Scholarly Collaboration In Engineering Education: From Big-Data Scientometrics To User-Centered Software Design

Engineering education research has grown into a flourishing community with an-ever increasing number of publications and scholars. However, recent studies show that a significant amount of engineering education knowledge retains a clear disciplinary orientation. If the gaps in scholarly collaboration continue to be prevalent within the entire community, it will become increasingly difficult to sustain community memory. This will eventually inhibit the propagation of innovations and slow the movement of research findings into practice. This dissertation studies scholarly collaboration in the engineering education research community. It provides a clear characterization of collaboration problems and proposes potential solutions. The dissertation is composed of four studies. First, the dissertation recognizes gaps in scholarly collaboration in the engineering education research community. To achieve this goal, a bibliometric analysis based on 24,172 academic articles was performed to describe the anatomy of collaboration patterns. Second, the dissertation reviewed existing technologies that enhance communication and collaboration in engineering and science. This review elaborated and compared features in 12 popular social research network sites to examine how these features support scholarly communication and collaboration. Third, this dissertation attempted to understand engineering education scholars‟ behaviors and needs related to scholarly collaboration. A grounded theory study was conducted to investigate engineering education scholars‟ behaviors in developing collaboration and their technology usage. Finally, a user-centered software design was proposed as a technological solution that addressed community collaboration needs. Results show that the engineering education research community is at its early stage of forming a small world network relying primarily on a small number of key scholars in the community. Scholars‟ disciplinary background, research areas, and geographical locations are factors that affect scholarly collaboration. To facilitate scholarly communication and collaboration, social research network sites started to be adopted by scholars in various disciplines. However, engineering education scholars still prefer face-to-face interactions, emails, and phone calls for connecting and collaborating with other scholars. Instead of connecting to other scholars online, the present study shows that scholars develop new connections and maintain existing connections mainly by attending academic conferences. Some of these connections may eventually develop into collaborative relationships. Therefore, one way to increase scholarly collaboration in engineering education is to help scholars better network with others during conferences. A new mobile/web application is designed in this dissertation to meet this user need. The diffusion of innovation theory and the small world network model suggest that a well-connected community has real advantages in disseminating information quickly and broadly among its members. It allows research innovations to produce greater impacts and to reach a broader range of audiences. It can also close the gap between scholars with different disciplinary backgrounds. This dissertation contributes to enhancing community awareness of the overall collaboration status in engineering education research. It informs policy making on how to improve collaboration and helps individual scientists recognize potential collaboration opportunities. It also guides the future development of communication and collaboration tools used in engineering education research

Understanding the structure and role of academics' ego-networks on social networking sites

Academic social networking sites (SNS) seek to bring the benefits of online networking to an academic audience. Currently, the two largest sites are Academia.edu and ResearchGate. The ability to make connections to others is a defining affordance of SNS, but what are the characteristics of the network structures being facilitated by academic SNS, and how does this relate to their professional use by academics? This study addressed this question through mixed methods social network analysis. First, an online survey was conducted to gain contextual data and recruit participants (n = 528). Second, ego-networks were drawn up for a sub-sample of 55 academics (reflecting a range of job positions and disciplines). Ego-networks were sampled from an academic SNS and Twitter for each participant. Third, co-interpretive interviews were held with 18 participants, to understand the significance of the structures and how the networks were constructed. Academic SNS networks were smaller and more highly clustered; Twitter networks were larger and more diffuse. Communities within networks are more frequently defined by institutions and research interests on academic SNS, compared to research topics and personal interests on Twitter. Emerging themes link network structure to differences in how academics conceptualise and use the sites. Academic SNS are regarded as a more formal academic identity, akin to a business card, or used as a personal repository. Twitter is viewed as a space where personal and professional are mixed, similar to a conference coffee break. Academic SNS replicate existing professional connections, Twitter reinforces existing professional relationships and fosters novel connections. Several strategies underpinning academics’ use of the sites were identified, including: circumventing institutional constraints; extending academic space; finding a niche; promotion and impact; and academic freedom. These themes also provide a bridge between academic identity development online and formal academic identity and institutional roles

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

CREATe 2012-2016: Impact on society, industry and policy through research excellence and knowledge exchange

On the eve of the CREATe Festival May 2016, the Centre published this legacy report (edited by Kerry Patterson & Sukhpreet Singh with contributions from consortium researchers)

Buy, Borrow, or Steal: Patterns in Searching for Scholarly Literature

This study examines how individuals at the University of North Carolina at Chapel Hill engage in certain resource retrieval actions when searching for peer-reviewed scholarly literature. A campus-wide survey was distributed to students, faculty, and staff of various disciplines in order to collect information and opinions on specific retrieval methods. Responses suggest that resource retrieval actions are influenced by the user’s status, their discipline affiliation, and the reason for their information need. The data also shows that the majority of UNC-CH patrons do not know how to request an electronic, peer-reviewed resource through the Library system. This study shows the need for more comprehensive outreach and education regarding electronic resources, especially as the Library continues to navigate new scholarly communication environments.Master of Science in Library Scienc

Facebook as a site of stress reduction and resilience amongst trailing wives living in Alaska

2017 Spring.Includes bibliographical references.This explanatory sequential mixed methodology (Creswell & Plano Clark, 2011) study considers how Facebook use impacts sojourners' perceived stress and resilience. Forty-one current and retired sojourning "trailing wives"—women who move primarily for their husband's career—located in Anchorage, Alaska, participated in the Phase 1 survey. Phase 1 found support for the predicted negative relationship between perceived stress and Facebook social connectedness, but the predicted positive relationship between Facebook social connectedness and resilience was not significant. Seventeen Phase 2 participants participated in semi-structured interviews, which were then analyzed using the constant comparative method (Strauss & Corbin, 1990), to explore the relationship between Facebook use and resilience further. Interview participants identified Facebook information seeking and social networking activities as particularly helpful in their early sojourn adjustment. Some participants also reported using Facebook and other social media sites (e.g., Instagram) to grow from their sojourn experience by practicing four of the resilience communication processes identified by Buzzanell (2010): drawing upon communication networks, emphasizing identity anchors, fostering optimism, and reframing negative experiences. Implications for practitioners (e.g., sojourners, human resources and mental health professionals) and researchers (across international business and social science disciplines) are also discussed

Collaborating on Web 2.0 Technologies: The Best-Fit Model for the Behavioral Intentions of Preservice Teachers

Professional collaborative partnerships among teachers are essential in delivering appropriate services, in an inclusive classroom for students with disabilities. Web 2.0 technologies are new, yet largely unexamined, tools that may be used to facilitate collaborative partnerships. Teacher preparation programs are currently attempting to understand the behavioral intention of preservice teachers on these new technologies. A total of 590 preservice teachers participated in this study and reported their current use, perceived benefits, and behavioral intentions on Web 2.0 technologies. The Decomposed Theory of Planned Behavior (DTPB) was used as a theoretical framework to help guide the study and identify possible behavior intention factors. The collected data was analyzed through Structural Equation Modeling (SEM) to find a best-fit path model that would lead to the behavioral intention of preservice teachers to use Web 2.0 technologies. This study found that preservice teachers are using Web 2.0 technologies at an increasing rate in their teacher preparation programs. Preservice teachers also reported perceiving peer interaction and sharing resources as the greatest collaborative benefits of these technologies. When the combined factors of attitude, subjective norms, and perceived behavioral control were identified, preservice teachers intend to collaborate on Web 2.0 technologies as professional teachers. Teacher preparation program faculty should be encouraged to use Web 2.0 technologies in their courses, with the understanding that it will benefit the future collaboration of teachers

Computer networking among faculty members: the effects of computer networking on faculty communication and culture

Because computer networking has become an important communication medium for faculty members, this study was conducted to explore the effects of computer networking upon faculty communication and culture. Members of research universities I astronomy departments (N = 180) were surveyed to find out how computer networking affected their communication and shared disciplinary and institutional cultures;Results showed that computer networking was most used for exchange between research partners. In addition, computer networking decreased the use of the telephone and conventional mail but had no effect on other traditional media. Computer networking also increased communication between remote colleagues. The effect of computer networking on faculty culture was minimal. However, in the slight differences which did appear, high level computer networkers defined the discipline and institution with a slightly different set of constructs. Moderate level computer network users held significantly different beliefs about the actual and ideal states of the discipline and institution. High level computer networkers were the least satisfied with the current state of their institutions. The low and high level computer networking groups showed more difference between the beliefs and ideal states they hold for their discipline and institution and their beliefs of other\u27s beliefs and ideal states. The computer groups varied most on the cooperation construct and on their beliefs and ideal states of the institution

Essays on Health Information Technology: Insights from Analyses of Big Datasets

The current dissertation provides an examination of health information technology (HIT) by analyzing big datasets. It contains two separate essays focused on: (1) the evolving intellectual structure of the healthcare informatics (HI) and healthcare IT (HIT) scholarly communities, and (2) the impact of social support exchange embedded in social interactions on health promotion outcomes associated with online health community use. Overall, this dissertation extends current theories by applying a unique combination of methods (natural language processing, machine learning, social network analysis, and structural equation modeling etc.) to the analyses of primary datasets. The goal of the first study is to obtain a full understanding of the underlying dynamics of the intellectual structures of HI and its sub-discipline HIT. Using multiple statistical methods including citation and co-citation analysis, social network analysis (SNA), and latent semantic analysis (LSA), this essay shows how HIT research has emerged in IS journals and distinguished itself from the larger HI context. The research themes, intellectual leadership, cohesion of these themes and networks of researchers, and journal presence revealed in our longitudinal intellectual structure analyses foretell how, in particular, these HI and HIT fields have evolved to date and also how they could evolve in the future. Our findings identify which research streams are central (versus peripheral) and which are cohesive (as opposed to disparate). Suggestions for vibrant areas of future research emerge from our analysis. The second part of the dissertation focuses on comprehensively understanding the effect of social support exchange in online health communities on individual members’ health promotion outcomes. This study examines the effectiveness of online consumer-to-consumer social support exchange on health promotion outcomes via analyses of big health data. Based on previous research, we propose a conceptual framework which integrates social capital theory and social support theory in the context of online health communities and test it through a quantitative field study and multiple analyses of a big online health community dataset. Specifically, natural language processing and machine learning techniques are utilized to automate content analysis of digital trace data. This research not only extends current theories of social support exchange in online health communities, but also sheds light on the design and management of such communities