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2006Research Conference | June 15–16

This conference highlighted the work of invited speakers, independent researchers who have received grant funds from the Institute of Education Sciences, and trainees supported through predoctoral training grants and postdoctoral fellowships. The presentations are those of the authors and do not necessarily represent the views of the U.S. Department of Education or the Institute of Education Sciences.
Hyatt Regency Washington on Capitol Hill
400 New Jersey Avenue, N.W.
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Evaluation of the Computer and Team Assisted Mathematical Acceleration (CATAMA) Lab for Urban, High-Poverty, High-Minority Middle Grade Students

Robert Balfanz, Johns Hopkins University
Allen Ruby, Johns Hopkins University

Abstract: National and international comparisons of student achievement show that U.S. minority and low-income students fall behind between the 4th and the 8th grades. They often leave middle school poorly prepared to succeed in a rigorous sequence of college preparatory math classes in high school.

Starting in September 2005, Computer and Team Assisted Mathematical Acceleration (CATAMA) Labs were established at three, inner city, middle grades, schools in Philadelphia. These Labs combine computer-based instruction, peer-assisted learning, and small group and individualized tutoring to teach both math concepts and skills. From each school's pool of students having math deficits, students were randomly assigned (within grade) to the Lab or a control group. Lab students spent approximately 45 minutes per day for about one trimester in the Lab while control students took another elective class. All students continued with their regular math class using a district-wide curriculum. The Lab was taught by a regular math teacher who received ongoing professional development.

This poster presents the research design and the results for the 380 students taking part in the first cycle of the study. A pre-test post-test (CTBS Terra Nova) experimental design with random assignment is used to study whether the Lab can raise the math achievement of underperforming students. Along with a comparison of means, regression analysis also is used to determine the contribution of other factors including race/ethnicity, gender, and initial level of student underperformance in math. A further extension will determine if any impact of the Lab is due to the additional time spent studying math in the Lab.