Takii (2026) — Author. Unknown institution. 📄 Full text (arXiv)
Attempts to establish a metaphysical foundation for Learning Analytics (LA) by addressing the ontological question of what LA fundamentally is. Despite 15 years of development since the first LAK conference, metaphysical discussions of LA have been sparse. The paper identifies eight agents (including learners) as ontological prerequisites for LA and uses the is/ought problem to argue that LA cannot derive normative claims from data alone.
Critical contribution: Defines norm-embedded LA as a class of practices where LA's purpose (improving learning) is conflated with its operations (measuring behavior), creating an ontological tension. Challenges the field to clarify its identity from internal principles rather than external frameworks.
While speculative and lacking empirical validation (hence low confidence), the paper provides useful philosophical grounding for debates about AI's role in analytics, data ethics, and normative claims in educational technology.
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