Mentoring experiences as professional development for leaders in environmental education : the cascade of influence

Environmental education is a field which has only come of age since the late nineteen sixties. While its content and practice have been widely debated and researched, its leadership has been minimally studied and, therefore, is only partially understood. The role of mentoring in the development of leaders has been alluded to, but has attracted scant research. Therefore, this study explores the importance of mentoring during the personal and professional development of leaders in environmental education. Four major research questions were investigated. Firstly, have leaders been men to red during their involvement with environmental education? Secondly, when and how has that mentoring taken place? Thirdly, what was the personal and professional effectiveness of the mentoring relationship? Fourthly, is there any continuation of the mentoring process which might be appropriate for professional development within the field of environmental education? Leaders were solicited from a broad field of environmental educators including teachers, administrators, academics, natural resource personnel, business and community persons. They had to be recognized as active leaders across several environmental education networks. The research elicited qualitative and quantitative survey data from fifty seven persons in Queensland, Australia and Colorado, USA. Seventeen semi-structured interviews were subsequently conducted with selected leaders who had nominated their mentors. This led to a further thirteen 'linked interviews' with some of the mentors' mentors and new mentorees. The interview data is presented as four cases reflecting pairs, triads, chains and webs of relationships- a major finding of the research process. The analysis of the data from the interviews and the surveys was conducted according to a grounded theory approach and was facilitated by NUD.IST, a computer program for non-numerical text analysis. The findings of the study revealed many variations on the classical mentoring patterns found in the literature. Gender and age were not seen as mportant factors, as there were examples of contemporaries in age, older men to younger women, older women to younger men, and women to women. Personal compatibility, professional respect and philosophical congruence were critical. Mentoring was initiated from early, mid and late career stages with the average length of the relationship being fourteen years. There was seldom an example of the mentoree using the mentor for hierarchical career climbing, although frequent career changes were made. However, leadership actions were found to increase after the intervention of a mentoring relationship. Three major categories of informal mentoring were revealed - perceived,acknowledged and deliberate. Further analysis led to the evolution of the core concept, a 'cascade of influence'. The major finding of this study was that this sample of leaders, mentors and new mentorees moved from the perception of having been mentored to the acknowledgment of these relationships and an affirmation of their efficacy for both personal and professional growth. Hence, the participants were more likely to continue future mentoring, not as a serendipitous happening, but through a deliberate choice. Heightened awareness and more frequent 'cascading' of mentoring have positive implications for the professional development of future leaders in environmental education in both formal and informal settings. Effective mentoring in environmental education does not seek to create 'clones' of the mentors, but rather to foster the development of autonomous mentorees who share a philosophical grounding. It is a deliberate invitation to 'join the clan'.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.