Toward a Metaphysics of Learning Analytics: Ontological Positioning of Data, Inference, and Normativity
For the learning analytics community, this work provides a foundational metaphysical framework derived from LA's own principles, addressing a gap in philosophical discussions.
This paper develops a metaphysics of learning analytics (LA) by addressing the ontological question of what LA is, identifying eight agents as ontological prerequisites, and clarifying that LA does not derive norms from data. It reveals that norm-embedded LA creates an ontological tension with LA's first principle.
The Learning Analytics (LA) community has undergone rapid development over the 15 years since the first LAK conference was held. However, while epistemological and ethical debates regarding the philosophical foundations of LA have been vigorous, metaphysical discussions have been sparse, signifying a lack of effort to derive the identity of LA from its internal principles. In this paper, we attempt to establish a metaphysics of LA by addressing the ontological question of ``What is LA?'' We do so by tracing back to LA's own definitions and principles to derive an answer from within LA itself. Specifically, we address what kind of existence the data LA operates on constitutes, identify eight agents including learners as ontological prerequisites, and clarify, via the is/ought problem, that LA does not derive norms from data. In particular, this system reveals that a class of LA practices, here termed \textit{norm-embedded LA}, conflates LA's purpose with its operations, creating an ontological tension with the first principle. We also discuss connections with related fields and the limitations of this system. The metaphysics outlined here is not imposed from outside LA, but surfaces what LA itself has always implicitly presupposed.