What does disaggregated data refer to in the context of assessing student progress?

Disaggregated data refers to the practice of breaking down data into specific subgroups to provide a more detailed analysis of performance metrics. In the context of assessing student progress, this allows educators and policymakers to understand how different segments of the student population are performing. This may include examining data based on characteristics such as ethnicity, socioeconomic status, special education needs, or English language proficiency.

This approach is crucial because it reveals gaps or disparities in achievement among different groups of students, enabling targeted interventions and supports where they are most needed. By focusing on specific subgroups, educators can tailor their strategies to address the unique challenges faced by these populations, fostering a more equitable educational environment.

The other options do not capture this specific focus on subgroups. For example, scores reflecting all students provide a general overview but miss nuances that might be present within subgroups. Comparisons between schools focus on overall performance without addressing subgroup disparities. Highlighting only top-performing students ignores the progress and needs of those who may be struggling, which is essential for comprehensive educational assessments.