Evaluating the Outcomes of a Peer-Mentoring Program for Transitioning Postsecondary Students

A peer-mentoring program was initiated in 2003 for students in an introductory biology course at a university in Ontario, Canada. Students could attend up to 5 peer-mentoring sessions during the 12-week fall semester. Quantitative-survey, participation, and academic data spanning 5 years were reviewed for the purpose of evaluating the program. An objectives-oriented approach was used to determine if the program was meeting its goals to improve students' introductory biology grades, facilitate transitioning experiences, and encourage students to pursue studies in biology. Data analysis revealed characteristics of participants and showed that students who participated in the program felt that it was a valuable experience. Students attending 3 or more sessions performed significantly better in their introductory biology courses than those attending fewer sessions. There were no indications that the peer-mentoring program had any impact on students' perceptions of transitioning to university or on their program selection preferences. Recommendations are made to improve the peer-mentoring program to better align its components and objectives.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.