Contextualizing the research problem: improving cluster analysis insights into student learning

Abstract

Cluster analysis is widely used in educational settings to gain insights into student learning. To justify their choice of clustering approach, authors often draw on methods used in earlier studies that they perceive to be similar. This can sometimes come at the expense of selecting a clustering method better suited to addressing their own study’s goals. We argue that the selection of an appropriate clustering method should be closely connected to the context of the study. We demonstrate our argument through the use of the Open University Learning Analytics Dataset (OULAD), a well-established open access educational data set. Through a review of studies citing the OULAD, we identify seven possible motivations for clustering educational data and then focus on two of these: early identification of at-risk students and identification of similar groups of learners. We discuss the educational context behind the two motivations selected, describe which variable subsets from the OULAD might best align with the specific research questions that they motivate, and illustrate how a preferred clustering method may be highly influenced and driven by specifics of the research question. For example, the desire for an expression of uncertainty in cluster membership allocation. Fully reproducible R code is provided.