Research-practice-technology partnerships (RPTPs) as the ecosystem sustaining modelable systems¶

Theory · All theories
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article extends research-practice partnerships into research-practice-technology partnerships, arguing that "Creating a modelable ecosystem requires coordinated effort across the research-practice-technology partnerships (RPTPs)". Institutions support implementation at scale and long-term data collection; instructors provide contextual insight for interpreting measures; researchers bring theoretical and methodological tools; developers supply the technical infrastructure to translate insights into usable tools.

Design Implications¶

Context¶

Requirements¶

• Sustained collaboration among institutions, instructors, researchers, and developers; a platform alone handed to instructors does not guarantee adoption or interpretable data.

Constraints¶

• Measurement and scalability alone are stated to be necessary but not sufficient without the collaborative ecosystem.

Target Learners¶

• students in institutions participating in multi-stakeholder learning analytics initiatives

Target Learning Objectives¶

• sustainable, large-scale educational improvement through learning analytics

Claims¶

• Predictive models built in a modelable ecosystem generalize across institutions [+W]

Related Theories¶

• Modelability Three Design Principles
• Research-Practice-Industry Partnership (RPIP) model with four pillars for involving educators in edtech development
• The skills-first ecosystem framework
• Research-Practice-Industry Partnership (RPIP) model

Examples¶

• Decide what stays constant and what varies, based on the research goal, before scaling across contexts
• Multi-stakeholder analytics partnership using improvement science with strict data governance
• Use shared frameworks, cross-district collaboration, and state-level policy alignment to scale AI-enabled edtech

Key Sources¶

• Alice Xu, Icy (Yunyi) Zhang, Adam B. Blake and James W. Stigler. (2026). Modelability as a Strategy for Improving the Generalizability and Scalability of Predictive Models. Journal of Learning Analytics, 13(1). https://doi.org/10.18608/jla.2026.9099