Decide what stays constant and what varies, based on the research goal, before scaling across contexts¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that "Scalable implementation requires thoughtful decisions about which aspects of the learning environment should remain constant and which should vary, based on the research goal, before implementing the system across multiple contexts". For early-warning models, holding curriculum and assessment constant lets behaviors reflect individual differences; for recommending materials, holding the student population constant while varying content attributes outcome differences to the materials.

Design Implications¶

Context¶

Requirements¶

• A specified research goal chosen before implementation, since the choice of what to hold constant depends on whether the model targets individual differences or instructional content effects.

Constraints¶

• Key elements should also remain stable over a sustained period; frequent modification makes longitudinal validation difficult, as pandemic-era behavior changes illustrated.

Target Learners¶

• students across diverse institutions implementing a shared learning platform

Target Learning Goals¶

• building generalizable predictive models of learning outcomes

Affordances¶

• Modelability Three Design Principles

Related Strategies¶

• Coursekata Instrumented Online Textbook

Examples¶

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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