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In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes’ centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.

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University student networks are recognised to be linked with student performance. Yet, no literature review seems to address student networks together with student learning or achievement. This paper focuses on the quantitative studies conducted since 2000 that relate to this issue. This literature review highlights five research domains: (1) the links between student and peer performance within face‐to‐face networks; (2) the effects of networks’ components (e.g., centrality) on student learning and achievement within face‐to‐face networks; (3) the impacts of online social network use on student performance; (4) the effects of social presence and of interactions within e‐learning; and (5) the effects of e‐learning networks components. This literature review underlines inconsistent findings within each of these research domains. This paper leads to a discussion on the methodological issues that might explain these inconsistencies and to research questions still to be covered when studying student networks in relation with education outcomes. Rationale for this study Academic success is not to be taken for granted. There is a need for understanding why some students succeed and others fail at university. Many studies focused on the relationships between achievement at university and student networks. To this day, no literature review exists regarding those links. Furthermore, in social networks analysis (SNA), scientists developed quantitative methodologies to describe networks. SNA techniques include, for instance, computing nodes’ centrality. There is a need to discuss how such quantitative methodologies are and could be implemented in education research. Why the new findings matter This knowledge about relationships between college students’ networks and learning, performance or academic achievement should help develop and implement policies that promote student success at university. Implications for educational researchers and policy makers Education researchers have to be cautious about some methodological challenges encountered when studying networks. Also, alternative centrality measures—that is, those less investigated or not investigated—might be valid candidates for representing the importance of students within networks.With respect to educational policy development: Mixing student abilities within classes appears to be a better option than grouping those abilities, but student prior performance has to be taken into account, as linked to the student type that would most benefit from heterogeneity of performance; Educational practitioners should ensure the sharing and dissemination of relevant and accurate resources and information in student (online) networks; We encourage education professionals to analyse how one might effectively integrate online social platforms into instruction, and to educate students about the adverse effects of misusing these platforms; We recommend education practitioners pay attention to students’ isolation feelings in e‐learning settings; In e‐learning settings, e...

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