information. We want to explore an efficient process of contextual information gathering for mixed reality (MR) interfaces that provide information directly in the user’s view. This approach allows for a seamless and flexible transition between language and subsequent contextual references, without disrupting the flow of communication. ’Augmented Language’ can be defined as the integration of language and communication with mixed reality to enhance, transform, or manipulate language-related aspects and various forms of linguistic augmentations (such as annotation/referencing, aiding social interactions, translation, localization, etc.). In this thesis, our broad objective is to explore mixed reality interfaces and their potential to enhance augmented language, particularly in the domains of speech and text. Our aim is to create interfaces that offer a more natural, generalizable, on-demand, and real-time experience of accessing contextually relevant information and providing adaptive interactions. To better address this broader objective, we systematically break it down to focus on two instances of augmented language. First, enhancing augmented conversation to support on-the-fly, co-located in-person conversations using embedded references. And second, enhancing digital and physical documents using MR to provide on-demand reading support in the form of different summarization techniques. To examine the effectiveness of these speech and text interfaces, we conducted two studies in which we asked the participants to evaluate our system prototype in different use cases. The exploratory usability study for the first exploration confirms that our system decreases distraction and friction in conversation compared to smartphone search while providing highly useful and relevant information. For the second project, we conducted an exploratory design workshop to identify categories of document enhancements. We later conducted a user study with a mixed-reality prototype to highlight five board themes to discuss the benefits of MR document enhancement
Teaching the Inevitable: Embracing a Pedagogy of Failure
Failure is often taken as a given in higher education, as an inevitable part of learning new things. Yet, it remains a part of learning that students tend to fear, and faculty tend to neglect. As faculty, we do not always strategize with or leverage our students’ struggles and failures for improved learning. Instead, we hope that students learn from their mistakes and study harder or try harder the next time, because moving on with material in class is necessary to meet learning objectives. In this article, we outline several strategies for using failure advantageously for promoting student growth and learning, and to minimize the stigma of struggle in academia. We make concrete suggestions and outline strategies and resources for faculty to incorporate a “pedagogy of failure” into their work with students and we describe structural barriers to using failure strategically in higher education.
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Information Technology Outlines (Professional English Basics). Підручник з англійської мови. Видання друге, перероблене і доповнене
Підручник покликаний розвивати професійно орієнтовану англомовну комунікативну компетентність через запропонований у ньому студентоцентрований підхід до навчання іноземної мови, який становить основу методики індивідуалізації викладання іноземних мов. Автори пропонують принципово нову структуру посібника, у якому кожен урок побудовано з урахуванням індивідуальних особливостей студентів. Підручник забезпечує аудиторну та самостійну роботу студентів. Для студентів галузі знань "Інформаційні технології", викладачів іноземної мови у вищих технічних навчальних закладах, та всіх, хто цікавиться проблемами викладання професійно орієнтованої англійської мови.The textbook is designed to develop professionally oriented English-language communicative competence through student-centered approach to foreign language teaching, which is the basis of the methodology of individualization of foreign language teaching. The authors propose a fundamentally new structure of the textbook, in which each lesson is built taking into account the individual characteristics of students. The textbook proposes tasks for class work and self-work of students. This book will be useful for students majoring in IT and related specialties, foreign language teachers in higher technical educational institutions, and anyone interested in teaching professional English
Models and analysis of vocal emissions for biomedical applications
This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
Designing AI-Based Systems for Qualitative Data Collection and Analysis
With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS.
Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups.
With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups
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Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence
Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach.
Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching
Text input tools’ complementarity in immersive virtual environments
This study presents a user test in order to ascertain the advantages and disadvantages of three different text input methods in immersive virtual environment: individual Speech-to-Text, collective Speech-to-Text and a virtual keyboard named Drum-Like Keyboard. We measured participants’ user experience, especially related to usability and utility, in order to offer relevant recommendations to people seeking to integrate text input in virtual reality. Our results show that Speech-to-Text and the virtual keyboard have complementary qualities, which can be used together for optimal results and experience
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