The AP Calculus program at Green High School was not adequately preparing students for college level calculus as more than 80 percent of the students failed the AP exam. The students were concurrently receiving high marks on in-class assessments. Hence, the in-class assessments were not good indicators of students calculus understanding according to the AP standards. A needs assessment was conducted, focusing on the difference in high school and college calculus, how to assess calculus and where Green High School's assessments were falling short. More research was conducted to examine the college Board expectations of calculus learning. A content matrix was designed to measure how well an in-class assessment aligns with college and AP calculus expectations of calculus knowledge. From this, new assessments were created that meet the goals of the content matrix.

The purpose of this study was to determine the degree to which i-Ready® Reading Instruction (a computer adaptive testing program) predicted Florida Standards Assessment English Language Arts (FSA ELA) scores for low performing students (Level 1) compared to those who are on grade level (Level 3). Participants included students in seventh grade at a central Florida school district who participated in i-Ready® Reading Instruction and who had previously scored a Level 1 or Level 3 on the FSA ELA. A hierarchical multiple regression was run to determine the impact of the interaction effect between prior year FSA performance level and time spent in i-Ready® Reading Instruction, while controlling for teacher-level and other student-level variables. Regression analyses indicated that prior year FSA ELA performance level did not moderate the degree to which time spent in i-Ready® Reading Instruction predicted FSA ELA score gains. Additionally, when the interaction term was removed from the regression, time spent in i-Ready® Reading Instruction did not predict FSA ELA score gains.

The purpose of this study was to examine the effectiveness of a computer adaptive mathematics program, i-Ready® Mathematics Instruction in Central Florida School District (CFSD) using a variety of quantitative analyses. In particular, students in the fifth grade at CFSD were examined in relation to the gains made on their end-of-year statewide mathematics assessment, known as the Florida Standards Assessment (FSA Math). Per i-Ready®, students who use i-Ready® Mathematics Instruction for a minimum of 45 minutes per subject per week for at least 25 weeks show significantly greater gains than those who do not (Curriculum Associates, 2017b). Therefore, two propensity score matching methods (one considering the multilevel structure of the data) were examined and compared to split students into the following two comparison groups: those who used the program with fidelity and those who did not. Next, a series of multilevel models, using both propensity score matching methods, and dependent t-tests were examined to evaluate the effectiveness of i-Ready® Mathematics Instruction. In conclusion, the use of the multilevel propensity score matching technique yielded a better fit for explaining the variation in relation to gains students in the fifth grade made on the FSA Math assessment. Additionally, i-Ready® Mathematics Instruction did positively impact fifth grade students in Central Florida School District who used the program with fidelity with relation to their FSA Math score gains, compared to those who did not use the program with fidelity.

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Travel time estimation is an integral part of transportation management. The use of advanced technologies in collecting travel time information has been a major concern to transportation engineers and system operators who rely on such data to improve safety and emergency response of transportation facilities. GPS and Bluetooth are two of the available technologies which are efficient and cost effective in collecting travel time data. This study focuses on the accuracy analysis of them. This thesis begins with a review of the theory of Intelligent Transportation System (ITS) instruments. Then it illustrates the methods of travel time collection in three techniques, License Plate, Global Positioning System (GPS) and Bluetooth. Third, it outlines the method of processing of the data and the statistical method to compare the accuracy of GPS and Bluetooth. Last, it presents the results and analyzes the accuracy of two technologies.

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