Pey-Yan Liou and Frances Lawrenz

1. Pey-Yan Liou and Frances Lawrenz Quantitative Methods in Education of the Department of Educational Psychology, University of Minnesota Variables Affecting Perceived Impact of Load-Forgiveness Policies on Encouraging Teaching In High-Need Areas Results and Conclusions Abstract Research Questions Unconditional HLM Models (One-Way ANOVA Models): the scholars’ personal characteristics and perceptions explain more of the variance in their perceptions of the influence of the Noyce funding on them becoming teachers (91% vs. 9%) and becoming high need school teachers than the characteristics of their teacher preparation/certification programs (95% vs. 5%). 1. How influential is the Noyce funding on the scholars’ decisions to become teachers and what scholar and preparation program variables are related to that perceived influence? 2. How influential is the Noyce funding on the scholars’ decisions to teach in high need schools and what scholar and preparation program variables are related to that perceived influence? This research used multi-level modeling to investigate the perceived effect of a teacher preparation loan forgiveness program on recruiting science and mathematics majors to become teachers and teach in high need schools. The study investigated how and which personal perceptions, characteristics and teacher preparation program variables influenced recipient perceptions. Data used for the study were collected from participants in the National Science Foundation Robert Noyce Teacher Scholarship Program, which provides funding for highly qualified science and mathematics majors to teach in high need schools. Recipients agree to teach in high need schools for at least two years in return for each year of funding. The results suggest that scholars’ race, their path into teaching, their perceptions of their preparation for teaching in high need schools and the amount of funding were significant variables. Key words: Load forgiveness, High need teaching, Multi-level modeling, Teacher recruitment, Program evaluation. Methods and Analyses Instruments The data utilized for this study came from three main sources: the Noyce scholar survey, the ORC dataset, and the Noyce PI Survey. Samples Data from 427 scholars in 37 programs were available to use in this analysis. Introduction Importance of student mathematics and science achievement:Several national calls1 to improve student mathematics and science achievement have been made due to STEM demands of the global economy. Inconsistency of educational achievement within the U.S. contexts: There is a significant science and mathematics score gap between students eligible and not eligible for the school lunch program2. The majority of the lower performing students are enrolled in high need schools such as inner city and extreme rural areas. Relationship between student achievement and teacher quality:Research has consistently shown teacher quality in mathematics and science education to be correlated with student achievement3. Role of teacher education programs on training preservice teachers: The different elements of teacher education programs have been shown to have effects on their graduates. The importance of culturally relevant pedagogy training and field experiences are emphasized on preparing teachers for diverse populations4. Impact of the teacher financial incentive programs: Financial incentives is a recruitment strategy with a long history. One example is the NSF Robert Noyce Teacher Scholarship Program begun in 2002 received additional funding under the American Recovery and Reinvestment Act of 2009. Noyce Evaluation Website http://www.cehd.umn.edu/EdPsych/NOYCE. Acknowledgments Measures Four outcome variables, three program-level variables, and five scholar-level variables5. Analyses Two sets of two-level unconditional and conditional HLM and HGLM models were used. The conditional HLM and HGLM models are as follows: Level 1: Scholar This project was funded by National Science Foundation Grant#REC0514884. References 1The Opportunity Equation: Transforming Mathematics and Science Education for Citizenship and the Global Economy. (2009). Carnegie Corporation of New York. 2National Center for Education Statistics (2009). The Nation’s Report Card: Mathematics 2009 (NCES 2010-451). Institute of Education Sciences, U.S. Department of Education, Washington, D.C. 3Darling-Hammond, L. (2000). Teacher quality and student achievement: A review of state policy evidence. Education Policy Analysis Archives, 8(1). 4Hollins, E. R., & Guzman, M. T. (2005). Research on preparing teachers for diverse populations. In M. Cochran-Smith, & K. M. Zeichner (eds.), Studying teacher education: The report of the AERA panel on research and teacher education (pp.477-548). New Jersey: Lawrence Erlbaum Associates. 5Liou, P.-Y., & Lawrenz, F. (2008). University of Minnesota Evaluation of the Robert Noyce Teacher Scholarship Program, Final report section two: Factor analysis of Robert Noyce Scholarship Program evaluation. University of Minnesota, Minneapolis. Level 2: Program are fixed effects