Education--Demographic aspects

The United States Bureau of the Census has released much of the 1990 census data through CD-ROM technology. The census data is readily accessible, easily obtained, simple to use, and virtually free. This study presented an innovative way to use census data coupled with business and educational practices as a marketing and planning aid for adult literacy education. The purpose of this study was to develop a model that utilized geo-demographic census data to identify and segment the adult low-literate population. The identification and segmentation of the adult low-literate population gives literacy educators the ability to plan literacy related educational programs and to market literacy education programs to a well-defined, well-understood population. Demographic data from the 1990 United States census was compiled for the number of adults age 25 and older living in each census tract of Broward County, Florida, and St. Louis County, Minnesota, who possessed low-literacy identifiers found by past national literacy surveys. School districts from the two study counties supplied records containing the locations and the ABE and adult ESOL programs offered at the adult literacy education sites. Combining the census findings with the school district records revealed several combinations of conclusions. Some geographic locations with high incidences of the low-literacy identifiers, as could be expected, contained adult literacy education sites. However, some geographic locations with high incidences of the low-literacy identifiers did not contain adult literacy education sites. In contrast, some areas with low incidences of the low-literacy identifiers contained several adult literacy education sites. A Pearson correlation involving the low-literacy identifiers found a positive relationship between each of the educational attainment variables, each of the language isolation variables and between the educational attainment variables and the language isolation variables. In Broward County, Florida, there was a moderate negative relationship between per capita income and the other low-literacy identifiers. In St. Louis County, Minnesota, there was a weak relationship between per capita income and the other low-literacy identifiers. A K-means cluster analysis of census tracts found nine homogeneous clusters in Broward County, Florida, and seven homogeneous clusters in St. Louis County, Minnesota. The model could be a beneficial planning and marketing aid for adult literacy programs in any geographic location. Included in this study are 40 data tables. Census tract maps and thematic maps also illustrate the findings.

This study used a discriminant analysis procedure to identify the
demographic variables (school, teacher, grade level, subject, test used
for evaluation, semester of treatment, and gender) or subsets of variables
that would predict elementary students' success with computer assisted
instruction (CAl). Also, multiple factorial analyses of variance were
performed to test the interaction effect between treatment (CAl) and the
demographic variables. Recommendations suggest that future research attempt to identify
the appropriate teacher training in the use of computers for instruction that
will produce recurring student achievement with CAl. It is also suggested
that future research examining the academic effects of CAl in the
elementary classroom (grade 2 through 5) should not be concerned with
the grade level of the student, the subject being studied (mathematics or
language arts), the test used for evaluation (local or standardized), or the
gender of the student.