Toward a Metaphysics of Learning Analytics: Ontological Positioning of Data, Inference, and Normativity

2026-06-04

AI Summary

This study addresses the longstanding absence of systematic ontological inquiry in learning analytics, which has obscured the field’s core identity and foundational principles. By revisiting its definitions and first principles, the paper constructs an internal metaphysical framework that clarifies the ontological status of data and identifies eight categories of ontological entities. Drawing on Hume’s is–ought distinction, it delineates the boundary between descriptive facts and normative prescriptions. The analysis reveals an ontological tension between “norm-embedded learning analytics” and the field’s first principles, demonstrating that normative claims cannot be directly derived from data alone. This distinction sharpens the separation between operational mechanisms and purpose-driven objectives, thereby establishing a coherent and rigorous ontological foundation for the discipline.

Abstract

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.

Problem

• Learning Analytics
• Metaphysics
• Ontology
• Normativity
• Data

Innovation

• metaphysics of learning analytics
• ontological positioning
• norm-embedded LA
• is/ought problem
• data ontology