The dual role of students with behavioural problems: an analysis of their experiences as bullies and victims /

The present study examined the bullying experiences of a group of students, age 10-14 years, identified as having behaviour problems. A total often students participated in a series of mixed methodology activities, including self-report questionnaires, story telling exercises, and interview style joumaling. The main research questions were related to the prevalence of bully/victims and the type of bullying experiences in this population. Questionnaires gathered information about their involvement in bullying, as well as about psychological risk factors including normative beliefs about antisocial acts, impulsivity, problem solving, and coping strategies. Journal questions expanded on these themes and allowed students to explain their personal experiences as bullies and victims as well as provide suggestions for intervention. The overall results indicated that all of the ten students in this sample have participated in bullying as both a bully and a victim. This high prevalence of bully/victim involvement in students from behavioural classrooms is in sharp contrast with the general population where the prevalence is about 33%. In addition, a common thread was found that indicated that these students who participated in this study demonstrate characteristics of emotionally dysregulated reactive bullies. Theoretical implication and educational practices are discussed.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.