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Exploration of Explanatory Variables in the Creation of Linear Regression Models and Logistic Regression Models to Predict the Performance of Preservice Teachers on the Science Portion of the EC-6 TExES Certification ExaminationAlexis, Naudin 12 1900 (has links)
The purpose of this study was to analyze the current and pre-service conditions that can affect student teachers' preparedness to pass the science portion of the EC-6 Texas Examinations for Educator Standards (TExES), one of the mandatory certification exam to become a teacher in Texas. Two types of prediction models were employed in this study: binomial logistic regression and multiple linear regression. The independent variables used in this study were: final grade in BIOL 1082, classification of students, transfer status, taken college biology, taken college chemistry, taken college physics, taken college environmental science, taken college earth science, attending college part-time, number of credits taken during the semester, first-generation college student, relatives with degree in education, and current GPA. The dependent variable of this study was the posttest score on science portion of the EC-6 TExES practice exam. A total of 170 preservice teachers participated this study. This study used students enrolled in BIOL 1082, who volunteered to take a Biology for Educators QualtricsTM survey and the EC-6 TExES practice exam in a pretest (start of semester) and posttest (end of semester) form. The findings of this study revealed that the single best predictor of preservice teachers' performance on the science portion of EC-6 TExES practice certification examination was the Grade in BIOL 1082.
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