What the study found
The study found that computer-based formative assessment data can predict later standardized assessment performance to a moderate degree. The best model used mean abilities across different competence domains and explained 30–48% of the variance, although past standardized assessment measures explained more.
Why the authors say this matters
The authors say these findings offer insights into how learning progress connects to later achievement. They suggest this may help teachers adapt instruction earlier and inform policies that reduce reliance on high-stakes testing.
What the researchers tested
The researchers estimated student abilities in a large sample of children at different points during compulsory schooling. They then compared regression models that predicted standardized assessment abilities from different subsets of features derived from formative assessment abilities and auxiliary variables.
What worked and what didn't
A model including mean abilities in different competence domains performed best. The most predictive formative assessment features generally came from the same or a similar competence domain as the standardized assessment ability being predicted, and the models showed systematic biases that the authors say should be considered in decision-making.
What to keep in mind
The abstract does not describe all model details or the exact nature of the systematic biases. It also notes that predictive performance was still below that of past standardized assessment measures.
Key points
- Computer-based formative assessment data predicted standardized assessment outcomes.
- The best model used mean abilities across competence domains.
- That model explained 30–48% of the variance.
- Past standardized assessment measures were more predictive than the formative assessment models.
- Predictive features usually matched the same or a similar competence domain.
- The authors reported systematic model biases.
Disclosure
- Research title:
- Formative assessment data predicted standardized assessment performance
- Authors:
- Benjamín Garzón, Stéphanie Berger, Charles Driver, Martin J. Tomasik
- Institutions:
- Kantonsschule Enge, Kantonsschule Enge, Kantonsschule Enge
- Publication date:
- 2026-02-23
- OpenAlex record:
- View
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