Social Network Analysis Measures Research Articles

Silent No More: Women as Significant Political Intermediaries in Ottoman Algeria

Over the course of nearly 300 years, Ottoman and non-Ottoman governors struggled to suppress the recalcitrant clans of the eastern province of Algeria and sustain control. Algerian women proved invaluable partners in these efforts. After the mid-seventeenth century, most Ottoman officials married into a local family as one of the surest ways to establish their legitimacy among the Algerian elite. Through text mining to extract named and unnamed entities and social network visualization to illustrate their relationships, I represent unnamed women’s spectral presence despite their absence in the archival record. These kinship connections and the sub-communities to which they give rise can be meaningfully investigated quantitatively using social network analysis measures, such as betweenness centrality scores. Examining these quantitative measures reveals both named and unnamed women’s positions within the structure of Ottoman-Algerian society. Through an analysis of the individual lives, relationships and the underlying structure that make up the Ottoman-Algerian network in Constantine between 1567 and 1837, I argue that local women were the most significant links in the chain that bound Algeria to the Ottoman Empire.

BÖLGESEL KALKINMA ÇALIŞMALARINDA ORTAK YAZARLIK VE ATIF AĞLARININ SOSYAL AĞ ANALİZİ İLE İNCELENMESİ

Sosyal ağ analizi ilişki yapılarının araştırılmasında kullanılan önemli bir yöntemdir. Aynı zamanda, bilimsel yayınların ilişki yapıları, işbirliği düzeyleri, üretken yazarların belirlenmesi, alıntı sayılarındaki durumun nitelik yönünden değerlendirilmesini sağlamaktadır. Bu çalışmada, Türkiye’de bölgesel kalkınma alanında yapılan çalışmalarda öne çıkan yazar ve bölümler, kurumsal işbirliği düzeyi sosyal ağ analizi ve düğümlerin ağ yapı içerisinde merkezilik değerleri yardımıyla incelenmektedir. 1975-2017 yılları arasında atıf indekslerinde (SCI, SSCI ve AHCI) Bölgesel Kalkınma konusunda, Türkiye adresli yapılmış 158 yayın değerlendirilmiştir. Elde edilen sonuçlar, atıf indekslerinde yayınlanmış bölgesel kalkınma çalışmalarının hızının 2010 yılından sonra arttığını göstermektedir. Yapılan çalışmalar yeterli kurumsal desteğe sahip olmamakla birlikte, etki faktörü düşük dergilerde yayınlanmaktadır. Ayrıca, ekonomi bilimine ait bir çalışma alanı olmasına karşın, yayın sayısının şehir ve bölge planlama gibi diğer alanlarda daha yüksek olduğu görülmektedir.

Social network analysis in humanitarian logistics research

PurposeThe purpose of this paper is to promote social network analysis (SNA) methodology within the humanitarian research community, surveying its current state of the art and demonstrating its utility in analyzing humanitarian operations.Design/methodology/approachA comprehensive survey of the related literature motivates a proposed agenda for interested researchers. Analysis of two humanitarian networks in Afghanistan demonstrates the use and utility of SNA, based on secondary data. In the second case study, the use of random graphs to detect network motifs is demonstrated using Monte Carlo simulation to create the benchmark null sets.FindingsSNA is an adaptable and highly useful methodology in humanitarian research, quantifying patterns of community structure and collaboration among humanitarian organizations. Network motifs suggesting distinct affinity between particular agencies within humanitarian clusters are observed.Research limitations/implicationsThe authors summarize common challenges of using SNA in humanitarian research and discuss ways to alleviate them.Practical implicationsPractitioners can use SNA as readily as researchers, to visualize existing networks, identify areas of concern and better communicate observations.Social implicationsBy making SNA more accessible to a humanitarian research audience, the authors hope its ability to capture complex, dynamic relationships will advance understanding of effective humanitarian relief systems.Originality/valueTo the best of knowledge, it is the first study to conduct a systematic analysis of the application of SNA in empirical humanitarian research and outline a concrete SNA-based research agenda. This is also a currently rare instance of a humanitarian study using random graphs to assess observed SNA measures.

SCM research leadership: the ranked agents and their networks

PurposeThis study aims to examine publications of supply chain management (SCM) researchers from across the world and maps the leadership role of authors and institutions based on how prolific they are in publishing and on network measures of centrality while accounting for the quality of the outlets that they publish in. It aims to inform stakeholders on who the leading SCM scholars are, their primary areas of SCM research, their publication profiles and the nature of their networks. It also identifies and informs on the leading SCM research institutions of the world and where leadership in specific areas of SCM research is emerging from.Design/methodology/approachBased on SCM papers appearing in a set of seven leading journals over the 15-year period of 2001-2015, publication scores and social network analysis measures of total degree centrality and Bonacich power centrality are used to identify the highest ranked agents in SCM research overall, as well as in some specific areas of SCM research. Social network analysis is also used to examine the nature and scope of the networks of the ranked agents and where leadership in SCM research is emerging from.FindingsAuthors and institutions from the USA and UK are found to dominate much of the rankings in SCM research both by publication score and social network analysis measures of centrality. In examining the networks of the very top authors and institutions of the world, their networks are found to be more inward-looking (country-centric) than outward-looking (globally dispersed). Further, researchers in Europe and Asia alike are found to exhibit significant continental inclinations in their network formations with researchers in Europe displaying greater propensity to collaborate with their European-based counterparts and researchers in Asia with their Asian-based counterparts. Also, from among the journals,Supply Chain Management: An International Journalis found to exhibit a far more expansive global reach than any of the other journals.Research limitations/implicationsThe journal set used in this study, though representative of high-quality SCM research outlets, is not exhaustive of all potential outlets that publish SCM research. Further, the measure of quality that this study assigns to the various publications is based solely on a publication score that accounts for the quality of the journals, as rated byAssociation of Business Schoolsthat the papers appear in and nothing else.Practical implicationsBy informing the community of stakeholders of SCM research about the top-ranked SCM authors, institutions and countries of the world, the nature of their networks, as well as what the primary areas of SCM research of the leading authors in the world are, this research provides stakeholders, including managers, researchers and students, information that is helpful to them not only because of the insights it provides but also for the gauging of potential for embedding themselves in specific networks, engaging in collaborative research with the leading agents or pursuing educational opportunities with them.Originality/valueThis research is the first of its kind to identify and rank the top SCM authors and institutions from across the world using a representative set of seven leading SCM and primary OM journals based on publication scores and social network measures of centrality. The research is also the first of its kind to identify and rank the top authors and institutions within specific areas of SCM research and to identify future research opportunities relating to aspects of collaboration and networking in research endeavors.

Methods for assessing higher education research team collaboration: comparing research outputs and participant perceptions across four collaborative research teams

ABSTRACTThis article suggests that situations in which multiple research teams are convened under similar conditions present an opportunity to discover factors that lead to productive collaboration. It argues that social network analysis of research team outputs becomes more valuable when paired with data about research participant perceptions; and that any variables used as indicators of collaboration need to be calibrated using datasets from multiple studies with cross-team comparisons. The article provides an example of the kind of methodology needed to achieve this, describing a study with data from four research teams based at an Australian university campus, reporting their research performance over four years under conditions in which many variables were controlled and with results augmented by a survey of participant perceptions. Findings from the study indicate that there were exceptions to hypothesized associations between participant perceptions of collaboration and specific social network analysis measures over co-authorship data. The article suggests that, given the methodological challenges of studying research teams in the field, multiple datasets combining findings such as those in the present study are a path towards the development of indicators of productive higher education research collaboration.

Opinion leader detection using whale optimization algorithm in online social network

In the current digital era, optimization is one of the most significant problems in the social network. Most of the issues related to optimization are NP-complete and not possible to solve them in polynomial time. Detection of opinion leader based on their optimized centrality measure is a critical issue. The opinion leaders have a non-trivial influence on the other user's decision-making process and can solve various problems related to the diffusion of new products and innovations in the real world. In this paper, we proposed a new Social Network-based Whale Optimization Algorithm (SNWOA) to find the top-N opinion leaders by measuring the reputation of the user using various standard optimization function in the network. The proposed algorithm is advantageous to determine the opinion leaders because it based on the bubble-net hunting behavior of humpback whales. The algorithm found the best possible solution as the number of users raises progressively in the network; therefore, the general complexity of the algorithm remains unchanged. Besides, we also proposed a new approach to categorize the communities based on the similarity index comprising neighbor similarity and clustering coefficient as the significant components. Initially, we computed the objective function of each user by using their centralities and deployed the proposed algorithm with different optimization functions to identify the local and universal opinion leaders. We implemented the proposed algorithm on the real and synthesized datasets and compared the result based on the accuracy, precision, recall, and F1-score. The result indicates that the proposed algorithms give a better result as compared to the other standard Social Network Analysis (SNA) measures. We also concluded that the community partitioning algorithm is even better than the other community detection algorithms based on different parameters and computational time.

Measuring coordination of epilepsy care: A mixed methods evaluation of social network analysis versus relational coordination

ObjectivesCoordination of multidisciplinary care is critical to address the complex needs of people with neurological disorders; however, quality improvement and research tools to measure coordination of neurological care are not well-developed. This study explored and compared the value of social network analysis (SNA) and relational coordination (RC) in measuring coordination of care in a neurology setting. The Department of Veterans Affairs Healthcare System (VA) established an Epilepsy Centers of Excellence (ECOE) hub and spoke model of care, which provides a setting to measure coordination of care across networks of providers. MethodsIn a parallel mixed methods approach, we compared coordination of care of VA providers who formally engage the ECOE system to VA providers outside the ECOE system using SNA and RC. Coordination of care scores were compiled from provider teams across 66 VA facilities, and key informant interviews of 80 epilepsy care team members were conducted concurrently to describe the quality of epilepsy care coordinating in the VA healthcare system. ResultsOn average, members of healthcare teams affiliated with the ECOE program rated quality of communication and respect higher than non-ECOE physicians. Connectivity between neurologist and primary care providers as well as between neurologists and mental health providers were higher within ECOE hub facilities compared to spoke referring facilities. Key informant interviews reported the important role of formal and informal programming, social support and social capital, and social influence on epilepsy care networks. ConclusionFor quality improvement and research purposes, SNA and RC can be used to measure coordination of neurological care; RC provides a detailed assessment of the quality of communication within and across healthcare teams but is difficult to administer and analyze; SNA provides large scale coordination of care maps and metrics to compare across large healthcare systems. The two measures provide complimentary coordination of care data at a local as well as population level. Interviews describe the mechanisms of developing and sustaining health professional networks that are not captured in either SNA or RC measures.

Data-driven modeling of diabetes care teams using social network analysis.

We assess working relationships and collaborations within and between diabetes health care provider teams using social network analysis and a multi-scale community detection. Retrospective analysis of claims data from a large employer over 2 years was performed. The study cohort contained 827 patients diagnosed with diabetes. The cohort received care from 2567 and 2541 health care providers in the first and second year, respectively. Social network analysis was used to identify networks of health care providers involved in the care of patients with diabetes. A multi-scale community detection was applied to the network to identify groups of health care providers more densely connected. Social network analysis metrics identified influential providers for the overall network and for each community of providers. Centrality measures identified medical laboratories and mail-order pharmacies as the central providers for the 2 years. Seventy-six percent of the detected communities included primary care physicians, and 97% of the communities included specialists. Pharmacists were detected as central providers in 24% of the communities. Social network analysis measures identified the central providers in the network of diabetes health care providers. These providers could be considered as influencers in the network that could enhance the implication of promotion programs through their access to a large number of patients and providers. The proposed framework provides multi-scale metrics for assessing care team relationships. These metrics can be used by implementation experts to identify influential providers for care interventions and by health service researchers to determine impact of team relationships on patient outcomes.

Role network measures to assess healthcare team adaptation to complex situations: the case of venous thromboembolism prophylaxis

Hospitals are complex environments that rely on clinicians working together to provide appropriate care to patients. These clinical teams adapt their interactions to meet changing situational needs. Venous thromboembolism (VTE) prophylaxis is a complex process that occurs throughout a patient’s hospitalisation, presenting five stages with different levels of complexity: admission, interruption, re-initiation, initiation, and transfer. The objective of our study is to understand how the VTE prophylaxis team adapts as the complexity in the process changes; we do this by using social network analysis (SNA) measures. We interviewed 45 clinicians representing 9 different cases, creating 43 role networks. The role networks were analysed using SNA measures to understand team changes between low and high complexity stages. When comparing low and high complexity stages, we found two team adaptation mechanisms: (1) relative increase in the number of people, team activities, and interactions within the team, or (2) relative increase in discussion among the team, reflected by an increase in reciprocity. Practitioner Summary: The reason for this study was to quantify team adaptation to complexity in a process using social network analysis (SNA). The VTE prophylaxis team adapted to complexity by two different mechanisms, by increasing the roles, activities, and interactions among the team or by increasing the two-way communication and discussion throughout the team. We demonstrated the ability for SNA to identify adaptation within a team.

Measurement of Social Networks for Innovation within Community Disaster Resilience

Disaster risk reduction (DRR) research has long recognised that social networks are a vital source of support during and after a shock. However, the quantification of this social support, primarily through its recognition as social capital, has proven problematic as there is no singular method for its measurement, invalidating the credibility of studies that try to correlate its effects with community disaster resilience. Within the wider resilience field, research that specifically utilises social networks as the focus of analysis is evolving. This paper provides a critical synthesis of how this developing discourse is filtering into community disaster resilience, reviewing empirical case studies from the Global South within DRR that use social network analysis and connectivity measurement. Our analysis of these studies indicates that a robust methodology utilising social network analysis is emerging, which offers opportunity for research cross-comparability. Our review also finds that without this bottom-up mapping, the implementation of top-down preparedness policy and procedures are likely to fail, resulting in the advocation of social network analysis as a critical methodology in future resilience research and policy planning.

Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals

Social network analysis (SNA) is a technique to study behavioral dynamics within a social group. In SNA, it is an open question whether it is possible to characterize animal-level behaviors by using group-level information. Also, it was believed that the combined use of SNA would provide a more comprehensive understanding of social dynamics. In light of these two factors, here we explain an approach to evaluate animal importance to a group by considering the variability in group-level structural information, which is computed by joining the animal- and group-level SNA measures node centrality and network entropy, respectively. Moreover, two other metrics, animal social interaction range and nearest-neighbor frequency matrix, which represent a social affiliation of each animal within the group, are computed to help address the general challenges in graph-based SNA and, thereby, improve the precision of animal importance measures. Finally, we derive the joint distribution of animal importance of the group in detecting atypical social behaviors. The approach is tested using tracking data of dairy cows. The reliability of the derived animal importance was superior to the already existing animal importance measures. To illustrate the usability of the animal importance metric, a simulation study was conducted to identify sick and estrus animals in a group. The social affiliation of sick cows was less when compared to healthy cows. Also, their individual distributions of animal importance were shifted toward the left of the mean of the animal importance distributions of healthy cows. Consequently, the joint distribution of animal importance of the group exhibited a bimodal distribution with a left tailored shape. The behavior of cows in estrus was opposite to that of sick cows. Moreover, with the increasing number of sick and estrus cows in the group, respectively, the group entropy decreased with larger variance and slightly increased with less variance. Therefore, the entropy-based animal importance metric has superior performances when evaluating animal importance to the group compared to the existing metrics. It can be used for generating alerts for the early detection of atypical social behaviors associated with, for instance, animal health, veterinary, and welfare.

Analysing the attractiveness, availability and accessibility of healthcare providers via social network analysis (SNA)

Due to the increasing pressure on resource limitations and the need for efficiency improvements, effective healthcare service planning should analyse trends in citizens' demand for healthcare services, as well as patients' choices of healthcare providers, which are determined not only by prestige, but also by physical accessibility and availability. The main objective of the present study is to explore the benefits of adopting the social network analysis (SNA) approach to investigate the determinants of a patient's choice of healthcare provider as a way to support better resource allocation decisions in healthcare systems. For the purpose of the analysis, administrative data was used to track the flow of patients within orthopaedic departments of the Lombardy region (Italy) in 2014. A network was developed with two types of nodes: those of orthopaedic departments in the regional hospitals (114 nodes), and those of municipalities (5092 nodes). Using the Louvain algorithm, the communities and sub-communities were determined on the basis of patients' choices, without any prior geographical considerations or planned catchment areas.Traditional SNA measures and other novel indicators, specifically developed for this study, were applied: in particular, attractiveness (i.e. the preference that patients assign to a certain department against the others available in the same reference area) and confinement (of demand) (i.e. the degree of the unique use of a certain department). A decision support matrix based on these indicators was defined for decision makers to use in evaluating department profiles, and optimizing the location of services and allocation of resources, while preserving accessibility to care and patients' preferences. The decision support matrix was also evaluated on the basis of real practice and decisions made by regional healthcare managers.

Social Network Analysis and Online Learning Communities in Higher Education: A Systematic Literature Review

This paper presents the results of a systematic literature review which sets out to explore the use of social network analysis (SNA) for investigating online learning communities in higher education. The impetus for such a review originated from an increased interest by researchers in SNA techniques to investigate interactions and learning engagement in various types of online communities. However, the researchers often omit to ground their research and SNA methods in community based learning frameworks such as communities of inquiry (CoI) and communities of practice (CoP). We identified a handful of studies that integrate SNA methods and key constructs from these frameworks and examined: SNA measures and corresponding theoretical constructs used; other analytical techniques used; limitations and; suggestions for further research. We found that while SNA is effective in detecting prominent participants, sub-groups and certain aspects of a CoP, a specific SNA measure cannot be correlated with a particular presence in a CoI. Therefore, SNA needs to be complemented with a qualitative analytical technique. Whether SNA can be used as a stand-alone technique for identifying communities remains to be seen. We also find a lack of consideration to attributional and performance variables in existing studies. In conclusion, we propose the development of a fully integrated research framework for a holistic analysis of online learning and teaching.

An Innovative Network Approach to Coordinating a National Effort to Improve Cardiovascular Health: The Case of Million Hearts.

To assess the structure, content, quality, and quantity of partnerships that developed in response to a national cardiovascular health initiative, Million Hearts. This study used a social network analysis (SNA) approach to assess the Million Hearts initiative network partnerships and identify potential implications for policy and practice. The Million Hearts network comprised a core group of federal and private sector partners that participate in Million Hearts activities and align with initiative priorities. To bound the network for the SNA, we used a list of 58 organizations (74% response rate) from a previously completed qualitative analysis of Million Hearts partnerships. We used the online PARTNER (Program to Analyze Record and Track Networks to Enhance Relationships-www.partnertool.net) survey to collect data on individual organizational characteristics and relational questions that asked organizations to identify and describe their relationships with other partners in the network. Key SNA measures include network density, centralizations, value, and trust. Our analyses show a network that is decentralized, has strong perceptions of trust and value among its members, and strong agreement on intended outcomes. Interestingly, partners report a desire and ability to contribute resources to Million Hearts; however, the perceptions between partners are that resources are not being contributed at the level they potentially could be. The majority of partners reported that being in the network helped them achieve their goals related to cardiovascular disease prevention. The largest barrier to successful activities within the network was cited as lack of targeted funding and staff to support participation in the network. The Million Hearts network described in this article is unique in its membership at the national level, agreement on outcomes, its powerful information-sharing abilities that require few resources, and its decentralized structure. We identified strategies that could be implemented to strengthen the network and its activities. By examining a national-level public-private partnership formed to address a public health issue, we can identify ways to strengthen the network and provide a framework for developing other initiatives.

Social Network Analysis (SNA): A Vision for Counter-Terrorism Approach in Modern Indian Defence Sector

Terrorism in India as well as worldwide has evolved numerous techniques for analyzing terrorist groups and networks. In addition to active on-field approaches for counter-terrorism, Social Network Analysis (SNA) is the one of the major alternative approach for Defence Sector. In this paper we discuss various approaches of SNA for counter-terrorism. This study mainly focuses on various measures of SNA for identifying leaders or key nodes in terrorist networks. We also discusses several data collection techniques for gathering the network data. Our comparative study of several SNA tools revealed the viability for investigating various networks. Case studies on various Terrorist Networks set a vision for Modern Indian Defence Sector to use SNA as a practical, operational and analytical tool for Counter-Terrorism and counter insurgency operations.

Defensive interactions in soccer small-sided games: an integrated approach between the fundamental tactical principles and the social network analysis

This study aimed to 1) present a new analysis of the interactions between the tactical principles of defensive coverage and delay using the Social Network Analysis (SNA); 2) compare the defensive cooperation patterns presented by players of different categories during a 3vs.3 soccer small-sided games SSG; 3) compare the level of defensive prominence presented by defenders, midfielders, and forwards in 3vs.3 SSG within and between different categories. Twenty-eight soccer athletes from U-13 (n=14) and U-14 (n=14) categories of a sports club performed 3vs.3 SSG for the analysis of the defensive tactical principles. Defensive interactions were considered successful defensive coverage and a delay actions performed within the same time interval. Macro (density and clustering coefficient) and micro (degree centrality, degree prestige and page rank) analyses were used as SNA measures. Results indicated no significant differences between categories for the macro and micro analyses. Only in the U-14 category, midfielders presented higher prominence levels than the other playing positions (p=0.004). We concluded that U-13 and U-14 athletes are not different regarding defensive cooperation patterns. A higher positional tactical knowledge obtained through deliberate practice is essential to induce different defensive interactions between playing positions, as shown by significant differences only in the U-14 category.

Discover opinion leader in online social network using firefly algorithm

Nowadays, with the widespread access to web 2.0, the social network plays an unbelievable role in knowledge sharing and diffusion of new products. People can share their views and can visit other's opinion about the particular material, news, products, artifacts and, trends, etc. anywhere, anytime, and anywhere. An Opinion leader is a critical person who can change, modify and transform other's view by their knowledge and proficiency. In this article, an innovative approach is proposed to discover the top-N local and global opinion leader within the community and social network respectively. Initially, we identified the community structure within the social network using the modified Louvain method and next identified the opinion leader using a modified firefly algorithm in each community. We also determined the global opinion leader within the same social network using the same firefly algorithm. The proposed approach is exceptionally supportive to expert and intelligent system because it competently discovered the local optimum concurrently in each subgroup of the social network. All the users can update its attractiveness value without any supposition, and as soon as the distance among the user's increases, the other users can automatically create another subgroup in the network and form the local community. In addition, as the population size in the network increases, the entire users measure their prominence simultaneously. Therefore, there is no consequence on computational time and accuracy of the algorithm. Thus, the proposed algorithm is superlative suitable for discovering the opinion leader in the local community and globally in the social network. For legalized the proposed approach, we implemented our proposed method on synthesized as well as on real dataset. Finally, we concluded that both the recommended procedures are much better concerning the accuracy, precision, recall, and F1-score with the widely used standard Social Network Analysis (SNA) measures.

A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies.

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network-generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error-prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called “ontology” that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

Influence of Learning Design of the Formation of Online Communities of Learning

This paper presents the findings of a study on a fully online Bachelor’s level course in Health Sciences at a European University conducted to explore the influence of learning design on the formation and evolution of different types of communities of learning. The impetus for the study came from the well-established effectiveness of community-based learning, a need for understanding learning design and analytics within networked structures and, the lack of theoretical grounding for social network analysis (SNA) in previous literature. Our study uses the Integrated Methodological Framework (IMF) which employs SNA as the key methodology for exploring community-based learning in light of the Communities of Practice (CoP) and Community of Inquiry (CoI) frameworks. The course comprised of three differently designed successive discussion forums spanning three weeks each. Network diagrams and SNA measures clearly showed the impact of the different learning designs on student engagement in the discussion forums. Based on CoP and CoI structural components within the IMF, a comparative analysis of whole-network properties of the three networks indicated the formation of a CoP, initiated and mediated by the tutor in discussion 1, sustained by the students in discussion 2, and disintegrated due to lack of guidance and facilitation in discussion 3. Qualitative analysis on the content of discussion posts revealed the importance of group oriented messages in the formation of the CoP. The paper discusses findings in terms of implications for learning design and analytics in online learning and the role of the tutor in community formation.

Identifying online communities of inquiry in higher education using social network analysis

This article presents findings from a case study on a fully online bachelor’s level course at an Australian University. The study was undertaken to demonstrate the effectiveness of the integrated methodological framework (IMF) in structurally exploring and identifying online communities of inquiry (CoI). The IMF employs social network analysis (SNA) as the key methodology for exploring community-based learning in light of the communities of practice (CoP) and CoI frameworks. The case study was conducted on two offerings of the same online course with some variations in the design. In line with the intentions of the lecturer to engage students in a CoI, the course included guided, facilitated, and graded weekly discussion activities. On application of the IMF, network diagrams and SNA measures clearly showed the impact of the different learning designs on student online engagement within the discussion forums in each semester. Based on structural components of a CoI within the IMF, a comparative analysis of the networks obtained indicated the formation of an unidentified community in S2 and a CoI in S3. The article discusses findings in terms of effectiveness of the IMF, impact of learning design on community formation and learning analytics in online learning. Published: 9 June 2018 Citation: Research in Learning Technology 2018, 26 : 2064 - http://dx.doi.org/10.25304/rlt.v26.2064