479 research outputs found

    Usability of mobile applications: literature review and rationale for a new usability model

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    The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in amobile context. This increase in usefulness has come at the expense of the usability of these devices in somecontexts. We conducted a small review of mobile usability models and found that usability is usually measured interms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend tobe overlooked in the usability models that are most prominent despite their likely impact on the success or failureof an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile ApplicationDevelopment) usability model which was designed to address the limitations of existing usability models whenapplied to mobile devices. PACMAD brings together significant attributes from different usability models inorder to create a more comprehensive model. None of the attributes that it includes are new, but the existingprominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation.We performed a literature search to compile a collection of studies that evaluate mobile applications and thenevaluated the studies using our model

    Converged Mobile Media: Evaluation of an Interactive User Experience

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    Mobility is the Message: Experiments with Mobile Media Sharing

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    This thesis explores new mobile media sharing applications by building, deploying, and studying their use. While we share media in many different ways both on the web and on mobile phones, there are few ways of sharing media with people physically near us. Studied were three designed and built systems: Push!Music, Columbus, and Portrait Catalog, as well as a fourth commercially available system โ€“ Foursquare. This thesis offers four contributions: First, it explores the design space of co-present media sharing of four test systems. Second, through user studies of these systems it reports on how these come to be used. Third, it explores new ways of conducting trials as the technical mobile landscape has changed. Last, we look at how the technical solutions demonstrate different lines of thinking from how similar solutions might look today. Through a Human-Computer Interaction methodology of design, build, and study, we look at systems through the eyes of embodied interaction and examine how the systems come to be in use. Using Goffmanโ€™s understanding of social order, we see how these mobile media sharing systems allow people to actively present themselves through these media. In turn, using McLuhanโ€™s way of understanding media, we reflect on how these new systems enable a new type of medium distinct from the web centric media, and how this relates directly to mobility. While media sharing is something that takes place everywhere in western society, it is still tied to the way media is shared through computers. Although often mobile, they do not consider the mobile settings. The systems in this thesis treat mobility as an opportunity for design. It is still left to see how this mobile media sharing will come to present itself in peopleโ€™s everyday life, and when it does, how we will come to understand it and how it will transform society as a medium distinct from those before. This thesis gives a glimpse at what this future will look like

    Brave New GES World:A Systematic Literature Review of Gestures and Referents in Gesture Elicitation Studies

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    How to determine highly effective and intuitive gesture sets for interactive systems tailored to end usersโ€™ preferences? A substantial body of knowledge is available on this topic, among which gesture elicitation studies stand out distinctively. In these studies, end users are invited to propose gestures for specific referents, which are the functions to control for an interactive system. The vast majority of gesture elicitation studies conclude with a consensus gesture set identified following a process of consensus or agreement analysis. However, the information about specific gesture sets determined for specific applications is scattered across a wide landscape of disconnected scientific publications, which poses challenges to researchers and practitioners to effectively harness this body of knowledge. To address this challenge, we conducted a systematic literature review and examined a corpus of N=267 studies encompassing a total of 187, 265 gestures elicited from 6, 659 participants for 4, 106 referents. To understand similarities in usersโ€™ gesture preferences within this extensive dataset, we analyzed a sample of 2, 304 gestures extracted from the studies identified in our literature review. Our approach consisted of (i) identifying the context of use represented by end users, devices, platforms, and gesture sensing technology, (ii) categorizing the referents, (iii) classifying the gestures elicited for those referents, and (iv) cataloging the gestures based on their representation and implementation modalities. Drawing from the findings of this review, we propose guidelines for conducting future end-user gesture elicitation studies

    Challenges in Bridging Social Semantics and Formal Semantics on the Web

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    This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

    ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋Šฅ๋ ฅ์„ ํ™•์žฅํ•˜๊ธฐ ์œ„ํ•œ ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์˜ ๋””์ž์ธ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์„œ์ง„์šฑ.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change. Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits. The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design. OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields. Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process. This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.์ผ์ƒ์˜ ๋งฅ๋ฝ์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ์œผ๋Š” ํ™œ๋™์ธ ์…€ํ”„ ํŠธ๋ž˜ํ‚น(self-tracking)์€ ๊ฐœ์ธ๊ณผ ์—ฐ๊ตฌ์˜ ์˜์—ญ์—์„œ ํ™œ๋ฐœํžˆ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์›จ์–ด๋Ÿฌ๋ธ” ๋””๋ฐ”์ด์Šค์™€ ๋ชจ๋ฐ”์ผ ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์ธํ•ด ์‚ฌ๋žŒ๋“ค์€ ๊ฐ์ž์˜ ์‚ถ์— ๋Œ€ํ•ด ๋งํ•ด์ฃผ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ๋” ์‰ฝ๊ฒŒ ์ˆ˜์ง‘ํ•˜๊ณ , ํ†ต์ฐฐํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์—ฐ๊ตฌ์ž๋“ค์€ ํ˜„์žฅ(in situ) ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๊ฑฐ๋‚˜ ์‚ฌ๋žŒ๋“ค์—๊ฒŒ ํ–‰๋™ ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ค๋Š” ๋ฐ์— ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ํ™œ์šฉํ•œ๋‹ค. ๋น„๋ก ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋„๊ตฌ๋“ค์ด ์กด์žฌํ•˜์ง€๋งŒ, ํŠธ๋ž˜ํ‚น์— ๋Œ€ํ•ด ๋‹ค์–‘ํ™”๋œ ์š”๊ตฌ์™€ ์ทจํ–ฅ์„ ์™„๋ฒฝํžˆ ์ถฉ์กฑํ•˜๋Š” ๊ฒƒ๋“ค์„ ์ฐพ๋Š” ๊ฒƒ์€ ์‰ฝ์ง€ ์•Š๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๋„๊ตฌ๋Š” ์ด๋ฏธ ์„ค๊ณ„๋œ ๋ถ€๋ถ„์„ ์ˆ˜์ •ํ•˜๊ฑฐ๋‚˜ ํ™•์žฅํ•˜๊ธฐ์— ์ œํ•œ์ ์ด๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค์˜ ์…€ํ”„ ํŠธ๋ž˜ํ‚น์— ๋Œ€ํ•œ ์ž์œ ๋„๋Š” ๊ธฐ์กด ๋„๊ตฌ๋“ค์˜ ๋””์ž์ธ ๊ณต๊ฐ„์— ์˜ํ•ด ์ œ์•ฝ์„ ๋ฐ›์„ ์ˆ˜๋ฐ–์— ์—†๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, ํ˜„์žฅ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ์—ฐ๊ตฌ์ž๋“ค๋„ ์ด๋Ÿฌํ•œ ๋„๊ตฌ์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ์—ฌ๋Ÿฌ ๋ฌธ์ œ์— ๋ด‰์ฐฉํ•œ๋‹ค. ์—ฐ๊ตฌ์ž๋“ค์ด ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ๋‹ตํ•˜๊ณ ์ž ํ•˜๋Š” ์—ฐ๊ตฌ ์งˆ๋ฌธ(research question)์€ ๋ถ„์•ผ๊ฐ€ ๋ฐœ์ „ํ• ์ˆ˜๋ก ์„ธ๋ถ„๋˜๊ณ , ์น˜๋ฐ€ํ•ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ณต์žกํ•˜๊ณ  ๊ณ ์œ ํ•œ ์‹คํ—˜ ์„ค๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์กดํ•˜๋Š” ์—ฐ๊ตฌ์šฉ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ํ”Œ๋žซํผ๋“ค์€ ์ด์— ๋ถ€ํ•ฉํ•˜๋Š” ์ž์œ ๋„๋ฅผ ๋ฐœํœ˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ„๊ทน์œผ๋กœ ์ธํ•ด ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์ด ๊ฐ์ž์˜ ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์—ฐ๊ตฌ์— ํ•„์š”ํ•œ ๋””์ง€ํ„ธ ๋„๊ตฌ๋“ค์„ ์ง์ ‘ ๊ตฌํ˜„ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ž์œ ๋„ ๋†’์€---์—ฐ๊ตฌ์  ๋งฅ๋ฝ๊ณผ ๊ฐœ์ธ์  ๋งฅ๋ฝ์„ ์•„์šฐ๋ฅด๋Š” ๋‹ค์–‘ํ•œ ์ƒํ™ฉ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š”---์…€ํ”„ ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์„ ๋””์ž์ธํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ๊ณ ์—์„œ๋Š” ์˜ด๋‹ˆํŠธ๋ž™(OmniTrack)์ด๋ผ๋Š” ๋””์ž์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™์€ ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋ก ์ด๋ฉฐ, ๋ฐ˜์ž๋™ ํŠธ๋ž˜ํ‚น(semi-automated tracking)์ด๋ผ๋Š” ์ปจ์…‰์„ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜๋™ ๋ฐฉ์‹๊ณผ ์ž๋™ ๋ฐฉ์‹์˜ ์กฐํ•ฉ์„ ํ†ตํ•ด ์ž„์˜์˜ ํŠธ๋ž˜์ปค๋ฅผ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋จผ์ € ์˜ด๋‹ˆํŠธ๋ž™์„ ๊ฐœ์ธ์„ ์œ„ํ•œ ๋ชจ๋ฐ”์ผ ์•ฑ ํ˜•ํƒœ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™ ์•ฑ์€ ๊ฐœ๊ฐœ์ธ์ด ์ž์‹ ์˜ ํŠธ๋ž˜ํ‚น ๋‹ˆ์ฆˆ์— ๋งž๋Š” ํŠธ๋ž˜์ปค๋ฅผ ์ปค์Šคํ„ฐ๋งˆ์ด์ง•ํ•˜์—ฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์‚ฌ๋žŒ๋“ค์ด ์–ด๋–ป๊ฒŒ ์˜ด๋‹ˆํŠธ๋ž™์„ ์ž์‹ ์˜ ๋‹ˆ์ฆˆ์— ๋งž๊ฒŒ ํ™œ์šฉํ•˜๋Š”์ง€ ์•Œ์•„๋ณด๊ณ ์ž ์‚ฌ์šฉ์„ฑ ํ…Œ์ŠคํŠธ(usability testing)์™€ ํ•„๋“œ ๋ฐฐํฌ ์—ฐ๊ตฌ(field deployment study)๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฐธ๊ฐ€์ž๋“ค์€ ์˜ด๋‹ˆํŠธ๋ž™์„ ํ™œ๋ฐœํžˆ ์ด์šฉํ•ด ๋‹ค์–‘ํ•œ ๋””์ž์ธ์˜ ํŠธ๋ž˜์ปคโ€”์•„์ฃผ ๋‹จ์ˆœํ•œ ๊ฐ์ • ํŠธ๋ž˜์ปค๋ถ€ํ„ฐ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ํ•„๋“œ๋ฅผ ๊ฐ€์ง„ ๋ณต์žกํ•œ ์ผ์ผ ํ™œ๋™ ํŠธ๋ž˜์ปค๊นŒ์ง€โ€”๋“ค์„ ์ƒ์„ฑํ•˜๊ณ , ์ˆ˜์ •ํ•˜๊ณ , ํ™œ์šฉํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์˜ด๋‹ˆํŠธ๋ž™์„ ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์—ฐ๊ตฌ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์—ฐ๊ตฌ ํ”Œ๋žซํผ ํ˜•ํƒœ์˜ '์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท(OmniTrack Research Kit)'์œผ๋กœ ํ™•์žฅํ•˜์˜€๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท์€ ์—ฐ๊ตฌ์ž๋“ค์ด ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ์—†์ด ์›ํ•˜๋Š” ์‹คํ—˜์„ ์„ค๊ณ„ํ•˜๊ณ  ์˜ด๋‹ˆํŠธ๋ž™ ์•ฑ์„ ์ฐธ๊ฐ€์ž๋“ค์˜ ์Šค๋งˆํŠธํฐ์œผ๋กœ ๋ฐฐํฌํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท์„ ์ด์šฉํ•ด ์ผ์ง€๊ธฐ๋ก ์—ฐ๊ตฌ(diary study)๋ฅผ ์ง์ ‘ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์˜ด๋‹ˆํŠธ๋ž™ ์ ‘๊ทผ๋ฒ•์ด ์–ด๋–ป๊ฒŒ ์—ฐ๊ตฌ์ž๋“ค์˜ ์—ฐ๊ตฌ ๋ชฉ์ ์„ ์ด๋ฃจ๋Š” ๋ฐ์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š”์ง€ ์ง์ ‘ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํœด๋จผ-์ปดํ“จํ„ฐ ์ธํ„ฐ๋ž™์…˜(Human-Computer Interaction) ๋ฐ ์œ ๋น„์ฟผํ„ฐ์Šค ์ปดํ“จํŒ…(Ubiquitous Computing) ๋ถ„์•ผ์— ๊ธฐ์ˆ ์  ์‚ฐ์ถœ๋ฌผ๋กœ์จ ๊ธฐ์—ฌํ•˜๋ฉฐ, ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๋„๊ตฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ๊ฐœ์ธ๊ณผ ์—ฐ๊ตฌ์ž๋“ค์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š”์ง€ ์‹ค์ฆ์ ์ธ ์ดํ•ด๋ฅผ ์ฆ์ง„ํ•œ๋‹ค. ๋˜ํ•œ, ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ๋””์ž์ธ์  ๋‚œ์ œ, ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•œ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ, ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃจ์ง€ ๋ชปํ•œ ๋‹ค๋ฅธ ์ง‘๋‹จ์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•œ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋…ผ์ œ์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค.Abstract CHAPTER 1. Introduction 1.1 Background and Motivation 1.2 Research Questions and Approaches 1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated Tracking 1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts 1.2.3 Designing a Research Platform for In Situ Data Collection Studies Leveraging OmniTrack 1.2.4 A Case Study of Conducting an In Situ Data Collection Study using the Research Platform 1.3 Contributions 1.4 Structure of this Dissertation CHAPTER 2. Related Work 2.1 Background on Self-Tracking 2.1.1 Self-Tracking in Personal Tracking Contexts 2.1.2 Utilization of Self-Tracking in Other Contexts 2.2 Barriers Caused by Limited Tool Support 2.2.1 Limited Tools and Siloed Data in Personal Tracking 2.2.2 Challenges of the Instrumentation for In Situ Data Collection 2.3 Flexible Self-Tracking Approaches 2.3.1 Appropriation of Generic Tools 2.3.2 Universal Tracking Systems for Individuals 2.3.3 Research Frameworks for In Situ Data Collection 2.4 Grounding Design Approach: Semi-Automated Tracking 2.5 Summary of Related Work CHAPTER3 DesigningOmniTrack: a Flexible Self-Tracking Approach 3.1 Design Goals and Rationales 3.2 System Design and User Interfaces 3.2.1 Trackers: Enabling Flexible Data Inputs 3.2.2 Services: Integrating External Trackers and Other Services 3.2.3 Triggers: Retrieving Values Automatically 3.2.4 Streamlining Tracking and Lowering the User Burden 3.2.5 Visualization and Feedback 3.3 OmniTrack Use Cases 3.3.1 Tracker 1: Beer Tracker 3.3.2 Tracker 2: SleepTight++ 3.3.3 Tracker 3: Comparison of Automated Trackers 3.4 Summary CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack 4.1 Usability Study 4.1.1 Participants 4.1.2 Procedure and Study Setup 4.1.3 Tasks 4.1.4 Results and Discussion 4.1.5 Improvements A_er the Usability Study 4.2 Field Deployment Study 4.2.1 Study Setup 4.2.2 Participants 4.2.3 Data Analysis and Results 4.2.4 Reflections on the Deployment Study 4.3 Discussion 4.3.1 Expanding the Design Space for Self-Tracking 4.3.2 Leveraging Other Building Blocks of Self-Tracking 4.3.3 Sharing Trackers with Other People 4.3.4 Studying with a Broader Audience 4.4 Summary CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection Studies 5.1 Design Space of Study Instrumentation for In-Situ Data Collection 5.1.1 Experiment-Level Dimensions 5.1.2 Condition-Level Dimensions 5.1.3 Tracker-Level Dimensions 5.1.4 Reminder/Trigger-Level Dimensions 5.1.5 Extending OmniTrack to Cover the Design Space 5.2 Design Goals and Rationales 5.3 System Design and User Interfaces 5.3.1 Experiment Management and Collaboration 5.3.2 Experiment-level Configurations 5.3.3 A Participants Protocol for Joining the Experiment 5.3.4 Implementation 5.4 Replicated Study Examples 5.4.1 Example A: Revisiting the Deployment Study of OmniTrack 5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food Logger 5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement on Habit Formation 5.4.4 Example D: Collecting Stress and Activity Data for Building a Prediction Model 5.5 Discussion 5.5.1 Supporting Multiphase Experimental Design 5.5.2 Serving as Testbeds for Self-Tracking Interventions 5.5.3 Exploiting the Interaction Logs 5.6 Summary CHAPTER 6. Using the OmniTrack Research Kit: A Case Study 6.1 Study Background and Motivation 6.2 OmniTrack Configuration for Study Instruments 6.3 Participants 6.4 Study Procedure 6.5 Dataset and Analysis 6.6 Study Result 6.6.1 Diary Entries 6.6.2 Aspects of Productivity Evaluation 6.6.3 Productive Activities 6.7 Experimenter Experience of OmniTrack 6.8 Participant Experience of OmniTrack 6.9 Implications 6.9.1 Visualization Support for Progressive, Preliminary Analysis of Collected Data 6.9.2 Inspection to Prevent Misconfiguration 6.9.3 Providing More Alternative Methods to Capture Data 6.10 Summary CHAPTER 7. Discussion 7.1 Lessons Learned 7.2 Design Challenges and Implications 7.2.1 Making the Flexibility Learnable 7.2.2 Additive vs. Subtractive Design for Flexibility 7.3 Future Opportunities for Improvement 7.3.1 Utilizing External Information and Contexts 7.3.2 Providing Flexible Visual Feedback 7.4 Expanding Audiences of OmniTrack 7.4.1 Supporting Clinical Contexts 7.4.2 Supporting Self-Experimenters 7.5 Limitations CHAPTER 8. Conclusion 8.1 Summary of the Approaches 8.2 Summary of Contributions 8.2.1 Artifact Contributions 8.2.2 Empirical Research Contributions 8.3 Future Work 8.3.1 Understanding the Long-term E_ect of OmniTrack 8.3.2 Utilizing External Information and Contexts 8.3.3 Extending the Input Modality to Lower the Capture Burden 8.3.4 Customizable Visual Feedback 8.3.5 Community-Driven Tracker Sharing 8.3.6 Supporting Multiphase Study Design 8.4 Final Remarks APPENDIX A. Study Material for Evaluations of the OmniTrack App A.1 Task Instructions for Usability Study A.2 The SUS (System Usability Scale) Questionnaire A.3 Screening Questionnaire for Deployment Study A.4 Exit Interview Guide for Deployment Study A.5 Deployment Participant Information APPENDIX B Study Material for Productivity Diary Study B.1 Recruitment Screening Questionnaire B.2 Exit Interview Guide Abstract (Korean)Docto

    Exploring mobile learning opportunities and challenges in Nepal: the potential of open-source platforms

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    With the increasing access to mobile devices in developing countries, the number of pilots and projects embracing mobile devices as learning tools is also growing. The important role it can play in improving education is also positively received within education communities. But, providing a successful mobile learning service is still significantly challenging. The considerable problems arise due to existing pedagogical, technological, political, social and cultural challenges and there has been a shortage of research concerning how to deploy and sustain this technology in a resource constrained educational environment. There are studies mainly conducted in sub-Saharan countries, India, and Latin America, which provide some guidelines for incorporating technology in the existing educational process. However, considering the contextual differences between these regions and other countries in Asia, such as Nepal, it requires a broader study in its own challenging socio-cultural context. In response to this difficulty, the aims of this exploratory research work are to study the distinct challenges of schoolsโ€™ education in Nepal and evaluate the use of open-source devices to provide offline access to learning materials in order to recommend a sustainable mobile learning model. The developmental study was conducted in University of West London in order to assess the feasibility of these devices. The main study in Nepal explored i) the overall challenges to education in the challenging learning environment of schools with limited or no access to ICT, ii) how ICT might be helping teaching and learning in the rural public schools, and iii) how an offline mobile learning solution based on the open source platforms may facilitate English language teaching and learning. Data collection primarily involved interviews, questionnaires, observations and supplemented by other methods. This thesis presents the sustainable model for deploying and supporting mobile technology for education, which is based on the findings emerging from completed exploratory studies in Nepal. It highlights all the aspects that need to be addressed to ensure sustainability. However, to translate this understanding to a design is a complex challenge. For a mobile learning solution to be used in such challenging learning contexts, the need is to develop simple and innovative solutions that provide access to relevant digital learning resources and train teachers to embed technology in education. This thesis discusses these findings, limitations and presents implications for the design of future mobile learning in the context of Nepal

    Personal Artifact Ecologies in the Context of Mobile Knowledge Workers

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    Recent work suggests that technological devices and their use cannot be understood in isolation, and must be viewed as part of an artifact ecology. With the proliferation of information and communication technologies (ICTs), studying artifact ecologies is essential in order to design new technologies with effective affordances. This paper extends the discourse on artifact ecologies by examining how such ecologies are constructed in the context of mobile knowledge work, as sociotechnical arrangements that consist of technological, contextual, and interpretive layers. Findings highlight the diversity of ICTs that are adopted to support mobile work practices, and effects of individual preferences and contextual factors (norms of collaboration, spatial mobility, and organizational constraints)
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