Purpose: Research shows that advanced course taking in high school benefits students in the short and long term. However, enrollments in advanced courses traditionally have disproportionately excluded students from racial/ethnic minority groups and students from low-income families. This project will explore the high school and postsecondary outcomes of automatic enrollment policies intended to increase access to advanced courses and will study the potential of an automatic notification tool embedded in a student information system (SIS) to efficiently scale the systematic identification of students previously overlooked for advanced course taking.

Project Activities: The research team will analyze data from 49 districts in Washington state that implemented district-level automatic enrollment policies and interview administrators in these districts to understand the features of their policies. The researchers also will partner with Infinite Campus, a large SIS, to pilot-test features of an automatic notification tool embedded in the course planning module of the SIS and interview tool users to understand how schools made use of automatic notification.

Products: The research team will produce estimates of the outcomes of automatic enrollment policies, with special attention to outcomes for minorities and students from low-income families who have historically been underrepresented in advanced courses. The researchers also will produce preliminary evidence of promising features of an automatic notification intervention that could be developed in the future and easily brought to scale. The research team will present results in peer-reviewed conference presentations and publications, research briefs, and webinars.

Structured Abstract

Setting: This project will take place in high schools in 49 public school districts in Washington state and 1,200 school districts across 45 states (including urban, suburban, and rural).

Sample: Approximately 21,300 high school students in 49 school districts in Washington, of whom approximately 60% are proficient in English language arts and 50% are proficient in mathematics, 43% are nonwhite, and 59% are eligible for free or reduced-price lunch; and approximately 180,000 Grade 9 students in 1,200 districts nationwide, who are approximately representative of the national Grade 9 student population.

Factors: The research team will examine malleable features of automatic enrollment policies, including grade levels targeted, advanced courses open for automatic enrollment, and eligibility criteria. The researchers also will examine malleable features of an automatic notification tool, including who is notified (students, parents, or school guidance counselors), and what information is presented in the notification.

Research Design and Methods:  To answer their research questions, researchers will link K–12 and postsecondary Washington data and build a research dataset from SIS data. The research team will use the data to estimate multiple comparative interrupted time series, regression discontinuity designs, and randomized control trials to examine high school and postsecondary outcomes.

The researchers will address six questions:

1. Did enrollment and passing rates in advanced courses in Washington districts that implemented automatic enrollment increase for minority students and students from low-income families?
2. Did enrollment and passing rates in advanced courses in Washington districts that implemented automatic enrollment shift to more closely reflect schoolwide demographic composition?
3. What is the effect of automatic enrollment on students' high school outcomes, especially for minority students and those from low-income families, and for students in schools and districts with different characteristics and policy features?
4. What is the effect of automatic enrollment on students' postsecondary outcomes, especially for minority students and those from low-income families, and for students in schools and districts with different characteristics and policy features?
5. What features of an automatic notification tool embedded into districts' SIS lead to increased enrollment and success in advanced high school courses?
6. How do school guidance counselors use the automatic notification tool when making course assignments for their students?

In addition, the research team will interview both district administrators to understand features of automatic enrollment policies and users of the automatic notification tool to understand how different automatic notification features were used to inform student course enrollments.

Control Condition: The research team  will compare Washington students in districts that implemented an automatic enrollment policy with students in districts that did not yet implement a policy and will compare Washington students who were just above the threshold for automatic enrollment with students just below the threshold. In addition, the researchers will compare districts that receive each of three automatic notification treatment conditions (each with a different notification feature) with districts that receive a business-as-usual condition in which a general notification to review students' course choices is presented.

Key Measures: The research team  will measure student, school, and district characteristics, including features of districts' automatic notification policies and the automatic notification tool, student assessments used to automatically enroll or notify students, school-level representation of minority students and those from low-income families in advanced courses, and student outcome variables (advanced course taking and success, high school graduation, college enrollment, early college success, persistence, and bachelor's degree completion).

Data Analytic Strategy: The research team  will use comparative interrupted time series (CITS) to examine the relationship between district automatic enrollment policies and school- and student-level course-taking patterns, a regression discontinuity design (RDD) to examine high school and postsecondary outcomes of being automatically enrolled in advanced courses, and a randomized controlled trial (RCT) to pilot features of a potential automatic notification tool.