SOCIAL NETWORK ANALYSIS: AN INTRODUCTION AND APPLICATION TO STEM EDUCATION RESEARCH

2019 ◽  
Author(s):  
Kathleen Quardokus Fisher ◽  
2020 ◽  
Vol 44 (1) ◽  
pp. 244-268 ◽  
Author(s):  
Dominik E. Froehlich ◽  
Sara Van Waes ◽  
Hannah Schäfer

Social network analysis (SNA) is becoming a prevalent method in education research and practice. But criticism has been voiced against the heavy reliance on quantification within SNA. Recent work suggests combining quantitative and qualitative approaches in SNA—mixed methods social network analysis (MMSNA)—as a remedy. MMSNA is helpful for addressing research questions related to the formal or structural side of relationships and networks, but it also attends to more qualitative questions such as the meaning of interactions or the variability of social relationships. In this chapter, we describe how researchers have applied and presented MMSNA in publications from the perspective of general mixed methods research. Based on a systematic review, we summarize the different applications within the field of education and learning research, point to potential shortcomings of the methods and its presentation, and develop an agenda to support researchers in conducting future MMSNA research.


2017 ◽  
Vol 43 (2) ◽  
pp. 225-253 ◽  
Author(s):  
Samrachana Adhikari ◽  
Beau Dabbs

In education research, social network analysis is being widely used to study different interactions and their overall implications. Recently, there has also been a surge in the development of software tools to implement social network analysis. In this article, we review two popular R packages, igraph and statnet suite, in the context of network summarization and modeling. We discuss different aspects of these packages and demonstrate some of their functionalities by analyzing a friendship network of lawyers. Finally, we end with recommendations for using these packages along with pointers to additional resources for network analysis in R.


2019 ◽  
Author(s):  
Dominik Emanuel Froehlich ◽  
Sara van Waes ◽  
Hannah Schäfer

Over the past three decades, educational research, policy, and practice have become increasingly interested in relationships and collaboration. In response, social network analysis (SNA) emerged as a theoretical and methodological framework, offering tools to explore relationships in depth. Compared to then existing approaches, SNA allows capturing relationships in a more nuanced way, by focusing on the patterns and qualities of relationships (Borgatti, Mehra, Brass, & Labianca, 2009). SNA offers a valuable perspective for examining whether and to what degree interaction and collaboration take place in education. Another key strength of SNA is that it offers several tools to visualize relationships (Hogan, Carrasco, & Wellman, 2007), which not only creates opportunities for (visual) research but also for practice (e.g., for intervention and feedback purposes). The potential of SNA is reflected in a surge in publications from 37 in 2003 to more than 400 a decade later in the Education Resources Information Center (ERIC; Froehlich, Rehm, & Rienties, 2019). SNA has established its usefulness in various educational sub-fields, for instance, in examining the role of relationships for student achievement (Moolenaar, Sleegers, & Daly, 2012), reform and improvement (Penuel, Bell, Bevan, Buffington, & Falk, 2016), policy implementation (Coburn, Russell, Kaufman, & Stein, 2012), and leadership (Spillane & Shirrell, 2017). No other methodological framework is that much focused on the in-depth exploration of the roles of relationships and structures in learning and instruction (Moolenaar, 2012; Sweet, 2016). The surge in SNA publications across the academic disciplines is largely driven by quantitative SNA studies (Freeman, 2004). Despite its merits, this formalized approach to network analysis has been criticized for a lack of attention to the qualitative aspects of relationships (Fuhse & Mützel, 2011; Hollstein, 2011). Recent work convincingly addresses these concerns by combining quantitative and qualitative approaches. These approaches succeed in addressing research questions not only related to the formal or structural side of relationships and networks. They also attend to questions related to the actual content and meaning of interactions, the (day to day) variability of social relationships, the developments of nodes and ties, and the idea of agency (Crossley, 2010; Crossley & Edwards, 2016).In this article, we posit that mixing methods within SNA is an original innovation that will help to answer new sets of research questions in education research (Bolíbar, 2015; Domínguez & Hollstein, 2014). We argue that a systematic review of mixed method social network analysis (MMSNA) is needed (1) to offer an overview of the existing body of work in education, (2) to show the merits of this approach, and (3) to develop a set of pointers for conducting rigorous MMSNA research and to support scholars in conducting future MMSNA research.


Author(s):  
Aras Bozkurt ◽  
Ela Akgun-Ozbek ◽  
Sibel Yilmazel ◽  
Erdem Erdogdu ◽  
Hasan Ucar ◽  
...  

<p>This study intends to explore the current trends in the field of distance education research during the period of 2009-2013. The trends were identified by an extensive review of seven peer reviewed scholarly journals: <em>The American Journal of Distance Education</em> (AJDE), <em>Distance Education</em> (DE), <em>The European Journal of Open, Distance and e-Learning</em> (EURODL), <em>The Journal of Distance Education</em> (JDE), <em>The Journal of Online Learning and Technology</em> (JOLT), <em>Open Learning: The Journal of Open, Distance and e-Learning</em> (OL) and <em>The International Review of Research in Open and Distributed Learning</em> (IRRODL). A total of 861 research articles was reviewed. Mainly content analysis was employed to be able to analyze the current research. Also, a social network analysis (SNA) was used to interpret the interrelationship between keywords indicated in these articles. Themes were developed and the content of the articles in the selected journals were coded according to categories derived from earlier studies. The results were interpreted using descriptive analysis (frequencies) and social network analysis. The reporting of the results were organized into the following categories: research areas, theoretical and conceptual frameworks, variables, methods, models, strategies, data collection and analysis methods, and the participants. The study also identified the most commonly used keywords, and the most frequently cited authors and studies in distance education. The findings obtained in this study may be useful in the exploration of potential research areas and identification of neglected areas in the field of distance education.  </p>


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