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

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Educational Networking

Part of the book series: Lecture Notes in Social Networks ((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.

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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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Authors and Affiliations

  1. University of Vienna, Vienna, Austria

    Dominik Froehlich

  2. Pädagogische Hochschule Weingarten, Weingarten, Germany

    Martin Rehm

  3. Open University, Milton Keynes, UK

    Bart Rienties

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  1. Dominik Froehlich
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  2. Martin Rehm
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  1. WOLNM, Artificial Intelligence on Education Lab, Mexico City, Mexico, Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco, Mexico City, Mexico

    Alejandro Peña-Ayala

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Froehlich, D., Rehm, M., Rienties, B. (2020). Reviewing Mixed Methods Approaches Using Social Network Analysis for Learning and Education. In: Peña-Ayala, A. (eds) Educational Networking. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-29973-6_2

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