Start of a Science: An Epistemological Analysis of Learning at Scale

The Learning at Scale (L@S) conference has brought together researchers from diverse scholarly communities to design and study technologies that are explicitly meant to scale to a large number and variety of learners. Over the last three years, the L@S community has published a thematic, methodological, and bibliometric analysis to reflect on its own interests, challenges, and foundations. This paper continues the wider reflection effort and complements these two prior analyses with an epistemological analysis of the way the papers employ learning theory, evaluate evidence, and deploy statistical models. The epistemological analysis uses two methodologies: coding the full papers from the first four years for epistemological markers of interest and analyzing the network of citations from all of the full papers for dominant institutional and epistemological traditions. By combining these two methods, the present analysis reveals that most papers explicitly show their theoretical commitments, target a narrow slice of available learning theory, draw on varied academic fields in different proportions, and showcase epistemological practices in line with what philosophers of computational science observe in communities using similar model-based methods. The paper then situates these claims in wider conversations occurring in the learning sciences and philosophy of science to provide theoretical insights as well as practical recommendations for how the community can more consciously conduct and communicate its scientific endeavor.