Researching and Understanding Educational Networks

McCormick, Robert; Fox, Alison; Carmichael, Patrick; Procter, Richard

doi:10.4324/9780203849170

Cited by 30 publications

(34 citation statements)

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“…In other words, a basic understanding of a network shapes how we interpret centrality measures, node attributes, and correlations between them. McCormick et al describe the mechanism by which nodes influence each other in a network by describing a link as representing a process of meaning making [36]. 3 From our point of view, this interpretation best applies to our study: we asked students to recall interactions, so links represent that the student has identified a relationship with another student.…”

Section: Theory: Network Meaning and Structurementioning

confidence: 99%

“…We take this to reflect what Wenger terms as mutual engagement [2]. The named student engaged with the other students in problem solving practices, which, to some extent, were part of a process of meaning making [36]. The student might have been mentioned for several reasons, although if he or she never added to the meaningmaking process of others, it is difficult to understand why other students would continue naming him or her.…”

Section: Problem Solving Network Correlationsmentioning

confidence: 99%

“…In contrast to the linear view, recent theories of learning (e.g., Refs. [2,25,36,51,52]) conceive learning as an activity involving many kinds of processes within and between learners and artifacts using multiple modalities. As a consequence, it seems very difficult to conceive of a linear model of learning that could incorporate the development of a shared repertoire of problem solving practices and how this repertoire manifests itself in student performances.…”

Section: B Linear Modelingmentioning

confidence: 99%

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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: Theory: Network Meaning and Structurementioning

confidence: 99%

Section: Problem Solving Network Correlationsmentioning

confidence: 99%

Section: B Linear Modelingmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Discussion of such 'out-of-school' or 'outside' school opportunities offers recognition of a broader conceptualisation of the notion of 'place' in the term 'workplace', more usually limited to the site, buildings and community of the immediate school (also noted by Amin & Roberts, 2008). Here, and consistent with others researching teachers in school-University partnerships and inter-school collaborations, thinking conceptualised in terms of networks rather than communities can be used to frame this broader view of sites for teacher learning (Lieberman 2000;McCormick et al 2010;McLaughlin et al 2008).…”

Section: Developing Notions Of Workplacementioning

confidence: 99%

“…Networks are held to be a way of thinking about the connectivity and interactions between individuals, groups, organisations and, in some variants of network theory, also non-human actants. The educational research literature informing network perspectives does not yet present a coherent theoretical framework because conceptualisations are being drawn from a range of distinct analytical fields such as business, anthropology and sociology (McCormick et al, 2010). A particular branch of network theorists considers a network as an entity, for example Lieberman, who sees such networks as providing an organisational structure independent of, yet incorporating schools and/or universities (Lieberman 2000).…”

Section: A Network Approach To Workplace Learningmentioning

confidence: 99%