In today's globalized and technology-driven landscape, engineers wield unprecedented influence. As a response to calls from engineering accrediting and professional organizations, engineering educators have begun to further emphasize the importance of ethical decision-making within the curriculum. However, despite numerous attempts to integrate ethics, there remains a lack of consensus on effective strategies, particularly for larger-scale initiatives. This research, utilizing Lattuca and Stark's (2009) Academic Plan model, explores the Pathways curriculum reform at Virginia Tech, a university-wide initiative aimed at integrating intercultural awareness and ethical reasoning across general education courses. Through a case study methodology, semi-structured interviews were conducted with 12 faculty in the College of Engineering. Participants shared insights on the barriers encountered, resources utilized, and perceptions of ethical culture within their various academic environments. Additionally, participants described their network interactions within and beyond the curriculum reform initiative. Findings suggest faculty leverage existing networks during curriculum reform, with identified barriers categorized as influence-driven and resource-driven. Integrating these insights into the Academic Plan model offers a nuanced, process-oriented understanding of curricular change. / Doctor of Philosophy / In today's globalized and technology-driven landscape, engineers wield unprecedented influence. As a response to calls from engineering accrediting and professional organizations, engineering educators have begun to further emphasize the importance of ethical decision-making within the curriculum. However, despite numerous attempts to integrate ethics, there remains a lack of consensus on effective strategies, particularly for larger-scale initiatives. This research explores the Pathways curriculum reform at Virginia Tech, a university-wide initiative aimed at integrating intercultural awareness and ethical reasoning across general education courses. To understand faculty experiences related to the curriculum reform, interviews were conducted with 12 faculty in the College of Engineering. Participants shared insights on the barriers encountered, resources utilized, and perceptions of ethical culture within their various academic environments. Additionally, participants described their personal collaborations within and beyond the curriculum reform initiative. Findings suggest faculty leverage existing networks during curriculum reform, with identified barriers categorized as influence-driven and resource-driven. By integrating these insights into one connected framework, we might be able to better understand and navigate the barriers associated with curriculum reforms.

The aim of this study is to describe and explain the political party participation among university students. The background to the study lies in the growing problem with the political party participation in Sweden where the members in political parties has been going down for many years and seemingly continue to do so. Thats why the purpose of this study is to examine the political party participation among university students using a questionnaire survey and will be explained by using the theory ”Civic Voluntarism Modell”.  To explain the participation through CVM there is three main factors in this theory: resources, motivation and social network, who will be the factors used for the questionnaire survey and for the analysis. The material that’s been analyzed is there for the answers of the respondents in the questionnaire survey done at Mälardalens University in Västerås at three different programs: Statsvetarprogrammet, Beteendevetenskapliga Programmet and Lärarprogrammet. The result of the survey show there is a very low political party participation among university students as well, low numbers of both members in political parties and even lower numbers of active members in political parties. The result also shows that there is a connection between Civic Voluntarism Modell and the political party participation in the study, mainly regarding resources but motivation and social network as well. The study ends with a discussion of how well the connection between CVM and political party participation appears within the survey and reflects about possible flaws and struggles in the questionnaire survey.

Some classes of Internet users have specific information needs and specialized information-seeking behaviors. For example, educators who are designing a course might create a syllabus, recommend books, create lecture slides, and use tools as lecture aid. All of these resources are available online, but are scattered across a large number of websites. Collecting, linking, and presenting the disparate items related to a given course topic within a digital library will help educators in finding quality educational material. Content quality is important for users. The results of popular search engines typically fail to reflect community input regarding quality of the content. To disseminate information related to the quality of available resources, users need a common place to meet and share their experiences. Online communities can support knowledge-sharing practices (e.g., reviews, ratings). We focus on finding the information needs of educators and helping users to identify potentially useful resources within an educational digital library. This research builds upon the existing 5S digital library (DL) framework. We extend core DL services (e.g., index, search, browse) to include information from latent user groups. We propose a formal definition for the next generation of educational digital libraries. We extend one aspect of this definition to study methods that incorporate collective knowledge within the DL framework. We introduce the concept of deduced social network (DSN) - a network that uses navigation history to deduce connections that are prevalent in an educational digital library. Knowledge gained from the DSN can be used to tailor DL services so as to guide users through the vast information space of educational digital libraries. As our testing ground, we use the AlgoViz and Ensemble portals, both of which have large collections of educational resources and seek to support online communities. We developed two applications, ranking of search results and recommendation, that use the information derived from DSNs. The revised ranking system incorporates social trends into the system, whereas the recommendation system assigns users to a specific group for content recommendation. Both applications show enhanced performance when DSN-derived information is incorporated. / Ph. D.

Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of key design changes were incorporated into a second version of TopicShop based on results and user comments of the pilot study including (1) the tasks of evaluation and organization are treated as integral instead of separable, (2) spatial organization is important to users and must be well supported in the interface, and (3) distinct user and global datasets help users deal with the large quantity of information available on the web. A full empirical study using the second iteration of TopicShop covered more areas of the World Wide Web and validated results from the pilot study. Across the two studies, TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 81% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provide -- in particular, the number of pages on a site and the number of other sites that link to it -- were the key to these results, as users exploited them to identify the most promising sites quickly and easily. We also evaluated a number of link- and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 (the common information retrieval metric indicating the percentage of high quality items selected that are actually high quality) is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 32% of all documents were of high quality. Surprisingly, a simple content-based metric, which ranked documents by the total number of pages on their containing site, performed nearly as well. These studies give insight into users' needs for the task of topic management, and provide empirical evidence of the effectiveness of task-specific interfaces (such as TopicShop) for managing topical collections. / Ph. D.

Research on personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users. There it is possible to classify items involved and to personalize based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as digital libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. For this reason, more research on implicit rating data is recommended, because it is easy to obtain, suffers less from terminology issues, is more informative, and contains more user-centered information. In previous reports on my doctoral work, I discussed collecting, storing, processing, and utilizing implicit rating data of digital libraries for analysis and decision support. This dissertation presents a visualization tool, VUDM (Visual User-model Data Mining tool), utilizing implicit rating data, to demonstrate the effectiveness of implicit rating data in characterizing users, user communities, and usage trends of digital libraries. The results of user studies, performed both with typical end-users and with library experts, to test the usefulness of VUDM, support that implicit rating data is useful and can be utilized for digital library analysis software, so that both end users and experts can benefit. / Ph. D.

As we develop intelligent systems to handle online interactions and digital stories, how do we address those stories that are unwritten and invisible? How do ensure that communities who value oral histories are not left behind, and their voices also inform the design of these systems? How do we determine that the technology we design respect the agency and ownership of the stories, without imposing our own biases? To answer these questions, I rely on accounts from different underrepresented communities, as avenues to examine how digital technology affect their stories, and the agency they have over them. From these stories, I elicit guidelines for the design of equitable and resilient tools and technologies. I sought wisdom from griots who are master storytellers and story-keepers on the craft of handling both written and unwritten stories, which instructed the development of the Respectful Space for technology typology, a framework that informs our understanding and interaction with underrepresented stories. The framework guided the approach to understand technology use by inhabitants of rural spaces in the United States--particularly long-distance hikers who traverse these spaces. I further discuss the framework's extensibility, by considering its use for community self-reflection, and for researchers to query the ethical implications of their research, the technology they develop, and the consideration for the voices that the technology amplifies or suppresses. The intention is to highlight the vast resources that exist in domains we do not consider, and the importance of the underrepresented voices to also inform the future of technology. / Doctor of Philosophy / Advances in technology do not always consider how they affect group interactions, and the resulting tensions for marginal and underrepresented groups and contexts. As more technological advances focus on these contexts and communities, it is important to consider, identify, and examine these tensions and their effect on communities. We use stories from different communities as avenues for understanding technological impact, and as guides for the design of equitable and resilient tools and technologies. Stories are accessible, universal, and powerful. They guide the design of the Respectful Space for technology typology that I describe in this dissertation. Stories also allow for a combination of different areas of research: we can use Human Computer Interaction (HCI) to understand the impact of technology on human behavior, parse human language with Natural Language Processing (NLP), understand patterns in storytelling with machine learning, and leverage theories from social sciences to understand how people think, how they organize themselves, and how this translates to online spaces. I present three studies in this dissertation whose broad aims are to elicit guidelines for designing respectful technologies, and to guide our design approach for underrepresented contexts based on stories from these spaces. Using the respectful approach as a scaffold, I then give context to other research domains: informing the design of tools to amplify other communities to tell their own stories offline and online, and, more broadly, in providing spaces to query how these techniques offer key opportunities to understand other emerging and growing areas in computer science including ethics, and fairness and accountability in algorithm design.

Improving the safety of medicines management when people leave hospital is an international priority. There is evidence that poor co-ordination of medicines between providers can cause preventable harm to patients, yet there is insufficient evidence of the structure and function of the medicines management system that patients experience. This research used a mixed-methods social network analysis to determine the structure, content and function of that system as experienced by patients. Patients’ networks comprised a range of loosely connected healthcare professionals in different organisations and informal, personal contacts. Networks performed multiple functions, including health condition management, and orienting patients concerning their medicines. Some patients experienced safety incidents as a function of their networks. Staff discharging patients from hospital were also observed. Contributory factors that were found to risk the safety of patients’ discharge with medicines included active failures, individual factors and local working conditions. System defences involving staff and patients were also observed. The study identified how patients often co-ordinated a system that lacked personalisation and there is a need to provide more consistent support for patients’ self-management of medicines after they leave hospital. This could be achieved through interventions that include patients’ informal contacts in supporting their medicines use, enhancing their resilience to preventable harm, and developing and testing the role of a ‘medicines key worker’ in safely managing the transfer of care. The role of GP practices in co-ordinating the involvement of multiple professionals in patient polypharmacy needs to be further explored. / University of Bradford studentship

The penetration of social media today enables the rapid spread of breaking news and other developments to millions of people across the globe within hours. However, such pervasive use of social media by the general masses to receive and consume news is not without its attendant negative consequences as it also opens opportunities for nefarious elements to spread rumors or misinformation. A rumor generally refers to an interesting piece of information that is widely disseminated through a social network and whose credibility cannot be easily substantiated. A rumor can later turn out to be true or false or remain unverified. The spread of misinformation and fake news can lead to deleterious effects on users and society. The objective of the proposed research is to develop a range of machine learning methods that will effectively detect and characterize rumor veracity in social media. Since users are the primary protagonists on social media, analyzing the characteristics of information spread w.r.t. users can be effective for our purpose. For our first problem, we propose a method of computing user embeddings from underlying social networks. For our second problem, we propose a long short-term memory (LSTM) based model that can classify whether a story discussed in a thread can be categorized as a false, true, or unverified rumor. We demonstrate the utility of user features computed from the first problem to address the second problem. For our third problem, we propose a method that uses user profile information to detect rumor veracity. This method has the advantage of not requiring the underlying social network, which can be tedious to compute. For the last problem, we investigate a rumor mitigation technique that recommends fact-checking URLs to rumor debunkers, i.e., social network users who are very passionate about disseminating true news. Here, we incorporate the influence of other users on rumor debunkers in addition to their previous URL sharing history to recommend relevant fact-checking URLs. / Doctor of Philosophy / A rumor is generally defined as an interesting piece of a story that cannot be authenticated easily. On social networks, a user can generally find an interesting piece of news or story and may share (retweet) it. A story that initially appears plausible can later turn out to be false or remain unverified. The propagation of false rumors on social networks has a deteriorating effect on user experience. Therefore, rumor veracity detection is important, and drawing interest in social network research. In this thesis, we develop various machine learning models that detect rumor veracity. For this purpose, we exploit different types of information regarding users, such as profile details and connectivity with other users etc. Moreover, we propose a rumor mitigation technique that recommends fact-checking URLs to social network users who are passionate about debunking rumors. Here, we leverage similar techniques used in e-commerce sites for recommending products to solve this problem.

[pt] A vigilância eletrônica interpessoal consiste na busca de informações sobre o parceiro amoroso nas redes sociais e visa à obtenção de conhecimento sobre seus comportamentos off-line e/ou on-line. O objetivo deste estudo foi adaptar e validar a Escala de Vigilância Eletrônica Interpessoal para as Redes Sociais no Brasil (ISS-Brasil). A escala foi traduzida para o português brasileiro e aplicada em uma amostra de 373 participantes, que responderam a um questionário on-line. Foi verificada a estrutura empírica da escala, computadas correlações com satisfação no relacionamento, apego adulto, cyberstalking, autoestima e isolamento durante a pandemia de Covid em 2020, além de verificada sua consistência interna. Correlacionou-se o escore da escala do grupo de participantes que viviam juntos com seus parceiros amorosos, desde o auge da pandemia da Covid em 2020 até hoje e o grau de isolamento social devido à pandemia. O mesmo foi feito com o grupo dos que viviam separados. Foram testadas diferenças de média da ISS-Brasil entre parceiros amorosos que viviam juntos e separados desde o isolamento de 2020 até o momento presente. Os resultados indicaram que o modelo teve bom ajuste e consistência interna. Não houve correlação significativa da vigilância com o isolamento social. As correlações entre vigilância e cyberstalking, apego adulto ansioso e autoestima foram significativas. Participantes que moravam juntos com parceiros amorosos na pandemia apresentaram médias menores na escala ISS-Brasil do aqueles que moravam separados. Os resultados apontam que a ISS-Brasil é adequada para mensurar a vigilância eletrônica interpessoal no contexto Brasil, apresenando adequadas propriedades psicométricas. / [en] Interpersonal electronic surveillance involves seeking information about a romantic partner on social media platforms, aiming to gain insights into their offline and/or on-line behaviors. This study aimed to adapt and validate the Interpersonal Electronic Surveillance Scale for Social Media in Brazil (ISS-Brazil). The scale was translated into Brazilian Portuguese and administered to a sample of 373 participants who completed an on-line questionnaire. The empirical structure of the scale was examined, and correlations were computed with relationship satisfaction, adult attachment, cyberstalking, self-esteem, and isolation during the 2020 Covid pandemic, in addition to verifying its internal consistency. The scores on the scale were correlated within groups of participants who lived together with their romantic partners from the peak of the Covid pandemic in 2020 until the present and the degree of social isolation due to the pandemic. The same was done for the group of participants who lived separately. Mean differences in ISS-Brazil scores between romantic partners living together and apart since the 2020 isolation were tested. The results indicated that the model had a good fit and internal consistency. There was no significant correlation between surveillance and social isolation. Significant correlations were found between surveillance and cyberstalking, anxious adult attachment, and self-esteem. Participants living together with romantic partners during the pandemic had lower mean scores on the ISS-Brazil scale than those living separately. The findings suggest that the ISS-Brazil scale is suitable for measuring interpersonal electronic surveillance in the Brazilian context, presenting adequate psychometric properties.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24875号 / 理博第4985号 / 新制||理||1712(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)准教授 MacIntosh Andrew, 教授 岡本 宗裕, 教授 明里 宏文 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM