Network Theory

Borgatti, Stephen P.; Lopez-Kidwell, Virginie

doi:10.4135/9781446294413.n4

Cited by 133 publications

(142 citation statements)

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“…As network analysis is relatively new in physics education research, it is worthwhile to consider how data are collected and how one would interpret the meaning of these data. Further, the interpretation of the meaning of a link is an ongoing debate among social network analysis researchers [22]. In the following sections, we describe two theoretical perspectives that are employed for the interpretation of the data we have collected.…”

Section: Introductionmentioning

confidence: 99%

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Bruun

Brewe

2013

Phys. Rev. ST Phys. Educ. Res.

115

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The role of student interactions in learning situations is a foundation of sociocultural learning theory, and social network analysis can be used to quantify student relations. We discuss how self-reported student interactions can be viewed as processes of meaning making and use this to understand how quantitative measures that describe the position in a network, called centrality measures, can be understood in terms of interactions that happen in the context of a university physics course. We apply this discussion to an empirical data set of self-reported student interactions. In a weekly administered survey, first year university students enrolled in an introductory physics course at a Danish university indicated with whom they remembered having communicated within different interaction categories. For three categories pertaining to (1) communication about how to solve physics problems in the course (called the PS category), (2) communications about the nature of physics concepts (called the CD category), and (3) social interactions that are not strictly related to the content of the physics classes (called the ICS category) in the introductory mechanics course, we use the survey data to create networks of student interaction. For each of these networks, we calculate centrality measures for each student and correlate these measures with grades from the introductory course, grades from two subsequent courses, and the pretest Force Concept Inventory (FCI) scores. We find highly significant correlations (p < 0:001) between network centrality measures and grades in all networks. We find the highest correlations between network centrality measures and future grades. In the network composed of interactions regarding problem solving (the PS network), the centrality measures hide and PageRank show the highest correlations (r ¼ À0:32 and r ¼ 0:33, respectively) with future grades. In the CD network, the network measure target entropy shows the highest correlation (r ¼ 0:45) with future grades. In the network composed solely of noncontent related social interactions, these patterns of correlation are maintained in the sense that these network measures show the highest correlations and maintain their internal ranking. Using hierarchical linear regression, we find that a linear model that adds the network measures hide and target entropy, calculated on the ICS network, significantly improves a base model that uses only the FCI pretest scores from the beginning of the semester. Though one should not infer causality from these results, they do point to how social interactions in class are intertwined with academic interactions. We interpret this as an integral part of learning, and suggest that physics is a robust example.

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Section: Introductionmentioning

confidence: 99%

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Bruun

Brewe

2013

Phys. Rev. ST Phys. Educ. Res.

115

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“…With respect to direction of causality, research in design and most other fields has generally examined how network structure affects behaviour and not vice versa (Borgatti and Halgin 2011). In design research, this can be explained by the fact that most dependent variables are associated with performance measures of sort, either related to the designer (e.g.…”

Section: Define Overall Research Purposementioning

confidence: 99%

Using Network Science to Support Design Research: From Counting to Connecting

Parraguez

Maier

2016

Experimental Design Research

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“…This inverse is called the closeness centrality, , and is defined as (5) Betweenness centrality measures the extent to which a vertex i lies on paths between other vertices (Freeman, 1977;Newman, 2010, p.185). This centrality measure is based on the network flow model (Borgatti and Lopez-Kidwell, 2011), where information or a resource flows from vertex to vertex along paths. Freeman (1977) made the simple assumption that every pair of vertices connected by a path in the network exchanges information with equal probability per unit time and that information always takes the shortest path-or randomly chooses one of equal shortest paths-through the network.…”

Section: Centrality In Directed Networkmentioning

confidence: 99%

Can System Dynamics Learn from Social Network Analysis?

Schoenenberger

Schenker‐Wicki

2015

SSRN Journal

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This article deals with the analysis of large or complex system dynamics (SD) models, exploring the benefits of a multimethodological approach to model analysis. We compare model analysis results from SD and social network analysis (SNA) by deploying SNA techniques on a pertinent example from the SD literature-the world dynamics model. Although SNA is a clearly distinct method from SD in that it focuses on social actors and their interrelationships, we contend that SD can indeed learn from SNA, particularly in terms of model structure analysis. Our argumentation follows renowned system dynamicists who acknowledge the potential of SD to synthesize and advance theories in social science at both the conceptual and technical levels. Can System Dynamics learn from Social

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Network Theory

Cited by 133 publications

References 0 publications

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores

Using Network Science to Support Design Research: From Counting to Connecting

Can System Dynamics Learn from Social Network Analysis?

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