Extensive faculty development must be an integral focus of any university's system change efforts. At Northwestern Oklahoma State University, a small regional university in the United States, a federally funded Preparing Tomorrows Teachers to use Technology (PT3) project, known as the TEACHNOLOGY Program, is providing faculty development through a collaborative consultation model. This model has been a very effective model that has resulted in systemic change in faculty member's technology use, particularly the integration of technology into a wide variety of teaching and learning activities.

This research examined the effects of a process of digital video editing used to create a multimedia portfolio on the quality of teacher candidates' critical reflections. Subjects were randomly assigned to one of three groups: control, experimental-reflection, and experimental-reflection with multimedia production. The difference between the control group and the experimental groups indicate that digital video editing does have an impact on teacher candidate's ability to reflect.

We combine the concept of evolutionary search with the systematic search concepts of arc revision and hill climbing to form a hybrid system that quickly finds solutions to static and dynamic constraint satisfaction problems (CSPs). Furthermore, we present the results of two experiments. In the first experiment, we show that our evolutionary hybrid outperforms a well-known hill climber, the iterative descent method (IDM), on a test suite of 750 randomly generated static CSPs. These results show the existence of a "mushy region" which contains a phase transition between CSPs that are based on constraint networks that have one or more solutions and those based on networks that have no solution. In the second experiment, we use a test suite of 250 additional randomly generated CSPs to compare two approaches for solving CSPs. In the first method, all the constraints of a CSP are known by the hybrid at run-time. We refer to this method as the static method for solving CSPs. In the second method, only half of the constraints of a CSPs are known at run-time. Each time that our hybrid system discovers a solution that satisfies all of the constraints of the current network, one additional constraint is added. This process of incrementally adding constraints is continued until all the constraints of a CSP are known by the algorithm or until the maximum number of individuals has been created. We refer to this second method as the dynamic method for solving CSPs. Our results show hybrid evolutionary search performs exceptionally well in the presence of dynamic (incremental) constraints, then also illuminate a potential hazard with solving dynamic CSPs.