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The widespread adoption of digital e-learning environments and other learning technology has provided researchers with ready access to large quantities of data. Much of this data comes from discussion forums and has been studied with analytical methods drawn from social network analysis. However, within this large body of research there exists considerable variation in the definition of what constitutes a social tie, and the consequences of this choice are rarely described or examined. This paper presents findings from two distinct learning environments regarding different social tie extraction methods and their influence on the structural and statistical properties of the induced networks, and the association between measures of centrality and academic performance. Our findings indicate that social tie definitions play an important role in shaping the results of our analyses. The primary purpose of this paper is to raise awareness of the consequences that such methodological choices may have, and to promote transparency in future research. Notes for Practice• Social network analysis has been one of the most commonly applied methods within learning analytics. However, many of the common constructs and tools these methodologies employ have not been subjected to robust validation. Such concerns pertain to construct validity: namely, does a metric actually measure what it purports to measure?• In this study, we find that different social tie extraction methods influence the structural and statistical properties of the induced networks, as well as the associations between centrality measures and academic performance.• Our results emphasize not only the importance of transparency in the choice of tie definition, but also the importance of providing a justification for that choice. Given the impact that tie definitions may have, we advise that practitioners investigate a number of options to ascertain the extent to which such methodological choices can bias their results.

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