Learning analytics can reliably record certain behaviors but cannot necessarily validate inferences about what learners know or are motivated to learn¶

Claim · All claims
Evidence · 1 study · design r2 · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? While learning analytics can reliably record behaviors like LMS logins or resource access, interpreting these as indicators of specific SRL processes requires careful validation. → de Barba 2025

Evidence¶

de Barba 2025¶

de Barba, P. G., Oliveira, E. A., & English, N. (2025). Development and validation of a learning analytics rubric for self-regulated learning. Education Tech Research Dev. https://doi.org/10.1007/s11423-025-10521-x

q1 · i? · design · r2

The article's literature review notes that the relationship between observable trace data and underlying SRL constructs is not always straightforward, requiring thoughtful integration of theory and empirical evidence. A systematic review by Du et al. (2023) found previous research either relies only on trace data or considers other data sources for triangulation.

"While learning analytics can reliably (i.e., consistently) record certain behaviors, such as logins to an LMS, it cannot necessarily validate inferences about what learners know or are motivated to learn (Song et al., 2025; Winne, 2020)."

Discussion¶

Related Claims¶

• Neither self-report nor eye tracking can reliably distinguish strategic from habituated behavior in empirical practice — related
• Engagement inferred from log data must be treated with caution because digital traces reflect unevenly distributed social and material conditions — related
• Course-level sample truncation can produce M-bias that helps explain difficult-to-interpret negative associations between LMS behaviour and student success — related