The Value of Epistemic Network Analysis in Single-Case Learning Analytics: A Case Study in Lifelong Learning

Abstract

Certain educational contexts like lifelong learning have been comparatively understudied, due to the uniqueness of such learning processes, which make it difficult to ascertain generalizable models or average intervention effects. The ability to collect large amounts of data from a single learner longitudinally, throughout their lifelong trajectory, and the use of learning analytics (LA), presents novel opportunities in this regard. However, quantitative data and models may not be enough to deeply understand such unique learning processes and their context, without the help of qualitative data and ethnographic methods of collecting and analyzing it. This paper presents an approach to understanding lifelong learning that combines both qualitative and quantitative data (which we have termed single-case learning analytics). We illustrate how these two kinds of longitudinal data can be combined and the role of Epistemic Network Analysis (ENA) in this approach, through a case study in which daily quantitative and qualitative data were gathered about a lifelong learner’s social-emotional learning (SEL), for about nine months. Our results show how qualitative data (and ENA outputs) can be used to not only close the interpretive loop but also help interpret and improve the accuracy of quantitative models of a single learner’s process and contextual influences. Along with ongoing advances in automated coding, this approach opens the door to user-facing, ENA-enhanced LA to support personalized learning over extended periods of time.KeywordsLifelong learningSocial emotional learningLearning analyticsEpistemic network analysisLongitudinal dataSingle-case research designs