Across the country many schools engage in seasonal benchmark screenings to monitor to progress of their students. These are relatively brief assessments administered to "check-in" on students' progress throughout the year. This dataset was simulated from a real dataset from one large school district using the terrific synthpop R package. Overall characteristics of the synthetic data are remarkably similar to the real data.
A data frame with 10240 rows and 9 columns.
Integer. Student identifier.
Integer. Identifies the cohort from which the student was sampled (1-3).
Character. Special Education status: "Non-Sped" or "Sped"
Character. The race/ethnicity to which the student identified. Takes on one of seven values: "Am. Indian", "Asian", "Black", "Hispanic", "Native Am.", "Two or More", and "White"
Character. Student's eligibility for free or reduced price lunch. Takes on the values "FRL" and "Non-FRL".
Character. Students' English language learner status. Takes on one of values: "Active", "Monitor", and "Non-ELL". Students coded "Active" were actively receiving English language services at the time of testing. Students coded "Monitor" had previously received services, but not at the time of testing. Students coded "Non-ELL" did not receive services at any time.
Character. The season during which the assessment was administered: "Fall", "Winter", or "Spring"
Integer. Reading scale score.
Integer. Mathematics scale score.
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