For training: Programs in leadership & policy need #EducationDataScience roadmaps. Future leaders must question & apply analytics responsibly.
👉 Preprint: doi.org/10.7916/amq5...

#EdPolicy #SchoolLeadership

For research: We need more than model fit. Test prediction accuracy. Build open, reproducible workflows connecting theory, equity & application.
#EducationResearch #DataScience #OpenScience

For practice: Move beyond dashboards ➡️ build leader-analyst partnerships. Joint sensemaking drives effective evidence-based improvement cycles.
#SchoolLeadership #DataDriven #Education

The 4As of algorithms in education:
✔ Accurate
✔ Accessible
✔ Actionable
✔ Accountable
Without them, data systems risk reinforcing inequities.
#DataEthics #EdTech #Education

Key insight: Prediction matters. Leaders don’t just want p-values. They need accurate, actionable forecasts to guide #EdPolicy & practice.
#EducationResearch #LearningAnalytics #EdTech

In this paper, I argue that education #datascience is a “third methodology” in research—joining quantitative & qualitative approaches. It combines prediction, visualization, and ethics with deep domain knowledge.

The Open Boarders plan?

Results seem to align with this paper: K.R. Koedinger,P.F. Carvalho,R. Liu,& E.A. McLaughlin, An astonishing regularity in student learning rate, Proc. Natl. Acad. Sci. U.S.A. 120 (13) e2221311120, doi.org/10.1073/pnas... (2023).