TEACHNJ: An Evaluation of Two Years Implementation

The Teacher Effectiveness and Accountability for the Children of New Jersey (TEACHNJ) Act was adopted by the New Jersey legislature in August 2012 with the intent to raise student achievement by improving the overall quality of instruction. As a result of this act, new teacher evaluation systems are being introduced in school districts across the state in an effort to more accurately assess teacher performance. The new teacher evaluations will be based on multiple classroom observations as well as the academic achievement of their students as measured on standardized tests. In addition, professional development opportunities are likely to change under this legislation, with schools customizing professional development programs to more effectively meet the needs of their teachers. The overarching question that informs our research is what impact will TEACH NJ have on the overall value of teacher evaluations and the quality of professional development opportunities offered to teachers. Data collected through survey research presents the pre-implementation practices (2011-2012 school year) as well as one year post-implementation practices (2013-2014) taking place in school districts throughout New Jersey. The findings reflect teachers’ perceptions of the value of the current teacher evaluation practices, the quality of the current professional development opportunities and the value the school administration places on teacher evaluations.

Utilizing Traditional Cognitive Measures of Academic Preparation to Predict First-Year Science, Technology, Engineering, and Mathematics (STEM) Majors' Success in Math and Science Courses

For the past several years, U.S. colleges and universities have faced increased pressure to improve retention and graduation rates. At the same time, educational institutions have placed a greater emphasis on the importance of enrolling more students in STEM (science, technology, engineering and mathematics) programs and producing more STEM graduates. The resulting problem faced by educators involves finding new ways to support the success of STEM majors, regardless of their pre-college academic preparation. The purpose of my research study involved utilizing first-year STEM majors’ math SAT scores, unweighted high school GPA, math placement test scores, and the highest level of math taken in high school to develop models for predicting those who were likely to pass their first math and science courses. In doing so, the study aimed to provide a strategy to address the challenge of improving the passing rates of those first-year students attempting STEM-related courses. The study sample included 1018 first-year STEM majors who had entered the same large, public, urban, Hispanic-serving, research university in the Southeastern U.S. between 2010 and 2012. The research design involved the use of hierarchical logistic regression to determine the significance of utilizing the four independent variables to develop models for predicting success in math and science. The resulting data indicated that the overall model of predictors (which included all four predictor variables) was statistically significant for predicting those students who passed their first math course and for predicting those students who passed their first science course. Individually, all four predictor variables were found to be statistically significant for predicting those who had passed math, with the unweighted high school GPA and the highest math taken in high school accounting for the largest amount of unique variance. Those two variables also improved the regression model’s percentage of correctly predicting that dependent variable. The only variable that was found to be statistically significant for predicting those who had passed science was the students’ unweighted high school GPA. Overall, the results of my study have been offered as my contribution to the literature on predicting first-year student success, especially within the STEM disciplines.