A Review of Theory and Practice in Scientometrics

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the “laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments

Methodologies for designing healthcare analytics solutions: a literature analysis

© The Author(s) 2019. Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field

A Bibliometric Study on Learning Analytics

Learning analytics tools and techniques are continually developed and published in scholarly discourse. This study aims at examining the intellectual structure of the Learning Analytics domain by collecting and analyzing empirical articles on Learning Analytics for the period of 2004-2018. First, bibliometric analysis and citation analyses of 2730 documents from Scopus identified the top authors, key research affiliations, leading publication sources (journals and conferences), and research themes of the learning analytics domain. Second, Domain Analysis (DA) techniques were used to investigate the intellectual structures of learning analytics research, publication, organization, and communication (Hjørland & Bourdieu 2014). The software of VOSviewer is used to analyze the relationship by publication: historical and institutional; author and institutional relationships and the dissemination of Learning Analytics knowledge. The results of this study showed that Learning Analytics had captured the attention of the global community. The United States, Spain, and the United Kingdom are among the leading countries contributing to the dissemination of learning analytics knowledge. The leading publication sources are ACM International Conference Proceeding Series, and Lecture Notes in Computer Science. The intellectual structures of the learning analytics domain are presented in this study the LA research taxonomy can be re-used by teachers, administrators, and other stakeholders to support the teaching and learning environments in a higher education institution

The Multi-Dimensional Research Agendas Inventory – Revised (MDRAI-R): factors shaping researchers’ research agendas in all fields of knowledge

This study creates a novel inventory that characterizes factors influencing the research agendas of researchers in all fields of knowledge: the Multi-dimensional Research Agendas Inventory-Revised (MDRAI-R). The MDRAI-R optimizes an initial inventory designed for the social sciences (the MDRAI) by reducing the number of items per dimension, improving the inventory’s psychometric properties, and including new dimensions (“Academia Driven” and “Society Driven”) that reflect the greater influence of social and organizational structures on knowledge production and demands for research impact. This inventory enhances our ability to measure research activities at a time when researchers’ choices matter more than ever, and it will be of interest to researchers, policy makers, research funding agencies, and university and research organizations.info:eu-repo/semantics/publishedVersio

Knowledge discovery with recommenders for big data management in science and engineering communities

Recent science and engineering research tasks are increasingly becoming dataintensive and use workflows to automate integration and analysis of voluminous data to test hypotheses. Particularly, bold scientific advances in areas of neuroscience and bioinformatics necessitate access to multiple data archives, heterogeneous software and computing resources, and multi-site interdisciplinary expertise. Datasets are evolving, and new tools are continuously invented for achieving new state-of-the-art performance. Principled cyber and software automation approaches to data-intensive analytics using systematic integration of cyberinfrastructure (CI) technologies and knowledge discovery driven algorithms will significantly enhance research and interdisciplinary collaborations in science and engineering. In this thesis, we demonstrate a novel recommender approach to discover latent knowledge patterns from both the infrastructure perspective (i.e., measurement recommender) and the applications perspective (i.e., topic recommender and scholar recommender). In the infrastructure perspective, we identify and diagnose network-wide anomaly events to address performance bottleneck by proposing a novel measurement recommender scheme. In cases where there is a lack of ground truth in networking performance monitoring (e.g., perfSONAR deployments), it is hard to pinpoint the root-cause analysis in a multi-domain context. To solve this problem, we define a "social plane" concept that relies on recommendation schemes to share diagnosis knowledge or work collaboratively. Our solution makes it easier for network operators and application users to quickly and effectively troubleshoot performance bottlenecks on wide-area network backbones. To evaluate our "measurement recommender", we use both real and synthetic datasets. The results show our measurement recommender scheme has high performance in terms of precision, recall, and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis. In the application perspective, our goal is to shorten time to knowledge discovery and adapt prior domain knowledge for computational and data-intensive communities. To achieve this goal, we design a novel topic recommender that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We evaluate our scheme based on large collections of the dataset (i.e., publications, tools, datasets) from bioinformatics and neuroscience domains. Our experiments result using the perplexity metric show that our model has better generalization performance within a domain for discovering highly-specific latent topics. Lastly, to enhance the collaborations among scholars to generate new knowledge, it is necessary to identify scholars with their specific research interests or cross-domain expertise. We propose a "ScholarFinder" model to quantify expert knowledge based on publications and funding records using a deep generative model. Our model embeds scholars' knowledge in order to recommend suitable scholars to perform multi-disciplinary tasks. We evaluate our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and experiment results show that our ScholarFinder model outperforms state-ofthe-art models in terms of precision, recall, F1-score, and accuracy.Includes bibliographical references (pages 113-124)

Interdisciplinarity in Smart Sustainable City education: exploring educational offerings and competencies worldwide

More and more higher education institutions are offering specialized study programs for current and future managers of Smart Sustainable Cities (SSCs). In the process, they try to reconcile the interdisciplinary nature of such studies, covering at least the technical and social aspects of SSC management, with their own traditionally discipline-based organization. However, there is little guidance on how such interdisciplinarity should be introduced. In order to address this gap, this paper identifies 87 SSC-related study programs from around the world and analyzes their disciplinary and interdisciplinary coverage. The analysis classifies programs and competencies, the former using text mining and clustering algorithms, the latter using Bloom’s taxonomy and correlation analysis

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communi cation and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specifc vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación - CNA) to determine the quality evaluation parameters for universities in Colombia) in digital assemblages could lead to a better understanding of their dynamics and social foundations, thus resulting in better communication policies and intervention where necessary. The approach presented in this paper intends to determine what are the semantic spaces (sociolinguistic features) shared by social groups in digital social networks. It includes fve layers based on Design Science Research, which are integrated with Natural Language Processing techniques (NLP), Computational Linguistics (CL), and Artifcial Intelligence (AI). The approach is validated through a case study wherein the semantic values of a series of “Twit ter” institutional accounts belonging to Colombian Universities are analyzed in terms of the 12 quality factors established by CNA. In addition, the topics and the sociolect used by diferent actors in the university communities are also analyzed. The current approach allows determining the sociolinguistic features of social groups in digital social networks. Its application allows detecting the words or concepts to which each actor of a social group (university) gives more importance in terms of vocabular

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

Globalistics And Globalization Studies: Aspects & Dimensions Of Global Views

Nowadays globalization processes have become all-embracing. But at the same time, despite the ever-increasing flow of publications on globalization, our understanding and knowledge of it still leaves much to be desired. Especially it concerns the global processes in general, of which globalization is a part. We also need to systematize our ideas about globalization and Global Studies to somehow fit the realities. In particular, this concerns the education process, because the current state of education will determine the way people will perceive reality in the forthcoming decades. This yearbook aims at contributing to the solution of these important tasks. It is the third in the series of yearbooks titled Globalistics and Globalization Studies. This year it has the following subtitle: Aspects & Dimensions of Global Views. Its authors consider globalization and Global Studies in different dimensions and aspects: philosophical, methodological, and pedagogical, in terms of various processes, problems and perspectives. Of course, to some extent this means that this yearbook presents rather diverse materials. But globalization itself is very diverse. And its comprehension may proceed in the framework of different theoretical approaches and points of view. In the present yearbook one can find perceptions of globalization and Global Studies by a number of scholars from different countries of the world and learn rather peculiar visions of globalization by the Russian scientists and educators. The yearbook will be interesting to a wide range of researchers, teachers, students and all those who pay attention to global issues