Predicting Student Achievement: A Path Analysis Model on A Mathematics Coaching Program

Scott A. Zollinger 1 , Patti Brosnan

1 Department of Mathematics Education, The Ohio State University, Columbus, Ohio 43221, United States
*Authors to whom correspondence should be addressed.

Received: 2011-9-25 / Accepted: 2011-11-28 / Published: 2011-12-30

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Abstract In response to calls for mathematics education reform, researchers at The Ohio State University, working with the Ohio Department of Education, developed the Mathematics Coaching Program (MCP). Coaches from 164 schools have participated in this classroom embedded professional development program designed to promote standards-based instructional practices. Preliminary results indicated MCP has a positive impact on student achievement. To provide supporting evidence of these results, researchers developed a path analysis model consisting of seven components to determine factors that were predictors of student achievement both before and after schools participated in MCP. Dependent variable components were pre- and post-MCP test scores and independent variable components represented economically disadvantaged students, non-white students, disability students, number of years in MCP, and coach mathematical content knowledge. Results indicated that the initial and modified theoretical models were not acceptable fits for our fourth grade sample data, but many parameter values were consistent with previous research. Disability, SES, and ethnicity were significant predictors of pre-MCP test scores and were negatively correlated. For post-MCP test scores, t-values of disability and ethnicity decreased to non-significant levels but t-values for SES nearly doubled. The number of years a school participated in the program was a significant predictor of post-MCP test scores but coach mathematical content knowledge was not. Overall, the path analysis model did not test as an acceptable fit for this data, but the final version represents a starting
point for future testing.

Research Areas:  Learning theory