Mindfulness: An Ancient Wisdom for the Reconceptualisation of Modern Education in the Complex World

Exploring the new science of emergence allows us to create a very different classroom than how the modern classroom has been conceptualised under the mentality of efficiency and output. Working on the whole person, and not just the mind, we see a shift from the epistemic pillars of truth to more ontological concerns as regards student achievement in our post-Modern and critical discourses. It is important to understand these shifts and how we are to transition our own perception and mentality not only in our research methodologies but also our approach to conceptualisations of issues in education and sustainability. We can no longer think linearly to approach complex problems or advocate for education and disregard our interconnectedness insofar as it enhances our children’s education. We must, therefore, contemplate and transition to a world that is ecological and not mechanical, complex and not complicated—in essence, we must work to link mind-body with self-environment and transcend these in order to bring about an integration toward a sustainable future. A fundamental shift in consciousness and perception may implicate our nature of creating dichotomous entities in our own microcosms, yet postmodern theorists assume, a priori, that these dualities can be bridged in naturalism alone. I, on the other hand, embrace metaphysics to understand the implicated modern classroom in a hierarchical context and ask: is not the very omission of metaphysics in postmodern discourse a symptom from an education whose foundation was built in its absence? The very dereliction of ancient wisdom in education is very peculiar indeed. Western mindfulness may play a vital component in consummating pragmatic idealism, but only under circumstances admitting metaphysics can we truly transcend our limitations, thereby placing Eastern Mindfulness not as an ecological component, but as an ecological and metaphysical foundation.

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.