Networks as integrated in research methodologies in PER

Bruun, Jesper

doi:10.1119/perc.2016.plenary.002

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…A similar research methodology, social network analysis, has emerged in recent years in biology and physics education research (Grunspan et al. , 2014; Bruun, 2016). However, social network analysis and graph theory are not the same, even though their names are often used interchangeably in the literature (Zweig, 2016).…”

Section: Methodological Frameworkmentioning

confidence: 99%

Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning

Chai

Lee

et al. 2019

LSE

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Group work in science, technology, engineering, and mathematics courses is an effective means of improving student outcomes, and many different factors can influence the dynamics of student discussions and, ultimately, the success of collaboration. The substance and dynamics of group discussions are commonly examined using qualitative methods such as discourse analysis. To complement existing work in the literature, we developed a quantitative methodology that uses graph theory to map the progression of talk-turns of discussions within a group. We observed groups of students working with peer facilitators to solve problems in biological sciences, with three iterations of data collection and two major refinements of graph theory calculations. Results include general behaviors based on the turns in which different individuals talk and graph theory parameters to quantify group characteristics. To demonstrate the potential utility of the methodology, we present case studies with distinct patterns: a centralized group in which the peer facilitator behaves like an authority figure, a decentralized group in which most students talk their fair share of turns, and a larger group with subgroups that have implications for equity, diversity, and inclusion. Together, these results demonstrate that our adaptation of graph theory is a viable quantitative methodology to examine group discussions.

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Section: Methodological Frameworkmentioning

confidence: 99%

Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning

Chai

Lee

et al. 2019

LSE

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“…Th e purpose of such an algorithm is reduce the apparent complexity of nodes and their connections by creating a map that captures essential larger scale structures in the network. Th e methodology is year,... concern for wolf survival visualized by Lindahl, Bruun, & Linder (2016) in a poster and is briefl y described by Bruun (2016). Here we fi rst describe the context and then provide an overview of the methodology.…”

Section: Analysis Of G Roup Discussionmentioning

confidence: 99%

“…In the case of the discussion visualized in Figure 3, the interpretations were changed from a one-sided discussion to a tug-of-war system. Th e fi nal map shows two distinct subsystems -one centered on the theme concern for wolf survival and one with two prominent themes, which has to do with a simple solution to the inbreeding of wolves: eff ective fences and obstacle: #reindeers -arising from issues relating to Table 2 Aff ordances and challenges of network analysis (Bruun, 2016)…”

Section: Analysis Of G Roup Discussionmentioning

confidence: 99%

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Network Analysis as a Research Methodology in Science Education Research

Bruun¹,

Evans²

2018

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With three examples, we explore diff erent ways of conceptualizing networks of nodes and links as educationally relevant entities. We show how one can analyse networks as they evolve over time − the dynamics of networks − and how one can model dynamic processes on networks. We also explain how networks can have both visual and mathematical properties that make them tractable as a way of generating knowledge about relational data. We suggest how a theory that emphasizes relational aspects might be developed using networks by providing an example about social networks, where we explain the generation of the theory-like structures rules of interaction, which are meant to have explanatory power. Likewise, we make interpretations of student discussions that are shaped in part by the algorithm we use to create discussion maps as well as by the changes we make to the transcript. Th e article illustrates how in a teacher-student dialogue, the structure of the network shapes the way we comprehend and talk about discussion networks. All these are dependent on the relational character of networks and would not be the same without that perspective. In other words, the theoretical positions we develop are shaped by the nature of networks. We conclude the article by discussing three developments, which need to occur to realize the potential of using network analysis in educational research.

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“…Learning analytics research has already made strides in investigating some elements of this but more empirical work, especially in relation to various designs, is required (Wise & Cui, 2018). Moreover, the domain of learning analytics enables us to record even more granular types of networks by capturing diverse student actions (Bruun, 2016). Alternately, where transcripts of student talk are available, text mining tools provide ways to incorporate the character of these conversations in the network structure, as learning analytics has also adopted (Joksimović et al, 2018(Joksimović et al, , 2019.…”

Section: Future Directionsmentioning

confidence: 99%

Networks and Learning: A View from Physics

Traxler

2022

Learning Analytics

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Like learning analytics, physics education research is a relatively young field that draws on perspectives from multiple disciplines. Network analysis has an even more heterodox perspective, with roots in mathematics, sociology, and, more recently, computer science and physics. This paper reviews how network analysis has been used in physics education research and how it connects to some of the work in this special issue. Insights from physics education research suggest combining social and interaction networks with other data sources and looking for finer-grained details to use in constructing networks, and learning analytics is promising for both avenues. The discussion ends by looking at the complications with incorporating gender into network analysis, and finally the possibilities for the future.

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Networks as integrated in research methodologies in PER

Cited by 9 publications

References 9 publications

Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning

Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning

Network Analysis as a Research Methodology in Science Education Research

Networks and Learning: A View from Physics

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