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

Unconsciously Influential. Understanding sociotechnical Influence on social media

Over the last two decades, the rise of social media platforms such as Instagram, YouTube, and TikTok has sparked a global shift in commercial practices worldwide. People are exposed to and influenced by massive amounts of commercial content carefully and strategically integrated into these platforms’ social content. In addition, due to network structures, people’s engagement in the form of likes, comments, and simply viewing content results in the influence of people within and outside their network. In this study, we adopt a sociotechnical perspective and study the interplay between social and technical components in how influence is exercised on social media. Specifically, we identify the actors involved in the influence of commercial content and analyse how they exercise their influence for commercial purposes. Based on our findings and analysis, we present three contributions to Information systems literature: (1) how people have become unconsciously influential in spreading commercial content, which is the premise for social media commercial success, (2) how people’s social and commercial lives and contents are increasingly intertwined and (3) how this interweaving effect removes peoples’ ability to reflect on the content they engage with critically. Our study draws attention to the societal outcomes caused by technologies in practice

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Detecting hierarchical relationships and roles from online interaction networks

In social networks, analysing the explicit interactions among users can help in inferring hierarchical relationships and roles that may be implicit. In this thesis, we focus on two objectives: detecting hierarchical relationships between users and inferring the hierarchical roles of users interacting via the same online communication medium. In both cases, we show that considering the temporal dimension of interaction substantially improves the detection of relationships and roles. The first focus of this thesis is on the problem of inferring implicit relationships from interactions between users. Based on promising results obtained by standard link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce three novel time-based approaches, \Time-F" based on a defined time function, Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F, and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that takes into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer managersubordinate relationships from email exchanges, and a scientific publication coauthorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments demonstrate that time-based methods perform better in terms of recall. In particular T-RPR turns out to be superior over most recent competitor methods as well as all other approaches we propose. The second focus of this thesis is examining the online communication behaviour of users working on the same activity in order to identify the different hierarchical roles played by the users. We propose two approaches. In the first approach, supervised learning is used to train different classification algorithms. In the second approach, we address the problem as a sequence classification problem. A novel sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is a exible technique for sequence classification to be applied in different domains. We experiment on an educational dataset collected from an asynchronous communication tool used by students to accomplish an underlying group project. Our experimental findings show that the first supervised approach achieves the best mapping of students to their roles when the individual attributes of the students, information about the reply relationships among them as well as quantitative time-based features are considered. Similarly, our multi-granularity pattern-based framework shows competitive performance in detecting the students' roles. Both approaches are significantly better than the baselines considered

Culture and Social Media

博士(文学)神戸市外国語大

Composing Online: A Case Study of Embodiment, Digitality, and YouTube

This study examines YouTube channel, ContraPoints, by trans woman Natalie Wynn. It begins with close readings and analyses of an example video and body of comments from Wynn’s oeuvre that draw conclusions about how trans embodiment intersects with online, multimodal composing. The study finds that, in her video “Beauty,” Wynn’s bodily presentation and rhetorical attitudes towards dominant norms of gender and sexuality constantly shift. Furthermore, the study uncovers evidence that commenter attitudes about gender and sexuality in the video “Autogynephilia” likewise shift as a result of encounters with the video and with other commenters. Next, the study reads the YouTube video page as an assemblage composed of smaller assemblages, or modules. I discover that each of the modules relate to one another in such a way as to endow the YouTube video page assemblage with the capacities to enter social justice movements, yet the specific properties of the modules on ContraPoints video pages fail to provide the sufficient conditions to exercise this capacity. Nevertheless, the study concludes that ContraPoints video page assemblages do have the capacity to generate interpersonal, communal reflections on complex issues around gender and sexuality, reflections that may give rise to changing beliefs. These belief changes are necessary for any future community-building that may enable social justice movements aimed at expanding rights around gender and sexuality. This case study, then, offers one answer among infinite possible answers to Phil Bratta and Scott Sundvall’s question of how composers with diverse embodiments address systems of domination using digital technology. The study also suggests that assemblage theory represents a productive framework for interpreting online, multimodal compositions that incorporate large bodies of information, or big-data assemblages

Bio+Terror: Science, Security, Simulation

The United States government has spent more than $125 billion since 2001 to prepare the nation for bioterrorism. This dissertation examines the emergence of bioterrorism as a credible threat in the contemporary moment, considering how the preparedness practices of the security state constitute new biopolitical formations. To explore how changing ways of knowing disease and risk are reshaping communities, this multi-sited study investigates the material outcomes of biosecurity in people\u27s lives. It shows how complex histories of disease and terror are remade in the modern age to bring about new spaces and forms of biological citizenship.Through interview, observation and detailed historical research, this research considers three sites where bioterrorism is reshaping public life. At Montana\u27s Rocky Mountain Laboratory, the community protest of the first high-security Biosafety Level-4 facility built in the 21st century exemplifies how public fear of microbes reshapes laboratory spaces and constructs environmental geographies around new conceptions of life, risk, and disease. The creation and implementation of new biopreparedness programs at the Centers for Disease Control and Prevention in Atlanta show how the alliance of public health practices with the nation\u27s security complex brings a new level of militarism to everyday practices of health and wellness. Finally, a case study of bioterrorism simulation exercises in New Mexico considers how the public rehearsal of terrorism events creates a perpetual state of emergency as governments and citizens publicly perform their responses to a crisis.By studying the technoscientific extensions of war in the modern age, this research questions how the care-giving acts of governance have been militarized and how enlisting the bioscience industry in the War on Terror is changing societal norms of knowing life, death, nature, and disease, grounded in these re-articulations of life itself. The emerging spaces and economies of terrorism preparedness exemplify how the fusion of new genomic biologies with national security practices brings material change to the spaces where people live and work. This research aims to convince scholars as well as policymakers and activists that the ways in which bioterrorism has been produced have consequences in how people live

Echoes in Bosnia and Beyond

As the twentieth anniversary of war in Bosnia—Herzegovina looms, many civilian survivors remain traumatised by the events they experienced and/or witnessed. Following the end of the war, the ensuing social and political upheaval and lack of resources have resulted in chronic emotional issues and mental health problems within the civilian population. Ongoing help has come from a British-based charitable organization—Healing Hands Network—which, since 1996, has provided hands-on therapies in and around Sarajevo to clients referred by local organizations, including the Association of Concentration Camp Victims, the Association of Civil War Victims. Women Victims of War and Mothers of Srebrenica. Some clients have received treatments for many years and the Charity has been looking into how the current or perhaps new interventions might help these clients move on

Detecting hierarchical relationships and roles from online interaction networks

In social networks, analysing the explicit interactions among users can help in inferring hierarchical relationships and roles that may be implicit. In this thesis, we focus on two objectives: detecting hierarchical relationships between users and inferring the hierarchical roles of users interacting via the same online communication medium. In both cases, we show that considering the temporal dimension of interaction substantially improves the detection of relationships and roles. The first focus of this thesis is on the problem of inferring implicit relationships from interactions between users. Based on promising results obtained by standard link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce three novel time-based approaches, \Time-F" based on a defined time function, Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F, and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that takes into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer managersubordinate relationships from email exchanges, and a scientific publication coauthorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments demonstrate that time-based methods perform better in terms of recall. In particular T-RPR turns out to be superior over most recent competitor methods as well as all other approaches we propose. The second focus of this thesis is examining the online communication behaviour of users working on the same activity in order to identify the different hierarchical roles played by the users. We propose two approaches. In the first approach, supervised learning is used to train different classification algorithms. In the second approach, we address the problem as a sequence classification problem. A novel sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is a exible technique for sequence classification to be applied in different domains. We experiment on an educational dataset collected from an asynchronous communication tool used by students to accomplish an underlying group project. Our experimental findings show that the first supervised approach achieves the best mapping of students to their roles when the individual attributes of the students, information about the reply relationships among them as well as quantitative time-based features are considered. Similarly, our multi-granularity pattern-based framework shows competitive performance in detecting the students' roles. Both approaches are significantly better than the baselines considered