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Reviewing Mixed Methods Approaches Using Social Network Analysis for Learning and Education

  • Dominik Froehlich
  • Martin Rehm
  • Bart RientiesEmail author
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Across the globe researchers are using social network analysis (SNA) to better understand the visible and invisible relations between people. While substantial progress has been made in the last 20 years in terms of quantitative modelling and processing techniques of SNA, there is an increased call for SNA researchers to embrace and mix methods developed in qualitative research to understand the what, how, and why questions of social network relations. In this chapter, we will reflect on our experiences with our latest edited book called Mixed Methods Approaches to Social Network Analysis for Learning and Education, which contained contributions from 20+ authors. We will first review the empirical literature of mixed methods social network analysis (MMSNA) by conducting a systematic literature review. Secondly, by using two case studies from our own practice, we will critically reflect on how we have used MMSNA approaches. Finally, we will discuss the potential limitations of MMSNA approaches, in particular given the complexities of mastering two ontologically different methods.

Keywords

Social network analysis Mixed method MMSNA Systematic review 

Abbreviations

AD

Academic development

AMOT

Amotivated students

CET

Cognitive Evaluation Theory

CSCL

Computer-supported collaborative learning

EMER

External motivation to external regulation

EMID

External motivation to identified regulation

EMIN

External motivation to introjected regulation

IMES

Intrinsic motivation to experience stimulation

IMTA

Intrinsic motivation to accomplish

IMTK

Intrinsic motivation to know

MM

Mixed methods

MMSNA

Mixed methods social network analysis

MRQAP

Multiple regressions quadratic assignment procedure

PBL

Problem-based learning

SDT

Self-Determination Theory

SNA

Social network analysis

VLE

Virtual learning environment

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ViennaViennaAustria
  2. 2.Pädagogische Hochschule WeingartenWeingartenGermany
  3. 3.Open UniversityMilton KeynesUK

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