The overall goal of this study was to better understand how the academic system affects change in instructional practices, referred to as instructional change, in engineering education. To accomplish this goal, and acknowledging the complex nature of academia, I used a technique designed to understand complex systems called System Dynamics Modeling. With such technique, I created a conceptual System Dynamics Model (SDM) that illustrates how the factors in the academic system interact dynamically to drive or hinder faculty motivation to adopt Research-based Instructional Strategies (RBIS) in their courses. The creation of this model followed a process that combined research literature with data gathered from 17 professors at an Engineering Department in another country. The model was constructed through an iterative process of systematically reviewing the literature, gather empirical data and creating Causal Loop Diagrams (CLD). The CLD are representations of the different causal relationships between elements in a system which ultimately create what we called virtuous or vicious (reinforcing) cycles and balancing cycles. The whole idea was not to find the causes for professors' motivation to change but how the factors in the academic system reinforce or limit such motivation. With this model I offered a different answer to the calls for change in engineering education toward increasing the pedagogical quality of our learning environments. My biggest argument is that previous instructional change initiatives have yielded low to moderate success, because effective instructional change would require a perspective that accounts for the complex nature of academia. With this study I am providing a different understanding of instructional change by using a system perspective that shows the interactions of elements within a complex system that ultimately influences faculty to adopt RBIS in their courses. / Doctor of Philosophy / The overall goal of this study was to better understand how the academic system affects change in instructional practices, referred to as instructional change, in engineering education. To accomplish this goal, and acknowledging the complex nature of academia, I used a technique designed to understand complex systems called System Dynamics Modeling. With such technique, I created a conceptual System Dynamics Model (SDM) that illustrates how the factors in the academic system interact dynamically to drive or hinder faculty motivation to adopt Research-based Instructional Strategies (RBIS) in their courses. The creation of this model followed a process that combined research literature with data gathered from 17 professors at an Engineering Department in another country. The model was constructed through an iterative process of systematically reviewing the literature, gather empirical data and creating Causal Loop Diagrams (CLD). The CLD are representations of the different causal relationships between elements in a system which ultimately create what we called virtuous or vicious (reinforcing) cycles and balancing cycles. The whole idea was not to find the causes for professors’ motivation to change but how the factors in the academic system reinforce or limit such motivation. With this model I offered a different answer to the calls for change in engineering education toward increasing the pedagogical quality of our learning environments. My biggest argument is that previous instructional change initiatives have yielded low to moderate success, because effective instructional change would require a perspective that accounts for the complex nature of academia. With this study I am providing a different understanding of instructional change by using a system perspective that shows the interactions of elements within a complex system that ultimately influences faculty to adopt RBIS in their courses.

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This quantitative study analyzed the influence of individual factors and institutional context on faculty participation in online teaching at public higher education institutions in the United States. Through an ex post facto design, cause and effect relationships were explored using statistical analysis of a large national data set. Variables in the data set directly related to the areas of interest in this study included interest in teaching, student-centered pedagogy, autonomy and control, instructional support, and institutional climate. Factors related to interest in teaching and institutional reward were statistically significant (p