Degree Discipline
Year
1 Matching Results
Results open in a new window/tab. Unexpected Results? Search the Catalog Instead.
Results:
1 - 1 of
1
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 Examination
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 …
Date:
December 2019
Creator:
Alexis, Naudin
System:
The UNT Digital Library