Student Learning, Assessment, and Data Use
This domain tests balanced assessment, data literacy, and the disciplined use of evidence to improve learning. Questions probe formative and summative purposes, validity, reliability, disaggregation, progress monitoring, and data privacy.
Core rules
- Assessment purpose drives design: formative evidence guides instruction; summative evidence certifies learning.
- Valid inferences require alignment to standards and appropriate sample size. Avoid overinterpreting small subgroup counts.
- Disaggregate by subgroup, standard, and item to locate patterns, not just averages.
- Use data teams with a cycle: question, gather, analyze, act, and monitor.
- Protect privacy: FERPA applies to education records; share only what is needed for legitimate educational purposes.
Worked example
A principal sees a 12-point reading gap for multilingual learners. She disaggregates by standard and item and finds vocabulary-in-context is weak, while main-idea items are comparable. The team adds explicit vocabulary routines, monitors weekly with a short formative probe, and reviews after six weeks. The gap narrows by five points.
Reasoning: disaggregation revealed a skill-specific problem; progress monitoring tested whether the intervention worked.
Traps
- Using a single test to judge a teacher or program.
- Confusing correlation with cause when scores change.
- Reporting averages that hide subgroup gaps.
- Collecting data without a decision rule.
- Sharing identifiable data informally.
- Over-testing without reducing low-value assessments.