|Title:||The Effects of School Climate and Supports on Mathematics Achievements for Students with Visual Impairments|
|Principal Investigator:||Cavenaugh, Brenda||Awardee:||Mississippi State University|
|Program:||Science, Technology, Engineering, and Mathematics [Program Details]|
|Award Period:||10/1/2006 to 9/30/2008||Award Amount:||$257,170|
Purpose: The purpose of this project is to examine the effects of school climate, including policies and practices related to teacher supports, student supports, and support for family involvement, and other contextual and individual factors on mathematics achievement for elementary and middle school students with visual impairments. Students with visual impairments tend to lag behind their sighted peers in math achievement, and there are persistent questions about the best approaches for developing math proficiency among this population. The majority of students with visual impairments are taught in regular schools and classrooms. Many school-related variables have been found to impact the achievement of students in general, but research has not been conducted to determine the effects of these variables on the achievement of students with visual impairments. This study is intended to address this research need.
Project Activities: The population of study is the sample of visually impaired students in the Special Education Elementary Longitudinal Study (SEELS), a nationally-representative longitudinal study of elementary and middle school students with disabilities. The primary outcome variable, mathematics achievement, is measured by two subtests from the Woodcock-Johnson III Test of Achievement (i.e., Calculation and Applied Problems). The school climate measure in this study includes teacher reported supports to teach special needs students, expectations of the school leadership, promotion of instructional improvement, and safety of the school. The school-related effects on mathematics achievement will be evaluated controlling for the possible moderating effects of demographic and disability-related background variables and other appropriate factors. Research methods include descriptive analyses and longitudinal hierarchical regression-based analyses of level of achievement and rate of change in achievement as a function of school climate, support, and contextual factors. Data analysis strategy includes descriptive examination of all measures followed by analysis of achievement levels and longitudinal change using multilevel modeling, specifically individual growth curve modeling. Structural equation modeling methods will also be employed to evaluate covariance structural relationships and investigate direct and mediated effects of support and contextual factors on the mathematics achievement of students with visual impairments.
Products: The expected outcomes from this study include:
Setting: This study will analyze data from the Special Education Elementary Longitudinal Study (SEELS), a nationally-representative longitudinal study of elementary and middle school students with disabilities conducted between 2000 and 2005. SEELS collected longitudinal data on achievement, student characteristics, and educational experiences from such sources as students, teachers, parents, principals, and direct assessments of achievement, self concept, and attitudes toward school.
Population: The population of interest in this study is students with visual impairments. These students are identified in the SEELS database by having a primary disability of visual impairment, or by being identified by a parent or teacher as having a visual impairment (which may be a secondary disability). Students identified as deaf-blind are not included. A minimum sample size of 122 - 161 students is planned. Students will be in grades K through 8.
Intervention: This study does not involve a predetermined intervention, but will instead suggest possible intervention modalities based on the school climate and support variables that are found to correlate with student achievement in mathematics.
Research Design and Methods: The design is basically correlational, examining an extant database for relations between predictor and outcome variables while controlling for the effects of possible moderating or mediating variables. The outcome of interest is mathematics achievement, and the predictor variables relate to school climate and supports, and the moderating/mediating variables relate to demographic and disability-related background variables.
Control Condition: There is no control condition in this design.
Key Measures: Measures are limited to data available in the SEELS database. The primary outcome variable, mathematics achievement, is measured by two subtests from the Woodcock-Johnson III Test of Achievement (i.e., Calculation and Applied Problems) that were directly administered to students in the SEELS sample. School climate is measured by a set of teacher survey items on such topics as available supports for teaching special needs students, expectations of the school leadership, promotion of instructional improvement, and safety of the school. School support is measured by a set of teacher survey items on student-teacher ratio and supports for general education teachers in working with students with disabilities. Student support is measured by a set of teacher survey items related to academic supports, peer support, vision services, and assistive technology services. Support for family involvement is measured by a teacher survey item on practices schools can use to promote parent involvement. Other variables of interest derived from teacher surveys are reading medium (student use of Braille or large print), family involvement, and school SES. In addition, data from direct student assessments of self-concept (Student Self Concept Scale) and motivation (Motivation for Schooling subscale of the School Attitude Measure) are included. Background variables are based on items in the SEELS database related to family SES, race/ethnicity, gender, age, disability, etc.
Data Analytic Strategy: The data analysis strategy includes descriptive examination of all measures followed by analysis of factors related to achievement levels and longitudinal change. Specific analysis methods include repeated-measures MANOVA and hierarchical linear growth curve modeling. Structural equation modeling methods are employed to evaluate covariance structural relationships and investigate direct and mediated effects of support and contextual factors on the mathematics achievement of students with visual impairments.
Journal article, monograph, or newsletter
Giesen, J.M., Cavenaugh, B.S., and McDonnall, M.C. (2012). Academic Supports, Cognitive Disability, and Mathematics Achievement for Visually Impaired Youth: A Multilevel Modeling Approach. International Journal of Special Education, 27(1): 17–26. Full text
McDonnall, M.C., Cavenaugh, B.S., and Giesen, J.M. (2012). The Relationship Between Parental Involvement and Achievement for Students With Visual Impairments. Journal of Special Education, 45(4): 204–215. doi:10.1177/0022466910365169 Full text