scholarly journals Keyword, Field, and Social Network Analysis Trends for K-12 Engineering Education Research

2020 ◽  
Author(s):  
Mallory Lancaster ◽  
Yi Luo ◽  
Johannes Strobel
Keyword(s):  
Education Research ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Engineering Education ◽  
K 12 ◽  
Engineering Education Research

scholarly journals A Framework for Quality K-12 Engineering Education: Research and Development

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Tamara J. Moore ◽  
Aran W. Glancy ◽  
Kristina M. Tank ◽  
Jennifer A. Kersten ◽  
Karl A. Smith ◽  
...  
Keyword(s):  
Education Research ◽  
Research And Development ◽  
Engineering Education ◽  
K 12 ◽  
Engineering Education Research

scholarly journals Linking Quantitative and Qualitative Network Approaches: A Review of Mixed Methods Social Network Analysis in Education Research

2020 ◽  
Vol 44 (1) ◽  
pp. 244-268 ◽  
Author(s):  
Dominik E. Froehlich ◽  
Sara Van Waes ◽  
Hannah Schäfer
Keyword(s):  
Education Research ◽  
Social Network ◽  
Mixed Methods ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Mixed Methods Research ◽  
Qualitative Approaches ◽  
Research Questions ◽  
Network Approaches

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.

scholarly journals Social Network Analysis in R: A Software Review

2017 ◽  
Vol 43 (2) ◽  
pp. 225-253 ◽  
Author(s):  
Samrachana Adhikari ◽  
Beau Dabbs
Keyword(s):  
Education Research ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Software Tools ◽  
Software Review ◽  
Friendship Network ◽  
R Packages

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.

scholarly journals Interaction, critical thinking, and social network analysis (SNA) in online courses

2013 ◽  
Vol 14 (3) ◽  
pp. 294 ◽  
Author(s):  
Joan Thormann ◽  
Samuel Gable ◽  
Patricia Seferlis Fidalgo ◽  
George Blakeslee
Keyword(s):  
Critical Thinking ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Courses ◽  
Online Course ◽  
Constructivist Approach ◽  
Use Of Technology ◽  
Instructor Presence ◽  
K 12

<p>This study tried to ascertain a possible relationship between the number of student moderators (1, 2, and 3), online interactions, and critical thinking of K-12 educators enrolled in an online course that was taught from a constructivist approach. The course topic was use of technology in special education. Social network analysis (SNA) and measures of critical thinking (Newman, Webb, &amp; Cochrane, 1995) were used to research and assess if there was a difference in interaction and critical thinking between 1, 2, or 3 student moderators who facilitated a forum discussion of an assignment in an online course. The same course was repeated over three years. Each year either 1, 2, or 3 students moderated. The analysis indicated more discussion per non-moderating student with the three student moderated group. Using SNA we found that there was only one noticeable difference among the three groups which was in the value of network centralization. Using critical thinking measures the three student moderator group scored higher in five of the eight critical thinking categories. Variations in instructor presence in the online courses may have influenced these findings.</p>

scholarly journals Linking Quantitative and Qualitative Network Approaches: A Review of Mixed Methods Social Network Analysis in Education Research

2019 ◽  
Author(s):  
Dominik Emanuel Froehlich ◽  
Sara van Waes ◽  
Hannah Schäfer
Keyword(s):  
Education Research ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Policy ◽  
Academic Disciplines ◽  
Methodological Framework ◽  
Policy And Practice ◽  
Qualitative Approaches ◽  
Research Questions

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, &amp; 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, &amp; 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, &amp; 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, &amp; Daly, 2012), reform and improvement (Penuel, Bell, Bevan, Buffington, &amp; Falk, 2016), policy implementation (Coburn, Russell, Kaufman, &amp; Stein, 2012), and leadership (Spillane &amp; 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 &amp; 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 &amp; 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 &amp; 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.

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