This research aims to continue to study the effectiveness of a promising academic intervention (implemented by SAGA Innovations) that has previously been shown to significantly improve academic outcomes for disadvantaged youth. In addition, this study will begin to investigate the effects of scaling up this promising strategy by exploring variation in tutor effectiveness and the optimal instructor-student and student-student pairings for improving academic outcomes.
The University of Chicago Education Lab research team is carrying out a randomized controlled trial of a promising academic intervention during the 2015-16 academic year in partnership with the Chicago Public Schools (CPS) and SAGA Innovations. Male and female CPS students in grades 9 and 10 will be randomly assigned either to receive what investigators believe to be a best-practice intensive academic support, or to a control group receiving status quo CPS and community services, for one academic year (AY2015-16). The intervention is high-dosage math tutoring provided by SAGA Innovations (previously Match Education of Boston). A previous randomized controlled trial conducted by the University of Chicago research team found that one year of this intervention, delivered in AY2013-14, generated between one and two extra years of academic growth in math, over and above what the normal U.S. high school student learns in one year. The estimated effects for math achievement are on the order of 0.19 to 0.30 SD, depending on the exact test and norming used. The intervention also improved student grades in math, by 0.58 points on a 1-4 grade scale, compared to a control mean of 1.77. These gains are particularly important because math success versus failure is a strong predictor of high school graduation. This current study aims to replicate the investigators' previous findings, and to that end the research team will again look at the academic, behavioral, and long-term effects of this high-dosage math tutoring program on youth. This study is also designed to explore issues that will be central to efforts to scale-up this promising strategy, including variation in tutor quality and whether there are optimal tutor-student and student-student pairings in terms of gender and race. The SAGA Innovations program expands on the nationally recognized innovation of high-dosage, in-school-day tutoring developed in Match Education's charter school in Boston. The tutoring program meets as a scheduled course, Math Lab, once a day during the normal school day, and is provided in addition to a student's regular math class. Students taking the course receive an elective credit upon completion. Every student works with the same full-time, professional tutor for the entirety of the school year. The content of the tutoring sessions is aligned with what students are learning in their regular math courses, but is also targeted to address individual gaps in math knowledge. Also following the original model developed by Match Education, SAGA tutors use frequent internal formative assessments of student progress to individualize instruction. In addition to replicating previous studies that suggest the promise of this high-dosage tutoring model for improving the academic outcomes of at-risk youth, this study also aims to provide insight into the ability of this program to serve youth at a much larger scale. Despite the great need for programs that can affect the national dropout crisis and improve youth outcomes, little is known about how to take promising education interventions to scale. This study will begin to explore whether there is a trade-off between effectiveness and scale by randomly assigning students to pairings and randomly assigning pairings to tutors. Tutors will be separately ranked from highest to lowest quality by SAGA leadership, and by randomly assigning tutors to students, the investigators will be able to explore what effect, if any, tutor quality has on student outcomes. In addition, this study will look at whether gender and race composition of student-tutor pairings and student-student pairings has an effect on outcomes. This work will enable the investigators to begin to learn about variation in tutor effectiveness and the optimal way to match kids to tutors. The research team hopes this work will have important implications for how to scale this promising strategy both within Chicago and beyond.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Enrollment
1,848
An intensive math tutoring program.
Math achievement
Performance on math standardized achievement test scores
Time frame: 1-year
Absentee rate
Number of school absences, obtained from Chicago Public Schools (CPS) administrative database
Time frame: 1-year
Student misconduct
Number of school misconduct infractions, obtained from Chicago Public Schools administrative database
Time frame: 1-year
Total courses failed
Number of total school courses failed, obtained from Chicago Public Schools administrative database
Time frame: 1-year
Math courses failed
Number of math courses failed, obtained from Chicago Public Schools administrative database
Time frame: 1-year
Non-math courses failed
Number of non-math courses failed, obtained from Chicago Public Schools administrative database
Time frame: 1-year
Math course grades
Math course grades, obtained from Chicago Public Schools administrative database
Time frame: 1-year
School persistence
Measure from CPS student records of school persistence (enrollment or graduation status by end of academic year)
Time frame: 1-year
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Violent crime arrests
Number of violent crime arrests, obtained from Chicago Police Department and Illinois State Police administrative databases
Time frame: 1-year
Other arrests (property, drug, and other crimes)
Number of non-violent crime arrests, including property crimes, drug crimes, and other crimes, obtained from Chicago Police Department and Illinois State Police administrative databases
Time frame: 1-year
Quarterly earnings data
Quarterly earnings collected by the Illinois Department of Employment Security, maintained for the state unemployment insurance system
Time frame: 1-year