This descriptive study uses a qualitative, content analysis to examine the middle school visual arts and core Texas Essential Knowledge and Skills (TEKS) to determine the potential common learning activities that can be aligned between the two. By performing an alignment of the potential common learning activities present in the middle school visual art TEKS and the middle school core TEKS, I demonstrate that there is a foundation for curriculum integration in the Texas middle school visual arts classroom.

This thesis seeks to understand how political awareness affects what information one uses to indicate their approval or disapproval of Congress and its members. More concisely, do more and less aware individuals rely on the same pieces of political information to mold their opinions of Congress? The second question of concern is what role does media consumption play in informing survey respondents about Congress. Third, I consider how survey respondents use cues like the condition of the economy and presidential job performance to help formulate their opinion of Congress Finally, by applying the Congressional approval literature to incumbent level approval, I seek to advance the theory and literature on what motivates the approval of incumbents.

This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.