Teachers see high workload, habits, low-quality data and skills, and predisposition as the main constraints on data-informed analysis, and none regularly collect teaching data¶

Claim · All claims
Evidence · 1 study · design r3 · q2
Currency · newest study 2022 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? Teachers named high workload, habits/routine, low-quality data and skills, and predisposition as main reasons for low adoption of data-informed analysis. → Saar 2022 q2 i? Although most teachers analyze their teaching mentally, none of the interviewed participants regularly collect data about their teaching. → Saar 2022

Evidence¶

Saar 2022¶

Saar, M., Rodríguez-Triana, M. J., & Prieto, L. P. (2022). Towards Data-Informed Teaching Practice: A Model for Integrating Analytics with Teacher Inquiry. Journal of Learning Analytics, 9(3), 88–103. https://doi.org/10.18608/jla.2022.7505

q2 · i? · design · r3

Open-ended interview question (Q10) in the think-aloud iteration with ten teachers, analyzed thematically. The article also states that "none of the participants regularly collect data about their teaching" despite admitting regular self-analysis should be routine.

"the main constraints were seen as high workload (n=4), habits/routine (n=3; "comfort, fear of anything new"), low-quality data and skills (n=2; "e-school analytics are not efficient," "I get stuck, need training"), and predisposition (n=3"

Discussion¶

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