Memo Date:
August 16, 2013
To:
Khalil Fuller, NBA Math Hoops
From
Juan D. Bonilla & Kirk Walters
Re:
Preliminary Analysis of the NBA Math Hoops Program
This memo presents the results of the preliminary analysis on the NBA Math Hoops Program. NBA Math Hoops is a competitive board game where students learn and apply fundamental math skills using specially designed NBA and WNBA player cards. The program was implemented by more than 130 volunteer teachers and 4000 students (predominantly from grades 4-9) throughout the U.S. during the 2012-13 school year. 1. Data For the analysis, we used the results of a 15-question math test that was applied to participating students before the beginning of the program (baseline test) as well as at the end of the program (follow-up test). Both tests have also information on students’ attitudes and abilities towards math—follow-up data also include students’ perceptions of the NBA Hoops program itself. The baseline data include 4060 student observations assigned to more than 130 participating teachers. In turn, the follow-up data has 1490 student scores distributed over 60 teachers. That is, there are teachers and students with (1) both pre and post information, (2) only baseline information, and (3) only follow-up information. In order to analyze the program results, student answers were graded and placed on a scale from 0 to 100. To facilitate the interpretation of the results, each student test score was standardized using the results from the baseline test. That is, from here on, all the test results provided will be interpreted in terms of the number of standard deviations relative to the baseline test. Ideally, we would like to have pre and post test score data for participating students in order to measure the effect of the program. Given that student IDs were absent, pre and post student data were linked through teacher names and student names. While making sure that teacher names were written in the same way in both applications (pre and post), linking pre and post data on students using their names was substantially more challenging. To do so, a Levenshtein editdistance algorithm was applied to find students that, despite having minor changes in their names, were essentially the same students.
1000 Thomas Jefferson Street NW, Washington, DC 20007-3835 | 202.403.5000 | TTY 877.334.3499 | www.air.org