Capturing New Nurses' Experiences and Supporting Critical Thinking

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

This study explored the lecture consulting experiences of lower-performing instructors at A University using text network analysis. The data consisted of open-ended responses from 67 instructors who received consulting between 2021 and 2024. Keyword frequency, TF-IDF, degree centrality, and betweenness centrality analyses were conducted to identify key concepts and their semantic relationships. The findings revealed that ‘class preparation’, ‘understanding students’, ‘class implementation’, ‘confidence’ and ‘instructional design’ were central concepts. The TF-IDF analysis highlighted ‘inducing participation’, ‘anticipated questions’, ‘repetitive practice’ and ‘delivery skills’ as distinctive strategic behaviors. Notably, ‘understanding students’ and ‘feedback’ served as key mediating concepts linking teaching experiences with instructional improvement. Overall, lecture consulting supported reflective practice, enhanced instructors’ confidence, and promoted a shift toward learner-centered teaching. The results provide empirical implications for faculty development programs and policy design.

본 연구는 교육개발협력에 관한 글로벌 연구 동향을 살펴보고, 이를 통해 국내 관련 연구에서의 향후 방향과 시사점을 탐색하는 것을 목적으로 한다. 이를 위해 교육개발협력 분야의 국제 학술지인「International Journal of Educational Development」를 선정하고, 2002년부터 2017년까지 약 15년간 게재된 연구 논문 966편을 대상으로 연구 초록에 제시된 (저자) 키워드를 텍스트 네트워크 분석하여 시기별, 교육영역별로 연구 주제가 어떻게 변화하고 이에 나타나는 특징이 무엇인지를 알아보았다. 이에 대한 주요 연구 결과는 다음과 같다. 첫째, 분석 대상 전체 논문에 나타난 연구 주제어의 출현 빈도를 살펴본 결과, 교육프 로그램관리, 학교수업, 지역공공행정, 교육지원서비스, 초등교육 순으로 높았으 며, 빈도 순 상위 20개의 핵심주제어에 대한 네트워크 중앙성 분석 결과는 빈도수 결과와 유사한 상관관계를 나타내었다. 그러나 중등교육, 학습, 교육연구, 교육변화, 교육의질 등의 주제어는 출현 빈도에 비해 높은 중앙성 지수를 나타내고 있어 다른 키워드들과 높은 관계성을 가지고 있었다. 둘째, 시기별 핵심 주제어 분석 결과 MDGs 전기 대비 후기와 SDGs 초기에는 새로운 키워드(초등교육, 초중등학교, 학교수업, 교육의 질, 중등교육, 교육계획)가 다양하게 나타났고, 중앙성 지수에서도 높은 수치를 나타내고 있어 새로운 핵심 연구 주제가 되고 있음을 알 수 있다. 셋째, 교육일반, 기초교육, 중등교육, 고등교육으로 분류한 교육 영역별 분석 결과에서는 빈도수와 중앙성이 높은 핵심 주제어가 각각 다소 상이 하게 나타나고 있어 영역에 따른 연구 키워드가 구분되고 있다는 특징이 부각되 었다. 본 연구는 국제 아젠다로서의 교육개발협력 특성을 고려하여 국제적 수준 에서 약 15년간 누적된 연구 논문들을 대상으로 객관적 데이터 분석 프로그램을 활용해 연구 주제의 변화 동향을 조망하였다는데 의의가 있으며, 현재 국내에서 실천적 노력과 더불어 교육개발협력에의 학문적 연구·개발이 지속적으로 강화되 어야 할 시점임을 고려할 때, 향후 보다 다양한 분야에서의 연구·개발에서 참고할 만한 시사점을 제공할 수 있을 것이다.The objective of the article is to find the research trends and the main traits presented in the keywords on abstracts of research articles of 「International Journal of Education Development」from 2002 to 2017. To do this, Text Network Analysis(TNA) was applied targeting 966 papers on the journal and the major research outcomes are as follows. First, the frequency analysis on the keywords showed that the keywords like Administration of education program, Schools and instruction, Regional public administration, Educational support service, Elementary education, and Elementary and secondary school were analyzed more than 100 times and also high in centrality degree. Second, the analysis results of the keywords presented in those research articles by development goal periods showed that several new keywords like Elementary education, Elementary and secondary school, Education quality, Secondary education, Educational planning have emerged frequently after SDGs and these keywords showed high in their centrality analysis. Third, the analysis on education level showed that the keywords like Elementary education, Administration of education program, School children were high in frequency and centrality degree in Elementary level. In secondary level, Schools and instruction, Administration of education program, Academic achievement were high, and in high level, college and university was high, respectively.

Co-authorship networks and research impact: A social capital perspective

Objectives This study examined the meaning and impact of team-based learning (TBL) on early childhood movement education by analyzing the reflective materials on teaching practices written by prospective early childhood teachers participating in TBL.&#x0D; Methods The participants of this study were 45 students enrolled in a 4-year early childhood education program in G City. The movement education class they took involved the design and demonstration of simulated lessons, which were conducted using the Team-Based Learning (TBL) approach. After completing a semester of classes, the learners wrote self-reflection materials on TBL and simulated lessons in the context of movement education. The qualitative data from these materials were analyzed using KrKwic and UCINET to extract keywords and perform centrality and semantic network analysis.&#x0D; Results Firstly, the common theme keywords that emerged from the TBL activities and simulated teaching demonstration experiences were ‘lesson plan’, ‘activity’, ‘movement’, ‘class’, ‘children’, ‘simulated teaching’, ‘feedback’, and ‘interest’. Secondly, the keywords associated with TBL activities included ‘time’, ‘team members’, ‘groups’, ‘opinions’, ‘active participation’, ‘challenges’, ‘parts’, ‘materials’, ‘expression’, ‘practice’, ‘modification’, and ‘utilization’. The semantic network analysis of TBL activities revealed a meaningful structure related to simulated teaching and the experiences of TBL activities. Thirdly, the keywords identified in relation to simulated teaching experiences were ‘teacher’, ‘thoughts’, ‘progress’, ‘experience’, ‘field’, ‘assistance’, ‘application’, ‘methods’, ‘process’, ‘rules’, ‘improvement’, and ‘demonstration’. The keyword ‘teacher’ showed high values in terms of linkages, degree centrality, closeness centrality, and betweenness centrality. The proximity centrality of ‘progress’, ‘experience’, and ‘application’ was high, while the betweenness centrality was low. The semantic network structure reflected the effectiveness of learning and application through the preparation and implementation phases of simulated teaching.&#x0D; Conclusions The semantic network analysis of early childhood movement education simulated teaching experiences conducted through TBL revealed that prospective early childhood educators perceive TBL activities and simulated teaching experiences as highly significant. These findings underscore the importance of learner-engaged instructional approaches in enhancing the teaching competencies of prospective early childhood educators.

Preventing power transmission failures in its network of electrical terminal stations is a major concern for the infrastructure of the Thai capital Bangkok and its vicinity. Towards this objective the present study aims to analyse the network and its reliability under conditions of increased demand. The analysis is based on a representation of the network as a graph allowing to identify the most important terminal stations by graph-theoretical terms. These are, in particular, the centrality measures Degree Centrality (DC) giving the number of a station’s one-hop neighbours, Closeness Centrality (CC) describing the efficiency of power transmission from one station to others, shortest-path Betweenness Centrality (BC) indicating the number of a station’s occurrences on the shortest paths between indirectly connected stations, Hub describing stations that are connected to a large number of important stations, and Authority indicating the stations that connect many important stations. Experimental results revealed that the Bangkok Noi station was most significant when the measures DC, CC and BC were considered and, on the other hand, that the North Bangkok station was vital in terms of CC, Hub and Authority. Therefore, these stations need to be closely monitored and their operation to be carried out with extreme care in order to prevent the occurrence of power transmission failures within the Bangkok metropolitan area.

Food addiction (FA) is a condition in which ultra-processed foods (UPFs) activate the brain's reward pathways, leading to binge eating, loss of control, and continued consumption despite negative consequences. It can appear early in childhood and is linked to behavioral, sociodemographic, and metabolic factors. This study assessed the contribution of FA, its structure, and connectivity in relation to sociodemographic, nutritional status, and metabolic variables in network analysis. A cross-sectional study was conducted with 93 children (7-11years old) living in Vitória de Santo Antão, Brazil. FA was assessed using the Yale Food Addiction Scale for Children, which was translated and validated for the Brazilian child population. Sociodemographic (age, sex, race, socioeconomic class), anthropometric (body weight, height, waist circumference, BMI, BMI-for-age, body fat percentage, lean mass, and fat mass), and metabolic (blood pressure, total cholesterol, triglycerides, HDL, LDL, and fasting glucose) factors were analyzed. For network analysis, the degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), and eigenvector centrality (EC) were evaluated. FA exhibited moderate centrality in sociodemographic and metabolic networks, acting as a connector between key variables such as age and socioeconomic class (BC = 0.071-0.500; EC = 0.301-0.500; CC = 0.636-0.667). These metrics indicate that FA, while not dominant, maintains access to influential nodes and participates in relevant information pathways. In contrast, within the anthropometric network, FA showed a peripheral role, with fewer direct links (DC = 0.222-0.285) and limited intermediation (BC = 0.111). Variation in centrality across domains underscores the selective integration of FA, suggesting that its impact is context dependent.

Centrality, which quantifies the importance of individual nodes, is among the most essential concepts in modern network theory. As there are many ways in which a node can be important, many different centrality measures are in use. Here, we concentrate on versions of the common betweenness and closeness centralities. The former measures the fraction of paths between pairs of nodes that go through a given node, while the latter measures an average inverse distance between a particular node and all other nodes. Both centralities only consider shortest paths (i.e., geodesics) between pairs of nodes. Here we develop a method, based on absorbing Markov chains, that enables us to continuously interpolate both of these centrality measures away from the geodesic limit and toward a limit where no restriction is placed on the length of the paths the walkers can explore. At this second limit, the interpolated betweenness and closeness centralities reduce, respectively, to the well-known current-betweenness and resistance-closeness (information) centralities. The method is tested numerically on four real networks, revealing complex changes in node centrality rankings with respect to the value of the interpolation parameter. Nonmonotonic betweenness behaviors are found to characterize nodes that lie close to intercommunity boundaries in the studied networks.

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Information and communication technology (ICT) outsourcing to suppliers has the potential to enhance buyers’ product innovation and overall performance. However, the impact of technology outsourcing to lower-tier suppliers on buyers’ financial performance in multi-tier supply chain networks remains unclear. Unlike prior dyadic studies that focus on a buyer-tier1 supplier perspective, our study extends the analytical scope to encompass buyer, tier-1, and tier-2 ICT supplier triads using multi-tier supply network data from Capital IQ. Building on existing literature indicating that a buyer’s betweenness centrality negatively affects its financial performance, we further find that tier-1 suppliers’ betweenness centrality also exhibits a negative relationship with buyers’ financial performance. In addition, we provide empirical evidence that a buyer’s timely supplier payment amplifies the positive effect of buyer degree centrality on its financial performance, while alleviating the positive effect of tier-1 suppliers’ degree centrality on buyers’ financial performance. Interestingly, timely supplier payment negatively moderates the association between a buyer’s closeness centrality and its financial performance, but positively moderates the corresponding association between tier-1 supplier closeness centrality and buyer financial performance. Our empirical analysis advances theoretical understanding of lower-tier suppliers and sheds light on the bridging role of tier-1 suppliers in the literature of multi-tier supply chain relationships. These findings highlight the benefits of technology-enabled multi-tier supply network structures and their interactions with buyer-tier1 supplier relationships. This study also provides practical insights for firms to improve financial outcomes through strategic ICT outsourcing to multi-layer suppliers.

The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidate's own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as well as inefficient in terms of information extraction. We believe that social network analysis is a more appropriate alternative. It allows for the identification of local and global patterns, identification of influential nodes as well as the contacts involved in the influence. In general, this particular analytic technique can help in examining the internal complex network dynamics. In the current paper, we investigates whether personal contacts matter more than know-how contacts in wiki election nominations and voting participation. We employ the use of standard social network analysis tools such as Pajek and Gephi. The presented work demonstrates the significance of personal contacts over know-how contacts of a person in online elections. We have discovered that personal contacts, i.e. immediate neighbors (based on degree centrality) and neighborhood (k-neighbors) of a person have a positive effect on a person’s nomination as an administrator and also contribute to the active participation of voters in voting. Moreover, know-how contacts, analyzed by means of measures such as betweenness and closeness centralities, have a relatively insignificant effect on the selection of a person. However, know-how contacts, measured in terms of betweenness centrality can positively contribute only to the voting process—primarily due to the role played in passing information around the network. These contacts, also measured in terms of influence domain and PageRank, can play a vital role in the selection of an admin. Additionally, such contacts have a positive association with the voting process in terms of reachability and brokerage roles.

Calculating centrality measures has been used to identify Cyber-physical System critical component for some time. However, accurate identification of critical elements within the interconnected system that are vulnerable to system failures and intentional attacks requires not only a reliable model but robust index to dynamic system operating environment. These measures include 1) Node Degree, 2) Node Importance, 3) Betweenness Centrality, 4) Closeness Centrality and 5) Eigenvector Centrality. System component criticalities have been evaluated based on these measures in this paper and their performances and rankings under different network topologies and operating conditions have been compared using 1) Pearson Correlation Coefficient and 2) Spearman Correlation Coefficient. This has enabled the quantification of the optimality of using different centrality measures in different network scenarios. The results suggest that Betweenness and Degree centralities are the most suitable indices for identifying critical electrical power system bus and cyber system node individually. Moreover, the degree of correlation between directed measures has suggested also the necessity of using directional model in Cyber-physical System studies.

Biomedical relations play an important role in biological processes. In this work, we combine information filtering, grammar parsing and network analysis for gene-disease association extraction. The proposed method first extracts sentences potentially containing information about gene-diseases interactions based on maximum entropy classifier with topic features. And then Probabilistic Context–Free Grammars is applied for gene-disease association extraction. The network of genes and the disease is constituted by the extracted interactions, network centrality metrics are used for calculating the importance of each gene. We used breast cancer as testing disease for system evaluation. The 31 top ranked genes and diseases by the weighted degree, betweenness, and closeness centralities have been checked relevance with breast cancer through NCBI database. The evaluation showed 83.9% accuracy for the testing genes and diseases, 74.2% accuracy for the testing genes.

코로나(Covid19) 사태로 인해 자동차 공급사슬에서의 단절(Disruption)로 인한 문제가 더욱 부각 되었고 무엇보다 안정적인 공급체인 구성이 기업의 생존에 화두가 되고 있다. 특히 복잡한 공급사슬 구조를 가져 유기적으로 연결되어 있는 자동차 부품산업은 각 공급업체별 개별적 분석으로 전체를 이해하는데 한계점이 존재한다. 따라서 좀 더 확장된 시각에서의 폭넓은 부품 네트워크의 실증적 분석에 대한 요구가 높아지고 있다. 본 연구에서는 한국자동차산업을 사회연결망분석(SNA_Social Network Analysis)기반하여 부품 네트워크를 분석하였다. 기존 연구에서는 각 개별 기업별 중심성(연결 중심성, 매개 중심성, 근접 중심성)을 특정 부품(브레이크 등)으로 국한하여 연구가 진행되었다. 본 연구에서는 자동차에 포함되는 부품들을 기능별로 구분하여 대부분의 주요 부품을 포함시켜서 각 부품의 한국자동차산업 네트워크의 특성을 분석하였다. 한국자동차사산업의 부품기능별로 체계적인 분석을 위하여 산업현장에서 통용되고 있는 분류체계 중에서 대한무역투자진흥공사(KOTRA)에서 수출입 자동차 부품 집계용으로 분류 사용하고 있는 6대 분류체계를 본 연구에 적용하였다. 이에 따른 연구 결과로 외향연결중심성(방향성 있음/없음)과 매개 중심성(방향성 없음)에서 부품기능별 유의미한 차이점을 확인할 수 있었다. 이를 통해 한국자동차산업에서의 부품기능별 부품 네트워크 특성 이해와 더불어 한국자동차산업 정책 전반에 참조할 수 있는 시사점을 제공하고자 하는데 본 연구의 목적이 있다.Understanding the automotive supply chain structure has received greater focus due to the disruption caused by Covid-19. Above all, a sustainable supply chain is now essential for companies’ survival. Particularly, the automobile parts industry, which has a complex supply chain structure, is difficult to understand through individual supplier analysis. Therefore, to provide better understanding for the structure of the auto parts supply chain, it is necessary to analyze the broad network. We analyzed the auto parts network of the Korean automobile industry using social network analysis (SNA). Previous studies considered the centrality of each individual company (degree centrality, betweenness centrality, closeness centrality) with specific parts (brakes, etc.). This study included the auto parts, classified by function and most major parts, and analyzed the characteristics of the network of the Korean automobile industry by part We found significant differences among part groups based on function with degree centrality and betweenness centrality. This study can provide the automobile industry policy suggestions through understanding of the characteristics of the Korean automobile parts network.