The effectiveness of training educators on their ability to identify function of problem behavior

The purpose of this study was to investigate the effectiveness of training educators in the pre-behavioral intervention process of functional behavioral assessment. An original evaluation instrument was developed entitled, The Survey for Students Exhibiting Challenging Behavior. The instrument included measures of participating educators, knowledge of function of problem behavior and their ability to generate recommendations for a behavior intervention plan. The instrument was distributed to schools in a large urban district and completed by special educators. Educators trained and untrained in the functional behavioral assessment process were compared in the study. ^ The study incorporated a post-test only design. All instruments were analyzed using a factorial ANOVA. Those educators who were trained in the district functional behavioral assessment program answered general questions related to function of problem behavior significantly better than those who did not receive training. There is no significant difference between educators on their ability to generate recommendations for behavior intervention plans. It is important that educators receive training in functional behavioral assessment to gain an understanding of the basic notions being function of problem behavior. Current training does not translate into educators' ability to make strong recommendations for behavior intervention plans. ^

The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit

The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.