Thesis (Ph. D.)--Texas A & M University, 2004. / Title taken from PDF title screen (viewed October 23, 2007). Includes bibliographical references (p. 66-74) and appendices.

The purpose of this study was to investigate predicting student success on one example of a "high stakes" test, the Texas Assessment of Academic Skills Exit Level Exam. Prediction algorithms for the mathematics, reading, and writing portions of the test were formulated using SPSS® statistical software. Student data available on all 440 students were input to logistic regression to build the algorithms. Approximately 80% of the students' results were predicted correctly by each algorithm. The data that were most predictive were the course related to the subject area of the test the student was taking, and the semester exam grade and semester average in the course related to the test. The standards of success or passing were making a 70% or higher on the mathematics, 88% or higher on the reading, and 76% or higher on the writing portion of the exam. The higher passing standards maintained a pass/fail dichotomy and simulate the standard on the new Texas Assessment of Knowledge and Skills Exit Level Exam. The use of the algorithms can assist school staff in identifying individual students, not just groups of students, who could benefit from some type of academic intervention.

The purpose of this study was to identify high performing school districts in Texas and to determine if there are different characteristics leading to the provision of an adequate education in high performing districts as compared to low performing districts. It specifically sought to determine which characteristics contributed most to an adequate education and used data from the Texas Academic Excellence Indicator System (AEIS) which chronicled scores on the Texas Assessment of Academic Skills (TAAS). The population for this study consisted of all 2001-2002 Texas public school districts with the exception of charter schools, special-purpose statutory districts, and state-administered districts, which resulted in using data from 1027 Texas school districts. Descriptive discriminant analysis was chosen as the method for statistical analysis. Data were obtained from the Texas Education Agency (TEA). Adequate and inadequate districts were analyzed according to eight variables. They were (1) taxable value per pupil, (2) the percentage of special education students, (3) the percentage of students coded as bilingual and ESL, (4) pupil-teacher ratio, (5) the size of the district, (6) the percentage of economically disadvantaged students in the district, (7) the district community type, such as rural or suburban, and (8) the total operating expenditures per pupil. Two analyses were conducted. The first analysis sought to determine the different characteristics between adequate districts (districts that scored 80% or above on the TAAS test) and inadequate districts (districts that scored 79% or below on the TAAS test). In order to determine these differences with a higher standard for adequacy, a second analysis was performed. The second analysis focused on districts deemed adequate by scoring 90% or above on the TAAS test compared to those districts deemed inadequate by scoring 69% and below. The eight variables accounted for 21% and 37% of the variance between groups respectively. For both analyses performed, the three variables that clearly contributed most to predicting membership in the two groups were the percentage of economically disadvantaged students( .935, .652), the percent of students in bilingual or ESL programs (.332, .371), and the taxable values per pupil (.178, .058). The percentage of economically disadvantaged students indicated that this variable explained 94% and 65% of the overall effect sizes. The percentage of bilingual and ESL students explained 33% and 37% of the effect size. Taxable Value per pupil explained 17% and 6% of the effect size. The percentage of special education students, total operating expenditures per pupil, the student-teacher ratio, community type, and the size of the district each, revealed only slight contribution to group differences. These results clearly suggested that the inadequate districts had higher percentages of economically disadvantaged students and ESL students while the adequate districts had higher taxable value per pupil, or wealth.

The purpose of this study was to (1) gather information regarding knowledge of TAAS and perceptions (attitudes) about TAAS (excluding the current battery of End of Course Tests) from teachers and administrators; (2) relate teacher and administrator knowledge and perceptions of the test to student test performance as reported in the Academic Excellence Indicator System (AEIS) Reports. Answers to the following questions were sought: 1. To what extent do teachers and administrators possess different levels of knowledge regarding TAAS, and different attitudes toward TAAS about its purpose and usefulness? 2. Are differences in teacher and administrator knowledge of TAAS related to student performance? 3. Are differences in teacher and administrator attitude toward TAAS related to student performance? Information was collected, by means of a twenty-six-item survey measuring teacher and administrator knowledge and attitude toward TAAS. The selected schools were chosen from schools rated as either exemplary or low performing by the state accountability system. The data were examined using Descriptive Statistics (Mean, Median, Mode, Standard Deviation) and Analysis of Variance (ANOVA). ANOVA was performed to determine if a significant variance existed between the responses of teachers and administrators and also between exemplary and lowperforming schools. Exemplary and low performing schools were chosen to determine if there were differences in teacher and administrator responses from these two groups. The results of this study attempt to show what, if any differences there are in attitudes toward and knowledge about TAAS based on responses from teachers and administrators from both exemplary and low performing schools. Based on the analysis of the data, there is no evidence from this study that indicates that there are significant differences in knowledge between teachers and administrators regarding TAAS. There is evidence that administrators possess a more positive attitude toward TAAS than do teachers. There is no evidence presented in this study that differences in teacher or administrator knowledge significantly impact student performance.

The management style being used by school personnel in Texas and across the nation today is predominately that of a bureaucracy. This model was organized around the industrial revolution that was exercising authority at the turn of the century. Writers and researchers have pointed out that such a model is not capable of providing students the knowledge and skills they will need to enter an increasingly demanding society. One management style relatively new to the educational arena today is that of Total Quality Management. This study reports the results of the impact of the training in those principles by measurement of student test scores.

This study examined the relationship between Career and Technical Education (CATE) and the Academic Excellence Indicator System (AEIS), the measure of school and learner success in Texas. CATE, an established program, traditionally encourages student achievement and perpetuates best educational practices. AEIS data was collected by the Texas Education Agency. In addition, a survey was used to measure CATE effectiveness and the relationship between effectiveness and AEIS performance. Two-factor mixed repeated measures ANOVAs were used to observe group differences over time. CATE and non-CATE exit level TAAS scores for reading and math at the district level were analyzed for 2000, 2001, and 2002. CATE students had higher group means but there was not statistical significance indicating that CATE students performed as well as non-CATE. Two-factor mixed repeated measures ANOVAs were also used for analysis of differences at the district level for attendance, dropout rates, and graduation rates. There were higher group means for attendance for CATE students and there was also statistical significance indicating that CATE students attended more often then non-CATE students. There was a lower group means for dropout rate and there was also statistical significance between groups over time. This was an inverse relationship indicating that CATE students dropped out less often then non-CATE students at a statistically significant level. The graduation rate analysis showed a higher group mean for CATE students but not statistical significance. CATE students graduated at the same rate as the non-CATE group. Pearson's r was used to correlate the relationship of the effectiveness of CATE programs with AEIS results. There was no statistical significance for reading and math TAAS exit-level tests with CATE effectiveness scores. Again there was no statistical significant relationship between CATE effectiveness and attendance and graduation. However there was statistical significance between CATE program effectiveness and dropout correlation for 2001.

The problem of this study was to determine whether learning styles of students affect their math achievement scores on the Texas Assessment of Academic Skills Test. The research questions addressed relevant to this study were: 1. Is there a positive correlation between students' learning styles and their achievement test scores in mathematics? 2. Is there a positive correlation between specific sub group's (as deemed by the state of Texas) and gender's learning styles and their achievement test scores in mathematics? The Pearson Product Moment Correlation coefficient and the Point-biserial correlation analysis was applied to the data collected from 500 fifth grade students attending a North Texas Intermediate school. The significance level was established at the .05 level. Part of the data was the student's responses to the Learning Style Inventory by Dunn, Dunn and Price. The findings established that the learning style preferences of all students in the area of persistence significantly impacted their math achievement scores. Gender and ethnicity were mitigating factors in the findings. These learning style preferences significantly impacted achievement in the following ways: * Caucasian students' preference of a high level of persistence in completing a difficult task. * Hispanic students' preference for a warm learning environment and motivational factor of pleasing the teacher. * Afro-American students' preference for kinesthetic learning. * Female students' learning style preferences appear in: - the design of the learning environment - the need for intake of food and/or drink - a high level of responsibility - a high sense of self-motivation , of teacher and of parent motivation. * Male students' learning style preferences appear in: - a warm learning environment - a high level of responsibility - the need for intake of food and/or drink - a high sense of teacher and of parent motivation - a late morning learning In summary, the author suggests that supplying the teachers with information concerning students' learning style preferences will benefit student achievement.

An examination of the characteristics of Texas middle schools has been conducted with the objective of developing a planning tool for middle staffs. This success is measured by the Academic Excellence Indicator System (AEIS), whose rating scale has three components: campus scores on the Texas Assessment of Basic Skills (TAAS), campus attendance percentages, and campus dropout rates. TAAS scores and attendance rates have been the focus of this study. Two years of data were examined separately for research question. Principal component analysis reduced the number of indicators in both years' data to 20 factors/ Each of these factors received a designated based on the characteristics that the component indicators had in common. A multiple regression analysis was performed on these factors to determine the influence each had on the campus TAAS scores and attendance. The unpredictability of human subjects requires an additional step in this study to achieve valid conclusions. A comparison of the two years' results is made to discover attendance, gifted and talented programs, and teacher gender were the strongest overall positive influences on student achievement. Campus demographics, retention, and ESL/bilingual programs have the strongest association with low student achievement.

The purpose of this study was to investigate special education outcomes in relation to state standardized testing. It specifically sought to determine if a relationship existed between selected data from the Texas Academic Excellence Indicator System (AEIS) comparing district students receiving special education services TAAS scores with selected district demographic, fiscal, and special education data. The population for this study consisted of all 2001-2002 grades 3-8 and 10 public school students with the exception of charter schools, special-purpose statutory districts, and state-administered districts. The reading analysis incorporated data from 896 Texas school districts. The mathematics analysis used data from 914 school districts. Multiple linear hierarchical regression was chosen as the method for statistical analysis. Data was obtained from the Texas Education Agency (TEA) as a special data pull. For both the reading and mathematics analyses, wealth and ethnicity were statistically insignificant although ethnicity individually accounted for a large percentage of the variance for both the reading (20.3%) and mathematics (13.2%) scores as well as producing negative β weights. All other predictor variables produced varying degrees of statistical significance. Community type, socioeconomic status, instructional expenditures per students, and instructional expenditures per student receiving special education services also produced negative β weights. Two variables in this study, enrollment and the percentage of students receiving special education services tested, produced positive β weights, substantial squared structure coefficients, and positive Pearson correlation coefficients. Of these two predictors, the strongest overall positive predictor for students receiving special education services success on the grades 3-8 and 10 reading and mathematics TAAS exams was the percentage of students receiving special education services tested. These percentages produced the largest positive correlations with passing rates (reading r = .283, mathematics r = .219) and the second largest regression coefficients (reading β = .224, mathematics β = .202). They individually accounted for the largest percentage of total criterion variance (reading = 33.0%, mathematics = 22.6%). For this study, these results clearly suggested that the dominant positive predictor of testing success for students receiving special education services was the percentage of students receiving special education services tested. Conversely, socioeconomic status was the dominant negative predictor.

Educators can no longer simply look at student totals to distribute instructional dollars. Databased decision-making must be instituted to overcome achievement gaps between white and non-white students. In low-socioeconomic (SES) settings, districts must increase expenditure per pupil (EPP) as low-SES rates rise for all students as district administrators must be in a position to show product rather than process. It was attempted to determine if a positive or negative relationship existed between Anglo, Hispanic, and African-American student test scores and wealth factors on the Texas Assessment of Academic Skills tests in 2000 and 2002. Wealth factors studied included EPP and SES. Data analysis was carried out on 974 independent and consolidated school districts in Texas. Low-SES was found to be a negative predictor of higher test performance on standardized reading and mathematics tests. To varied degrees, low-SES affected all students from all ethnicities as well as affluent students. EPP was attributed with a positive effect on student test performance. Increases of $1,000 or more at one time produce performance increases from 0.20 to 0.40 points. In making specific recommendations, the researcher advises increasing expenditures low-SES districts, schools, and classrooms through the creation of specific district linear equations exhibited in this study. Funds must be earmarked for those students that are affected by poverty. It is also recommended to decrease the number of low-SES students by merging high-SES and low-SES students to dilute poverty's effects. Additional correlation studies that address instructional strategies and outside factors are needed. Finally, a replicating study using Texas Assessment of Knowledge and Skills data over a period would be beneficial.