Students' cognitive, affective, socio-demographic characteristics and school/classroom instructional contexts as factors in mathematics and science achievement: Analyses of TIMSS.

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

Students' Cognitive, Affective, Socio-demographic characteristics and school/classroom instructional contexts as factors in mathematics and science achievement: Analysis of TIMSS. The proposed research will develop an integrative and broad framework for understanding mathematics and science achievement in secondary school student. The project is a three-year study of the critical factors related to mathematics and science laearning in secondary schools. There is a persistent national concern about the mathematics and science achievement of the U.S. secondary students. The major purpose of the proposed study is to identify, analyze and examine the interrelated factors that have been shown by research and theory to be related to mathematics and science learning by developing a more comprehensive conceputal framework. Despite growing body of knowledge in the area of academic achievement, research findings in mathematics and science learning have been inconsistent and have produced limited understanding of the processes of achievement in these critical subject areas. Research has linked mathematics and science achievement o several important domains: socio-demographic variables such as gender, ethnicity, race and social class; cognitive and affective variables such as prior achievement, ability interest, motivation, attitudes and beliefs; teacher, classroom and school variables such as opportunity to learn (OTL), instruction and social context of the classroom. Using these constructs, theoretically sound and a priori models will be developed and estimated to understand the effects of these variables on mathematics and science achievement and their interrelationships with each other. Multivariate analytical procedures such as Structural Equation Modeling (SEM) and Hierarchical Linear Models (HLM) will be used to analyze the data. The two nationally representative data sets that would be used in the study are the Third International Mathematics and Science Study (TIMSS) and Longitudinal Study of American Youth (LSAY). These two data sets, being cross-sectional and longitudinal respectively, offer different analytical advantages. This project was proposed as a three-year secondary analysis of existing survey data for the secondary school students collected for the Third International Mathematics and Science study. It would focus only on US students, but would luse the information collected in that study on their attitudes toward science and their gender, race, and social class. The proposal also would use a longitudinal study, Longitudinal Survey of American Youth, to expand this analysis.