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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]

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 interdisciplinary field of human movement sciences (HMS) has gained massive interest among educational institutions around the world, not only in terms of academic programs but also in research. With this emergence, the researchers aimed to describe the productivity of HMS research in the Philippines. The descriptive bibliometric analysis phase of this paper considered papers published and indexed in Google Scholar from January 2010 to June 2021 and was analyzed after data cleaning and preprocessing. Results revealed that a total of 274 research publications were recorded between the years 2010 and 2021 with an average annual publication rate of 28.6% as far as the dataset was concerned. Also, public higher education institutions (HEIs) emerged to be the most productive generators of research outputs, specifically topped by the University of the Philippines (UP). Moreover, UP-based authors dominated the rankings of the most productive HMS researchers, with "Jeffrey Pagaduan" in the top rank. Results further indicated that 75% of the authors collaborated with fellow researchers within or outside their institution. Meanwhile, the term "physical education" was recorded to be the most frequently appearing word in most of the publications. Through the aid of the data mining approach social network analysis (SNA), five (5) researchers from public HEIs and two from private HEIs were identified to have the largest networks as discussed in the paper. The exploration of research in terms of productivity and the embedded social network of researchers may serve as a springboard in defining the future development of the field in the Philippines.

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]

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.

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.

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.

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.

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Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords "Sentinel", "deep learning" (DL), and "LSTM" (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images' accuracy. [ABSTRACT FROM AUTHOR]

The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem's scale, complexity, and constraints, necessitating careful consideration of each method's strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources. [ABSTRACT FROM AUTHOR]

Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally represent subject knowledge. The system uses the node centrality and node weight to expand the knowledge graph system, which can better express the structural relationship among knowledge. It applies the particle swarm fusion algorithm of multiple rounds of iterative simulated annealing to achieve the recommendation of learning paths. Furthermore, the system feeds back the students' learning situation to the teachers. Teachers check and fill in the gaps according to the performance of the learners in the teaching activities. Aiming at the weak links of students' knowledge points, the particle swarm intelligence algorithm is used to recommend learning paths and learning resources to fill in the gaps in a targeted manner.

Textile, apparel, and fashion (TAF) industries contribute significantly to global environmental pollution at every point of the supply chain. Clothing manufacturing and transportation produce a large volume of waste and high greenhouse gas emissions, often taking advantage of cheap labor in developing countries. As a result, stakeholders are becoming more aware of the effect of the textile, apparel, and fashion industries on the climate and human rights, thus pushing businesses to mitigate their environmental damage. This paper offers a systematic literature review of sustainability trends in the TAF industries in the last 20 years. Bibliometric tools are also used to support the content analysis of the papers. The findings reveal three primary research areas in the TAF context: consumers' behaviour towards sustainable clothing, circular economy initiatives, and sustainability challenges across the whole supply chain. As a result, this study highlights literature gaps and provides future research suggestions for each identified research cluster. In addition, drivers and barriers to implementing corporate social responsibility and circular economy practices are identified. Consequently, this study will help researchers and academicians work in this area to identify unexplored sub-fields, which reflect some potential investigation areas for expanding scientific literature on the topic. Finally, this study supports practitioners and managers in exploring the main research themes addressed in the scientific field, providing knowledge to improve and align business models with current sustainability trends. [ABSTRACT FROM AUTHOR]

In today's digital society, computer-supported collaborative learning (CSCL) and collaborative problem solving (CPS) have received increasing attention. CPS studies have often emphasized outcomes such as skill levels of CPS, whereas the action transitions in the paths to solve the problems related to these outcomes have been scarcely studied. The patterns within action transitions are able to capture the mutual influence of actions conducted by pairs and demonstrate the productivity of students' CPS. The purpose of the study presented in this paper is to examine Finnish sixth graders' (N = 166) patterns of action transitions during CPS in a computer-based assessment environment in which the students worked in pairs. We also investigated the relation between patterns of action transitions and students' social and cognitive skill levels related to CPS. The actions in the sequential processes of computer-based CPS tasks included using a mouse to drag objects and typing texts in chat windows. Applying social network analysis to the log file data generated from the assessment environment, we created transition networks using weighted directed networks (nodes for those actions conducted by paired students and directed links for the transitions between two actions when the first action is followed by the second action in sequence). To represent various patterns of action transitions in each transition network, we calculated the "numbers of nodes" (numbers of actions conducted), "density" (average frequency of transitions among actions), "degree centralization" (the dispersion of attempts given to different actions), "reciprocity" (the extent to which pairs revisit the previous one action immediately), and "numbers of triadic patterns" (numbers of different repeating formats within three actions). The results showed that pairs having at least one member with high social and high cognitive CPS skills conducted more actions and demonstrated a higher average frequency of action transitions with a higher tendency to conduct actions for different number of times, implying that they attempted more paths to solve the problem than the other pairs. This could be interpreted as the pairs having at least one student with high social and high cognitive CPS skills exhibiting more productive CPS than the other pairs. However, we did not find a significant difference across the pairs in terms of alternating sequences of two or three actions. Investigating the patterns of action transitions of the dyads in this study deepens our understanding of the mutual influence between the CPS actions occurring within dyads. Regarding pedagogical implication, our results offer empirical evidence recommending greater awareness of the students' social and cognitive capacities in CPS when assigning them into pairs for computer-based CPS tasks. Further, this study contributes to the methodological development of process-oriented research in CSCL by integrating an analysis of action transition patterns with a skill-based assessment of CPS.

Background: There has been a marked increase in cervicogenic headaches in recent years, significantly affecting sufferers' daily lives and work. While several treatments exist for this type of headache, their long-term effects could be improved, and additional data from large clinical samples are needed. This study aims to systematically examine the current state of research in cervicogenic headaches through a bibliometric analysis, identify areas of current interest, and provide insight into potential future research directions. Methods: This article examines research trends in the field of cervicogenic headache through a bibliometric analysis of scholarly articles in the field of cervicogenic headache over the past four decades. The bibliometric analysis method employed included searching the Web of Science database using topics related to cervicogenic headaches. Inclusion criteria were limited to articles and review papers on cervicogenic headaches published between 1982 and 2022. The retrieved dataset was then analyzed using R software and VOSviewer to identify the major research areas, countries and institutions, the most influential authors, journals and keywords, co-citations in the literature, and co-authorship networks. Results: This study analyzed 866 articles published between 1982 and 2022, involving 2,688 authors and generating 1,499 unique author keywords. Neuroscience and neurology were the primary focus, with participation from 47 countries, primarily led by the United States, which has the most published articles (n = 207), connections (n = 29), and citations (n = 5,238). In the cervicogenic headache study, which involved 602 institutions, the University of Queensland received the most significant number of citations (n = 876), and Cephalalgia was the journal with the most published articles and received the most local citations (n = 82) and highest growth (n = 36). Two hundred sixty-nine journals have published articles on cervicogenic headaches. Among researchers studying cervicogenic headache, Sjaastad O had the most published articles (n = 51) and citations (n = 22). The most commonly occurring keyword was "cervicogenic headache." Except for the fourth most impactful paper, as determined by the Local Citation Score, which analyzed clinical treatments, all the top documents emphasized investigating the diagnostic mechanisms of cervicogenic headache. The most commonly occurring keyword was "cervicogenic headache." Conclusion: This study used bibliometric analysis to provide a comprehensive overview of the current research on cervicogenic headaches. The findings highlight several areas of research interest, including the need for further investigation into the diagnosis and treatment of cervicogenic headaches, the impact of lifestyle factors on cervicogenic headaches, and the development of new interventions to improve patient outcomes. By identifying these gaps in the literature, this study provides a foundation for guiding future research to improve the diagnosis and treatment of cervicogenic headaches. [ABSTRACT FROM AUTHOR]

Over the last decade, human resource (HR) analytics has been widely discussed in the landscape of human resource management due to its dynamic capacity to transform into a data-driven decision-making system for optimizing workforce management. The aim of this paper is to provide a comprehensive summary of the emerging trends and themes of HR analytics drawn from previous literature and offer valuable insight into academia, researchers, practitioners, and policymakers. This paper employs a bibliometric methodology while using RStudio, Biblioshiny, and VOSviewer tools to collect and analyze 102 articles from the Scopus database from January 2008 to September 2023. The findings of the paper reveal current state-of-the-art research in the HR analytics domain while exploring key themes and areas for further study. This study offers practical guidelines for policymakers and contributes to the existing knowledge domain of HR analytics. [ABSTRACT FROM AUTHOR]

To identify and analyze the 50 most cited articles on temporary anchorage devices (TADs) and investigate the achievement and development of scientific research about the topic through a bibliometric analysis. On August 22, 2022, a computerized database search was performed to detect papers published in the scientific literature about TADs from 2012 to 2022. Metrics data were identified using the Incites Journal Citation Reports (Clarivate Analytics) data set. The Scopus database was used to obtain information on the authors' affiliations, country of origin, and h-index. Key words were automatically harvested from the selected articles to implement the visualized analysis. From a total of 1858 papers screened by searching the database, a list of the top 50 most cited articles was created. The total number of citations collected by the 50 most cited articles in TADs was 2380. Among the 50 most cited articles on TADs, 38 were original research papers (76.0%) and 12 were reviews (24.0%). As shown by the key word–network analysis, Orthodontic anchorage procedure was identified as the larger node. Findings of this bibliometric study showed an increasing number of citations for papers on TADs, accompanied by a simultaneous rise in scientific interest in this topic in the past decade. The present work identifies the most influential articles, emphasizing the journals, the authors, and the topics addressed. [ABSTRACT FROM AUTHOR]

Social capital concept has become popular over the past decades. What is not known in literature and worth investigating is the network designs and nature that enhance the development of new learning ideas and knowledge creation. The aim of the paper is to examine network structure dimensions, namely centrality (bonding) and structural holes (bridging or looser ties) of social capital formed within higher education networks that stimulate the development of new ideas and knowledge creation of the participants in the networks across multiple domains of science. This study employs negative binomial regression on a sample of participants within a higher education network to predict the development of new ideas and knowledge creation. The results show that the different features of social capital dimensions influence the development of new ideas and knowledge creation of the participants in the networks differently. Specifically, the study reveals that the brokerage network appears to be more relevant than cohesion in the Ghanaian higher education institution networks. When knowledge creation is decomposed into different fields of science, Health Science is found to be the most productive. The decomposing of the knowledge creation into different fields of science remains a value of this study.

Likert-style surveys are a widely used research instrument to assess respondents' preferences, beliefs, or experiences. In this paper, we propose and demonstrate how network analysis (NA) can be employed to model and evaluate the interconnectedness of items in Likert-style surveys. We explore the advantages of this approach by applying the methodology to the aspects of student experience scale datasets and compare the results to the principal component analysis. We successfully create a meaningful network based on survey item response similarity and use modular analysis of the network to identify larger themes built from the connections of particular aspects. The modular NA of the network of survey items identifies important themes that highlight differences in students' overall experiences. Our network analysis for Likert-style surveys methodology is widely applicable and provides a new way to investigate phenomena assessed by Likert-style surveys.

Root traits have an important impact on plant growth, which reflects the adaptation of plants to nutrients. This paper describes the focus and progress of root traits research and provides references for future research. Based on the Web of Science online database, articles related to root traits from 2000–2021 were evaluated using bibliometric methods. The results showed that the study of root traits has been growing steadily in the last two decades. Wang Yan is the author with the highest number of published papers, the most citations and the highest H-index. The top three published countries are the USA, China and India. The top three institutions are the University of Western Australia, China Agricultural University and Nanjing Agricultural University. Plant and Soil, Frontiers in Plant Science and PLoS One have the highest number of published articles. In terms of co-occurrence of keyword analysis, growth, soil and plant diversity are highly correlated with root traits. It was found that cooperation between authors, institutions and countries are not close enough and that z global network of cooperation has not yet been formed. Therefore, worldwide cooperation should be strengthened to promote resource sharing and the openness of relevant mechanisms. [ABSTRACT FROM AUTHOR]

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale "rural-urban" population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is widely used as an important indicator and ecological parameter of vegetation structure and growth status, but the LAI lacks bibliometric analysis. To further understand the LAI's research status and frontier dynamics, we used 75 years of data (1947–2021) from the Web of Science for scientific bibliometric analysis. The results showed that 22,276 LAI re-search papers were published from 1947 to 2021. According to the characteristics of the literature growth, LAI research can be divided into five stages: incubation, cultivation, acceleration, evolution, and outbreak periods. The research power at the different stages had different characteristics. The overall research power of the United States is number one globally, followed by China, Canada, and France. The related disciplines were widely varied, involving agriculture (the most studied field of LAI research), environmental science and ecology, remote sensing, and other fields. The development of the Google Earth engine, cloud computing platforms, and unmanned aerial vehicle technology will provide more critical support for LAI research. The results of this paper quantitatively show the development history, research hotspots, and application of LAI research and provide a reference for understanding the current situation and development trends of global LAI research. [ABSTRACT FROM AUTHOR]