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Research on mutual cooperation among scholars or research institutions has become more and more common. The purpose of this paper is to explore the current status of cooperation between scholars and research institutions in the field of Chinese education. In this paper, we use the method of the complex network to analyze the cooperative behavior of academic papers published by Chinese educational scholars by collecting academic papers on education leadership, education policy, quality education, and vocational education. Our conclusions show that most of the academic papers published by Chinese educational scholars are non-cooperative. In the authors of the co-authored papers, there is a significant "Matthew effect", that is, some key scholars in these fields that link the collaborators. Lastly, there is no obvious aggregation effect between the authors of the co-authored papers which indicating a widespread and extensive connection between the collaborators. The above conclusions provide valuable insights into our understanding of the cooperative behavior of Chinese education scholars.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

The present study analyzes the offshoring network constructed from the information contained in the Panama Papers, characterizing worldwide regions and countries as well as their intra- and inter-relationships. The Panama Papers 2016 divulgence is the largest leak of offshoring and tax avoidance documentation. The document leak, with a volume content of approximately 2.6 terabytes, involves more than two hundred thousand enterprises in more than two hundred countries. From this information, the offshore connections of individuals and companies are constructed and aggregated using their countries of origin. The top offshore financial regions and countries of the network are identified, and their intra- and inter-relationship are mapped and described. We are able to identify the top countries in the offshoring network and characterize their connectivity structure, discovering the more prominent actors in the worldwide offshoring scenario and their range of influence.

Abstract Background To sustain the critical progress made, prioritization and a multidisciplinary approach to malaria research remain important to the national malaria control program in Benin. To document the structure of the malaria collaborative research in Benin, we analyze authorship of the scientific documents published on malaria from Benin. Methods We collected bibliographic data from the Web Of Science on malaria research in Benin from January 1996 to December 2016. From the collected data, a mulitigraph co-authorship network with authors representing vertices was generated. An edge was drawn between two authors when they co-author a paper. We computed vertex degree, betweenness, closeness, and eigenvectors among others to identify prolific authors. We further assess the weak points and how information flow in the network. Finally, we perform a hierarchical clustering analysis, and Monte-Carlo simulations. Results Overall, 427 publications were included in this study. The generated network contained 1792 authors and 116,388 parallel edges which converted in a weighted graph of 1792 vertices and 95,787 edges. Our results suggested that prolific authors with higher degrees tend to collaborate more. The hierarchical clustering revealed 23 clusters, seven of which form a giant component containing 94% of all the vertices in the network. This giant component has all the characteristics of a small-world network with a small shortest path distance between pairs of three, a diameter of 10 and a high clustering coefficient of 0.964. However, Monte-Carlo simulations suggested our observed network is an unusual type of small-world network. Sixteen vertices were identified as weak articulation points within the network. Conclusion The malaria research collaboration network in Benin is a complex network that seems to display the characteristics of a small-world network. This research reveals the presence of closed research groups where collaborative research likely happens only between members. Interdisciplinary collaboration tends to occur at higher levels between prolific researchers. Continuously supporting, stabilizing the identified key brokers and most productive authors in the Malaria research collaborative network is an urgent need in Benin. It will foster the malaria research network and ensure the promotion of junior scientists in the field.

Massive open online courses (MOOCs) have also received interest from researchers worldwide; however, there was no comprehensive review of the MOOC research. This paper aims to identify the MOOCs research scientific landscape as the trend from publications worldwide. In assessing research trends, the bibliometric network analysis using distance-based network mapping in VOSviewer was applied in this review. The 3,211 eligible articles published between 2011 and 2020 confirmed three main research clusters: learning system, human characteristics and higher education clusters. The results also showed that terms, such as 'learning systems', 'gender differences' and 'flipped classroom' emerged as ongoing research trends. In addition, these findings indicated that the overall productivity rates in the Middle East and Gulf regions were low. Besides, the authorship mapping indicated an absence of the small-world properties. A discussion of the findings and directions for further research are also provided. Based on the network analysis method, this paper presents the researchers' alternative method to review literature using an approach that possibly includes mostly published articles related to MOOCs.

Aim: A well-known method for quantitatively evaluating scholarly work is bibliometric analysis. Best-cited papers raise awareness of the influential publications and patterns in the literature on a specific subject. The aim was to conduct bibliometric analysis to determine most cited articles on vesiculobullous oral lesions. This is the first study on citation analysis with respect to vesiculobullous oral lesions. Materials and Methods: A retrospective data search was explored on December 2022 using the Scopus database. The articles were evaluated, and fundamental data for bibliometric analysis was reviewed. Standard details about the author, linked organizations, publishing year, and place of origin were noted. Statistical analysis was performed using Chi-square analysis. VOSviewer software was used to determine the bibliometric network analysis for co-occurrence among coauthors and commonly used keywords. Results: A total of 344 articles published from 1971 to 2022 were included in the study. A total of 6680 citations and 19.41 citations per article were observed. The journal Archives of Dermatology received the most citation. There was a significant association between the number of citations and the journal type (open access vs. non-open access) (P < 0.05). Four to five highly related clusters with the help of VOSviewer software were found during co-occurrence network analysis. Conclusions: The top 10 articles on vesiculobullous oral lesions that received the most citations were listed in detail in the present study. This will be a valuable resource for academics, clinicians, and researchers in the fields of dermatology, general pathology, oral pathology, and oral medicine. [ABSTRACT FROM AUTHOR]

Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines research on learning analytics through bibliometric, data mining, and analytics approaches. This paper argues that research interest in learning analytics is increasing steadily; some countries, higher education institutions, and researchers have a specific research agenda that indicates their intention to specialize in that field. It is also noted that there is a need for more interdisciplinary studies on learning analytics and a further need to merge technological capabilities with pedagogy. Based on the findings of text-mining and social network analysis, the following themes were identified: (1) learning analytics to improve teaching and personalize learning, (2) hegemony of data-driven teaching and learning practices, (3) multimodal learning analytics as the next generation practice, (4) learning design for learning analytics, (5) formative assessment through learning analytics, (6) learning analytics for social online learning spaces, and (7) privacy and ethical concerns to overcome. This paper suggests focusing on issues such as ethics and privacy and warns researchers to pay attention to the risks of both an educational panoptic society and quantified decision-making processes. Furthermore, rather than relying on algorithms, it is suggested to incorporate social values and center the learners in the learning analytics processes. Finally, this paper asks the following: "If we quantify the learning processes, how can we benefit from the carpe diem of educational processes and then seize the beauty of teaching and learning?"

The importance of a positive start to school has been highlighted in a range of national and international research. This has stimulated considerable ongoing research attention, as well as initiatives across policy and practice, all with the aim of promoting a positive transition to school for all children. Despite the common interests across these sectors, the links and/or relationships between and among research, policy and practice remain unclear. This article maps the potential online users of the "Transition to School: Position Statement"--a document developed collaboratively by researchers, policymakers and practitioners--and organisations whose ambit includes transition to school. Using network analysis, the authors identify the online network of stakeholders involved in the field of early childhood and the links between these, before considering how such links might influence discourse and policy formation around transition to school. The analysis highlights weak cross-sectoral links and online networks dominated by government departments and agencies. Implications of these results are explored and the potential for digital research methods in research about transition to school is considered.

This paper presents a cartography of the digital literacy academic field. Such cartography is comprised of two sections: a categorization of the field through literature review and analysis, and an exploration of its main issues through thematic and network analysis. On the one hand, five conceptual categories of digital literacies are found: functional, sociocultural, critical, transformative, and sociomaterial. On the other, main issues are described with 21 recurring themes of digital literacy and a few networks depicting its most salient matters of concern, concluding with an interpretation of these in the composition of 8 encompassing issue spaces: digital literacies conceptions and practices, digital literacy in education, access and digital divide, digital texts and literacy, websites and social networks, digital technologies at the workplace and healthcare, digital technologies users and uses, and information issues. Finally, a few paragraphs are dedicated to the limitations of categorizing and issue mapping.

I trace the bibliometric evolution of "New Public Management Is Dead" by Dunleavy et al. to investigate how the seminal paper influenced the administrative reform debate. They suggested Digital-Era Governance as the main post-NPM idea. My bibliometric analysis discovers public value, administrative reform trajectories, and digital government as influential themes. Unlike Dunleavy et al., the literature found the managerial reform wave is not linear, reform ideas are supplementary, and NPM remains a major toolkit. Future research should focus on reintegration and need-based holism, linking digital government to administrative reform, and the negative impact of digital government on democracy. [ABSTRACT FROM AUTHOR]

Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science’s diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as “inclusive society” and “early childhood development”, by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs.

In this paper we performed a brief survey of the recent literature on statistical analysis of networks. For instance, we reviewed contributions dealing with statistical properties of complex networks like the degree distribution, the clustering coefficient, and other statistical analysis techniques such as resampling, bootstrapping, randomization and so forth. We see that those statistical techniques are suitable to investigate so-called non-deterministic networks. That means, we refer to networks that cannot be inferred deterministically as in graph theory. Therefore we believe that these approaches complement classical ones meaningfully and, hence, we continue doing research in this field.

Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap. Examining online advertisements to determine the skills sought by employers of new hires for these emerging areas and ensuring that program course content addresses these skills can be a daunting task. In this paper, the authors describe the development of a data-driven analytics framework that can be used for evaluating emerging skill clusters in online job adverts and the application of the framework to a mobile computing course at the authors' institution.

In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among students was used to construct the in-class social networks and analyze the structural characteristics of them. Finally, the CRITIC and entropy weight method was introduced for obtaining the combined weight values and the GRA-TOPSIS multi-decision fusion algorithm to mine the key student nodes that have negative impact. The experiments showed that the algorithm of this paper can evaluate students objectively based on their classroom social networks, providing technical support for process-oriented comprehensive quality education evaluation.

Chagas disease is a chronic, tropical, parasitic disease, endemic throughout Latin America. The large-scale migration of populations has increased the geographic distribution of the disease and cases have been observed in many other countries around the world. To strengthen the critical mass of knowledge generated in different countries, it is essential to promote cooperative and translational research initiatives. We analyzed authorship of scientific documents on Chagas disease indexed in the Medline database from 1940 to 2009. Bibliometrics was used to analyze the evolution of collaboration patterns. A Social Network Analysis was carried out to identify the main research groups in the area by applying clustering methods. We then analyzed 13,989 papers produced by 21,350 authors. Collaboration among authors dramatically increased over the study period, reaching an average of 6.2 authors per paper in the last five-year period. Applying a threshold of collaboration of five or more papers signed in co-authorship, we identified 148 consolidated research groups made up of 1,750 authors. The Chagas disease network identified constitutes a "small world," characterized by a high degree of clustering and a notably high number of Brazilian researchers.

The term open education has recently been used to refer to topics such as Open Educational Resources (OERs) and Massive Open Online Courses (MOOCs). Historically its roots lie in civil approaches to education and open universities, but this research is rarely referenced or acknowledged in current interpretations. In this article the antecedents of the modern open educational movement are examined, as the basis for connecting the various strands of research. Using a citation analysis method the key references are extracted and their relationships mapped. This work reveals eight distinct sub-topics within the broad open education area, with relatively little overlap. The implications for this are discussed and methods of improving inter-topic research are proposed. [This paper was presented at the 2018 Open Education Consortium Global Conference, held in Delft (The Netherlands) April 24th-26th, 2018.]

O objetivo deste trabalho foi propor a utilização da análise de redes sociais (ARS) direcionada aos arranjos institucionais, possibilitando a pesquisa empírica da responsabilidade socioambiental no setor florestal. Foi realizado um estudo de caso, no qual se construiu graficamente uma rede a partir da identificação de todas as organizações atuantes em projetos ambientais através de parcerias com empresas do setor. A base de dados empregada foi o Relatório de Responsabilidade Socioambiental da Associação Brasileira de Celulose e Papel (BRACELPA, 2006). Os dados, processados pelo software UCINET, permitiram visualizar interações entre o setor privado, o setor público e o terceiro setor. A partir dessa base de dados foi possível detectar cinco subgrupos de atuação socioambiental formados pelas empresas do setor florestal e suas parceiras, além da existência de diferenças quantitativas e qualitativas entre os arranjos institucionais de cada subgrupo. Esses arranjos podem ser explorados inserindo em futuras análises outros atributos aos atores sociais pertencentes à rede. A metodologia permite a obtenção de dados estratégicos, sendo a Bracelpa possível articuladora e potencializadora da atuação socioambiental das empresas através do fortalecimento dos laços relacionais entre os subgrupos. Este trabalho abordou o conceito de redes sociais e de análise de redes para as Ciências Florestais. Em especial, os resultados apontaram caminhos para novas pesquisas sobre cultura gerencial, multiplicação de informações, dinamização das ações, sinergia com parceiros e potencial de incorporação da metodologia pelas próprias organizações do setor.The aim of this paper was to propose the utilization of a network analysis as a methodology to discuss the Social-Environmental Responsibility on forest based companies focused on the articulation among industry, governmental agencies and the outsourced sector. A case study was conducted identifying all the social participants involved in the environmental projects published in the Social-Environmental Responsibility Report of Brazilian Pulp and Paper Association (Bracelpa, 2006). Data was analyzed with UCINET software and allowed visualize interactions among private companies, public sector and the outsourced sector. Five subgroups were detected formed by the companies and their partners. There are quantitative and qualitative differences among subgroup arrangements. These arrangements can be explored inserting new attributes to the actors. The methodology allows for the obtaining strategic information for strengthening actions. Bracelpa can fortify these relations between subgroups. This paper brings the social net concept to forestry science. Specially, the results indicate the possibilities of new research involving culture management, informational multiplication, shares dynamics, partnerships and forestry companies using the methodology.

Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.

Social networking sites have become affinity spaces for teachers. Many teachers use them with different intentions and motivations, including learning. On social media platforms there are active teachers who have developed a certain leadership and recognition from many teachers. In some areas, like marketing or fashion, people with influence are called influencers. This paper investigates who they are, how their network is configured and how they perceive themselves. The questions that directed our research were: Who are the predominant Spanish teacher leaders on Twitter? What is the network structure that characterizes them? What perceptions do these teacher leaders have about their role and its impact on their professional development as teachers and others? This study has two distinct but interrelated phases. We investigated the structure and relationships among 54 Spanish teacher leaders. Using a social network analysis (SNA) approach, through the analysis of the social behavior of these teachers on the social network Twitter, we first identify educational profiles who have a high degree of centrality in the network. These are teachers who are recognized as opinion leaders by a significant proportion of their fellows. In addition to the degree of centrality that tells us how relevant a user is in a specific digital community, we identified teachers who play a key role in the circulation of information in the network studied. In some way, these teachers share common characteristics with activists in other fields. Of the 54 teachers, we selected 20 who were then interviewed. The findings demonstrate that they don't consider themselves micro-celebrities or influencers. We found a lack of identification not only with the term, but also with the image of an influencer which was understood as banal, superficial, commercial, and far from what they do in social networks. These teachers develop their identity as new digital artisans who foster a culture of collaboration and create affinity spaces that allow informal learning. Their motivation is intrinsic, through recognition and prestige among other teachers, which leads them to build a kind of constructivist leadership.

Education has been considered a cornerstone for human and economic development. Although there is a national educational strategy in most countries, various implementations are at the state level. This paper studies academic efficiency at the primary and secondary levels and the human development dimensions -- long and healthy life, being knowledgeable, and enjoying a decent standard of life -- at the state level. For this purpose, a network data envelopment analysis (NDEA) with two stages was proposed. The first stage studies the educational process efficiency, while the second evaluates its impact in the form of the human development index. The study found significant differences between the evaluated states in the education stage, where the lowest efficiencies are mainly in the southwest of Mexico. The results also indicate that better education quality leads to greater regional, economic, and social development at the state level. This study contributes to the NDEA applications on the understanding of the impact that education has in improving the development of the regions holistically.

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from "reasoning chain construction tasks." We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks.

Introduction: This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It focuses specifically on a virtual community of learning concerned with Economics education (CoL01). Method: The methods employed include descriptive statistics, content and network analysis. Analysis: We examined in detail the case of a virtual community of Economics educators using the digital archive of exchanged messages during 15 years, and focusing on: community of learning characteristics and dynamics, its density, the centrality of participants and type of knowledge shared. We compared it with three other virtual communities in the same disciplinary domain. Results: The community of learning under analysis revealed a cyclical behaviour in its development. However, the behaviour of participants (i.e. ratio of answers per topic of discussion) remains stable. Leadership is not the responsibility of a single individual. Instead it is shared by a nucleus of persons inside the community of learning. Knowledge sharing is student and teaching-oriented. Conclusions: Contrary to the literature that emphasises the importance of the role of a moderator for community interaction, leadership in debate in the studied community is not centralised in a single person or a very small group. Instead, there is a strong nucleus of members in the community of learning which is more active during the whole period under analysis and may play a brokering role that is key to maintaining the dynamics of participation. Large digital public archives proved to be useful for a deep analysis of the characteristics and dynamics of communities of learning. [This paper was published in: "Proceedings of ISIC: The Behaviour Conference" (Leeds, England, Sep 2-5, 2014), Part 2, Paper isic20.]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

To further develop rich and expressive ways of modelling roles of contributors in discussion forums of online courses, particularly in MOOCs, networks of forum users are analyzed based on the relations of information-giving and information-seeking. Specific connection patterns that appear in the information exchange networks of forum users are used to characterize user roles. Additionally, semantic roles are derived by identifying thematic areas in which an actor (learner) looks for information (problem areas) and the areas of interest in which an actor provides information to others (areas of expertise). The interplay of social and semantic roles is analyzed using a socio-semantic blockmodelling approach. The results indicate that social and semantic roles are not strongly interdependent. The methodological contribution is in combining traditional blockmodelling with semantic information to characterize participant roles. Furthermore, we use sequential pattern analysis techniques to analyze the posting activity of users over time in terms of categories of cognitive engagement. The combination of the different approaches reveals that user roles derived from the analysis of engagement patterns are strongly related to socio-semantic user roles. [Paper presented at the LAK '16--International Conference on Learning Analytics and Knowledge (6th, Edinburgh, United Kingdom, April 25-29, 2016).]

Introduction: Health information-seeking and sharing online has become immensely intertwined with day-to-day information-seeking of US immigrants with health concerns. Despite the consistent recognition of unique health needs among different US immigrant communities, little is known about the distinctive patterns and extent of health information behaviour of Korean Americans, who are one of the most rapidly growing Asian immigrant populations in the USA. Method: The study uses online discussion forums whose participants are Korean Americans living in the USA. A mixed methodology of both content and semantic network analyses was used for data analysis. Results: It was revealed that forum participants discuss two dominant topics: breast cancer tests and treatment. Although breast cancer tests was the central topic, the emphasis was on diagnosis rather than prevention. Additionally, the data indicates that participants derive psychological or emotional support from breast cancer information-seeking and sharing. Moreover, concerns stemming from insurance and financial difficulties are the biggest causes of distress for those affected by breast cancer. Conclusions: The findings from this study demonstrate the complexity of health information-seeking behaviour and the subsequent vast spectrum of concerns. Online health information behaviour involving breast cancer is often connected not only with medical practices but also with social, financial and psychological challenges as well. [Paper presented at the Information Seeking in Context (ISIC): The Information Behaviour Conference, Part 1 (11th, Zadar, Croatia, September 20-23, 2016).]

This study uses Critical Discourse Analysis and Social Network Analysis to examine an online peer mentoring site created to unite first-year and third-year preservice teachers enrolled in an undergraduate teacher education program. The peer mentoring site was developed to provide both first-year preservice teachers and more experienced peers the opportunity to discuss, share, and learn both from and with one another. The study demonstrated that the online peer mentoring site supported valuable interactions and professional communications among first-year and third-year students. In particular, the preservice teachers engaged with one another to share their experiences and learning as they prepared to be teachers. This study has implications for the field of undergraduate education in its demonstration that an online peer mentoring site can allow for the cultivation of learning, ideas and knowledge exchange, and support of students learning in informal environments. This research also has implications for future research to determine how different populations of students use an online peer mentoring site to interact with their peers and the outcomes that emerge. Such data could inform future development of peer mentoring sites and similar learning spaces. [Paper presented at the Special Interest Group on Online Teaching and Learning (SIG-OTL), American Educational Research Association (AERA) Centennial Annual Meeting (100th, Washington, D.C., April 8-12, 2016).]

Higher education institutions are facing an increasing demand to collaborate with each other in the knowledge economy. Yet, research on how higher education management enhances collaborative work is rare. This paper takes research collaboration as an example and presents a provisional exploration initiated by a higher education institution in Singapore to enhance collaborations among researchers within the institution. The paper first explores three key challenges (i.e., harnessing differences, avoiding counterproductive coercing of collaborations, and optimizing team size) and introduces a social network perspective as a means to understand research collaboration. It uses analytic tools from Social Network Analysis to provide insight into the patterns and dynamics of collaborations among researchers within the institution. The insights are used to inform the institution's formulation of strategies to enhance research collaboration, including strategies for research development, research community engagement and talent retention. Critical considerations are given to the ways in which management might adopt and adapt a social network perspective to facilitate collaborative work in higher education. Exploring the utility of social theories and tools for enhancing collaborative work in higher education contributes to 'importing' theories to higher education research, particularly the institutional management and the knowledge and research themes of higher education research.

Purpose: The purpose of this study is to reveal how research on brain-based learning (BBL) addressing adult learners' education and training issues has contributed to the overall knowledge base linking neuroscience, adult education and human resource development (HRD) research and practices. Through this comprehensive review of the BBL studies, this paper aims to expand the landscape of understanding educational phenomenon in adult education and organizational settings using the lens of neuroscience. Design/methodology/approach: Using the content analysis method, this study extracts key research themes and methodological choices from the body of BBL studies. In addition, this paper explores the relationships and proximity among key concepts of BBL research using keyword network analysis. For data analysis, this study reviews the current literature on BBL addressing both adult education and HRD topics from 1985 to 2019. Findings: The findings of this study provide a clearer picture of the potential mechanisms of BBL approaches observed in the literature of adult education and HRD. What has been found from the thematic analysis is that addressing misconceptions about the neuroscience of learning is regarded as an important topic. In terms of the methodological approaches, the literature review was a dominantly used method, whereas experimental or quantitative research has yet to be fully performed. Influential keywords and topics obtained from the keyword network analysis reveal the primary foci and structural patterns of current BBL research. Originality/value: This study makes a significant contribution to theories and research in adult education and HRD scholarship as it provides an integrative view of key research themes and major issues about BBL. Additionally, our findings offer practical insights for adult educators and HR professionals to successfully apply neuroscientific approaches.

Textbooks can have a fundamental and positive effect on both learning and teaching. As such, in order to understand how students structure concepts in their minds, it is necessary to analyze the structure in which those concepts presented in textbooks are described. This study examined a network of concepts used in the domain of biotechnology in the Iranian 12th grade biology textbook and investigated the alignment of science concepts used in its sub-domains. The findings of this study suggest that too many concepts, as well as unconnected ones, were used in biotechnology domain, which led to a complicated conceptual network. Therefore, this paper recommends that the number of biology concepts described in the later text books in the biotechnology domain at Iranian high schools should be reduced in order to facilitate a better formation of connections between concepts and their descriptions.

Like learning analytics, physics education research is a relatively young field that draws on perspectives from multiple disciplines. Network analysis has an even more heterodox perspective, with roots in mathematics, sociology, and, more recently, computer science and physics. This paper reviews how network analysis has been used in physics education research and how it connects to some of the work in this special issue. Insights from physics education research suggest combining social and interaction networks with other data sources and looking for finer-grained details to use in constructing networks, and learning analytics is promising for both avenues. The discussion ends by looking at the complications with incorporating gender into network analysis, and finally the possibilities for the future.

Connectivism, a learning theory that reveals a new learning in the Internet environment, has become a popular academic topic at the forefront of online learning. The MOOC Research Team at the Distance Education Research Centre at Beijing Normal University designed and developed the first massive open online course in China, adapting a connectivist (cMOOC) approach. Using the data collected from six offerings of the cMOOC over 3 years, the big data paradigm was used for data analysis including complex network analysis, content analysis, text mining, behaviour sequence analysis, epistemic network analysis, and statistical and econometric models. This paper summarizes the findings of the patterns of connectivist learning, including a) the basic characteristics and evolutional patterns of complex networks, b) the characteristics and modes of knowledge production, c) the patterns of instructional interactions, and d) the relationships between "pipe" (connection) and content and between facilitators and learners. It is expected that the outcome of this study could make contributions to understanding the changes of online learning in depth and further promote the theoretical development and practical application of a connectivist approach.

Lately, new materialism has been proposed as a theoretical framework to better understand material-dialogic relationships in learning, and concurrently network analysis has emerged as a method in science education research. This paper explores how to include materiality in network analysis and reports the development of a method to construct network data from video. The approaches, (1) information flow, (2) material semantic and (3) material engagement, were identified based on the literature on network analysis and new materialism in science education. The method was applied and further improved with a video segment from an upper secondary school physics lesson. The example networks from the video segment show that network analysis is a potential research method within the materialist framework and that the method allows studies into the material and dialogic relationships that emerge when students are engaged in investigations in school.

With the development of the 4th Industrial Revolution technology, the digitalization of supply chain is also receiving a lot of attention. With various technological developments, the importance of digital transformation in a firm's supply chain is increasing. Therefore, for a more comprehensive understanding of digital transformation in supply chain, we have implemented a rigorous systematic literature review by supplementing the shortcomings of existing literature and combining bibliometric analysis and network analysis with keyword network analysis. Using the SCOPUS database, we collected 182 papers addressing digital transformation in the supply chain published in renowned business administration journals, identified research trends. Then, we analyzed the connection of citations as well as the author's keywords and their changes over time. Based on these results, this study presents the number of publications and changes in papers by period, influential authors and papers, key keywords and topics, etc. on digital transformation in supply chain. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce citation networks, a novel method for identifying field connections, and to elaborate a theory of spatiality that deploys the metaphors of bubbles and foams to imagine space and change. After introducing the method and theory that guide this investigation, the author will introduce and describe the foams of the research published in the "Journal for Research in Mathematics Education" ("JRME") and "Educational Studies in Mathematics" ("ESM") during the 2010s. In doing so, the author will show what topics of inquiry constitute the dominant research foci of the field, or at least of the field as it is published in these mainstream mathematics education journals. Then, using Larnell and Bullock's (2018) socio-spatial urban framework as a lens, the author provides a critical reading of the bubbles and foams to discern what is marginalized or excluded, namely the urban. After investigating the inclusion of "urban," the author concludes by situating the role of the "Journal of Urban Mathematics Education" ("JUME") as a place to blow bubbles and reconfigure what can be seen, said, thought, and done in the name of (urban) mathematics education research.

The widespread adoption of digital e-learning environments and other learning technology has provided researchers with ready access to large quantities of data. Much of this data comes from discussion forums and has been studied with analytical methods drawn from social network analysis. However, within this large body of research there exists considerable variation in the definition of what constitutes a social tie, and the consequences of this choice are rarely described or examined. This paper presents findings from two distinct learning environments regarding different social tie extraction methods and their influence on the structural and statistical properties of the induced networks, and the association between measures of centrality and academic performance. Our findings indicate that social tie definitions play an important role in shaping the results of our analyses. The primary purpose of this paper is to raise awareness of the consequences that such methodological choices may have, and to promote transparency in future research.

In this paper, the author proposes a conceptual model with reference to the usage of Twitter as a language tool to enhance the learning of ESL/EFL. Due to the advancements in the technology and social media applications, teaching and learning of ESL/EFL have gone beyond the physical boundaries. This paper discusses the importance of microblog application-Twitter in ESL/EFL learning. The readers will find interesting ways of utilizing Twitter and its various features such as tweets, re-tweets, poll, etc for improving ESL/EFL scenarios. It will also be revealed that the ESL/EFL learning parameters derived from social network indicators -- degree, in-degree, centrality, density, and degree centralization play a critical role in evaluating the ESL/EFL learning. The participation level based on the social network analysis will affirm the improvement in the ESL/EFL learning. Finally, further research insights into the usage of the conceptual model in various settings are described.

Interdisciplinary research collaboration is needed to perform transformative science and accelerate innovation. The Science of Team Science strives to investigate, evaluate, and foster team science, including institutional policies that may promote or hinder collaborative interdisciplinary research and the resources and infrastructure needed to promote team science within and across institutions. Social network analysis (SNA) has emerged as a useful method to measure interdisciplinary science through the evaluation of several types of collaboration networks, including co-authorship networks. Likewise, research administrators are responsible for conducting rigorous evaluation of policies and initiatives. Within this paper, we present a case study using SNA to evaluate interprogrammatic collaboration (evidenced by co-authoring scientific papers) from 2007-2014 among scientists who are members of four formal research programs at an NCI-designated Cancer Center, the Markey Cancer Center (MCC) at the University of Kentucky. We evaluate change in network descriptives over time and implement separable temporal exponential-family random graph models (STERGMs) to estimate the effect of author and network variables on the tendency to form a co-authorship tie. We measure the diversity of the articles published over time (Blau's Index) to understand whether the changes in the co-authorship network are reflected in the diversity of articles published by research members. Over the 8-year period, we found increased inter-programmatic collaboration among research members as evidenced by co-authorship of published scientific papers. Over time, MCC Members collaborated more with others outside of their research program and outside their initial dense co-authorship groups, however tie formation continues to be driven by co-authoring with individuals of the same research program and academic department. Papers increased in diversity over time on all measures with the exception of author gender. This inter-programmatic research was fostered by policy changes in cancer center administration encouraging interdisciplinary research through both informal (e.g., annual retreats, seminar series) and formal (e.g., requiring investigators from more than two research programs on applications for pilot funding) means. Within this cancer center, interdisciplinary co-authorship increased over time as policies encouraging this collaboration were implemented. Yet, there is room for improvement in creating more interdisciplinary and diverse ties between research program members.

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics–by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)–using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

Social interaction between students is a crucial but under-researched part of the education realm. Understanding how connections form in university classes and their effects on learning outcomes may provide extraordinary knowledge for researchers, educators, and policy-makers. This paper collected data from the questionnaire survey and then processed them with Gephi software to produce visualization and measurement. Initial results seem to indicate a significant correlation between students' connectedness and academic performance in one class. However, in another class, the results show a contrasting situation as there is no evidence that social network attributes impact learning performance. Taken together, these results would seem to suggest that the characteristics of the network should be judged on a case-by-case basis, and large-scale SNA analyses have been rarely reported. This present study provides a springboard for a new way to shed some light on classmates' interconnection. Using a similar approach to this article, it is believed that there is ample opportunity to study the association between classmate connectedness and career success. Research techniques and approaches around Social Network Analysis are expected to evolve further in the foreseeable future.

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents. [ABSTRACT FROM AUTHOR]