A generative design system based on evolutionary and mathematical functions

Previous work by Professor John Frazer on Evolutionary Architecture provides a basis for the development of a system evolving architectural envelopes in a generic and abstract manner. Recent research by the authors has focused on the implementation of a virtual environment for the automatic generation and exploration of complex forms and architectural envelopes based on solid modelling techniques and the integration of evolutionary algorithms, enhanced computational and mathematical models. Abstract data types are introduced for genotypes in a genetic algorithm order to develop complex models using generative and evolutionary computing techniques. Multi-objective optimisation techniques are employed for defining the fitness function in the evaluation process.

Building mentoring capacities in experienced teachers

While teacher education equips beginning teachers with critical knowledge and skills about teaching and fosters an understanding of learning in and from teaching some of the most critical elements of teaching are only learned in the workplace when beginning teachers commence their professional teaching careers. This transition to professional practice may be facilitated by mentoring from a more experienced teacher. Expert mentoring assists beginning teachers to build their teaching capacities more quickly and also lays the foundation for innovative professional practice. However, the presence of a mentor alone is not sufficient with the success of mentoring reliant on the skills and knowledge of mentors. Mentoring relationships are most effective when mentors are trained for their roles. While mentor preparation is the single most important factor in contributing to mentoring success, few teachers receive formal training to prepare them adequately for mentoring roles. The purpose of this paper is to report on the implementation of a mentoring development program designed to build mentoring capacities in experienced teachers. The program was trialled in a school in rural Australia. A range of qualitative data was collected from participants over the duration of the mentoring program and follow up data collected six months subsequent to the conclusion of the program.

Mentoring preservice teachers in primary mathematics

A literature-based instrument gathered data about 147 final-year preservice teachers’ perceptions of their mentors’ practices related to primary mathematics teaching. Five factors characterized effective mentoring practices in primary mathematics teaching had acceptable Cronbach alphas, that is, Personal Attributes (mean scale score=3.97, SD [standard deviation]=0.81), System Requirements (mean scale score=2.98, SD=0.96), Pedagogical Knowledge (mean scale score=3.61, SD=0.89), Modelling (mean scale score=4.03, SD=0.73), and Feedback (mean scale score=3.80, SD=0.86) were .91, .74, .94, .89, and .86 respectively. Qualitative data (n=44) investigated mentors’ perceptions of mentoring these preservice teachers, including identification of successful mentoring practices and ways to enhance practices.

Employing the five-factor mentoring instrument : analysing mentoring practices for teaching primary science.

Primary science education is a concern around the world and quality mentoring within schools can develop preservice teachers’ practices. A five-factor model for mentoring has been identified, namely, personal attributes, system requirements, pedagogical knowledge, modelling, and feedback. Final-year preservice teachers (mentees, n=211) from three Turkish universities were administered a previously validated instrument to gather perceptions of their mentoring in primary science teaching. ANOVA indicated that each of these five factors was statistically significant (p<.001) with mean scale scores ranging from 3.36 to 4.12. Although mentees perceived their mentors to provide evaluation feedback (95%), model classroom management (88%), guide their preparation (96%), and outline the science curriculum (92%), the majority of mentors were perceived not to assist their mentees in 10 of the 34 survey items. Professional development programmes that target the specific needs of these mentors may further enhance mentoring practices for advancing primary science teaching.

Principals as architects of formal mentoring programs in schools

An important responsibility of principals in schools is fostering a healthy learning-rich environment for both staff and students. Previous research (Duignan & Gurr, 2008; Ehrich, 1998; Leithwood & Day, 2007; Nias, Southworth, & Campbell, 1992) has shown that effective principals create opportunities for teachers to learn with and from each other. For instance, they are involved in establishing supportive structures and creating environments for collaboration and learning to take place (Leithwood & Day, 2007). They do this in a variety of ways such as providing resources and professional development opportunities, structuring time for staff to learn and work together, and establishing a host of other conditions to facilitate learning and sharing.

Much ado about mentoring : some learnings from the field

Mentoring has been the focus of both research and writing across a range of professional fields including, for example, education, business, medecine, nursing and law for decades. Even so it has been argued by researchers that much less confusion continues to surround its meaning and understanding. Part of this confusion lies in the fact it has been described in many ways. Some writing in the field focuses on it as a workplace activity for men and womean, a developmental process for novices and leaders alike, a career tool for enhancing promotion, an affirmative action strategy for members of minority groups, and a human resource development strategy used in organisations (Ehrich and Hansford, 1999).

Krylov subspace methods for approximating functions of symmetric positive definite matrices with applications to applied statistics and anomalous diffusion

Matrix function approximation is a current focus of worldwide interest and finds application in a variety of areas of applied mathematics and statistics. In this thesis we focus on the approximation of A^(-α/2)b, where A ∈ ℝ^(n×n) is a large, sparse symmetric positive definite matrix and b ∈ ℝ^n is a vector. In particular, we will focus on matrix function techniques for sampling from Gaussian Markov random fields in applied statistics and the solution of fractional-in-space partial differential equations. Gaussian Markov random fields (GMRFs) are multivariate normal random variables characterised by a sparse precision (inverse covariance) matrix. GMRFs are popular models in computational spatial statistics as the sparse structure can be exploited, typically through the use of the sparse Cholesky decomposition, to construct fast sampling methods. It is well known, however, that for sufficiently large problems, iterative methods for solving linear systems outperform direct methods. Fractional-in-space partial differential equations arise in models of processes undergoing anomalous diffusion. Unfortunately, as the fractional Laplacian is a non-local operator, numerical methods based on the direct discretisation of these equations typically requires the solution of dense linear systems, which is impractical for fine discretisations. In this thesis, novel applications of Krylov subspace approximations to matrix functions for both of these problems are investigated. Matrix functions arise when sampling from a GMRF by noting that the Cholesky decomposition A = LL^T is, essentially, a `square root' of the precision matrix A. Therefore, we can replace the usual sampling method, which forms x = L^(-T)z, with x = A^(-1/2)z, where z is a vector of independent and identically distributed standard normal random variables. Similarly, the matrix transfer technique can be used to build solutions to the fractional Poisson equation of the form ϕn = A^(-α/2)b, where A is the finite difference approximation to the Laplacian. Hence both applications require the approximation of f(A)b, where f(t) = t^(-α/2) and A is sparse. In this thesis we will compare the Lanczos approximation, the shift-and-invert Lanczos approximation, the extended Krylov subspace method, rational approximations and the restarted Lanczos approximation for approximating matrix functions of this form. A number of new and novel results are presented in this thesis. Firstly, we prove the convergence of the matrix transfer technique for the solution of the fractional Poisson equation and we give conditions by which the finite difference discretisation can be replaced by other methods for discretising the Laplacian. We then investigate a number of methods for approximating matrix functions of the form A^(-α/2)b and investigate stopping criteria for these methods. In particular, we derive a new method for restarting the Lanczos approximation to f(A)b. We then apply these techniques to the problem of sampling from a GMRF and construct a full suite of methods for sampling conditioned on linear constraints and approximating the likelihood. Finally, we consider the problem of sampling from a generalised Matern random field, which combines our techniques for solving fractional-in-space partial differential equations with our method for sampling from GMRFs.

Heart versus mind : the functions of emotional and cognitive loyalty

While there is substantial research on attitudinal and behavioral loyalty, the deconstruction of attitudinal loyalty into its two key components – emotional and cognitive loyalty – has been largely ignored. Despite the existence of managerial strategies aimed at increasing each of these two components, there is little academic research to support these managerial efforts. This paper seeks to advance the understanding of emotional and cognitive brand loyalty by examining the psychological function that these dimensions of brand loyalty perform for the consumer. We employ Katz’s (1960) four functions of attitudes (utilitarian, knowledge, value-expression, ego-defence) to investigate this question. Surveys using a convenience sample were completed by 268 consumers in two metropolitan cities on a variety of goods, services and durable products. The relationship between the functions and dimensions of loyalty were examined using MANOVA. The results show that both the utilitarian and knowledge functions of loyalty are significantly positively related to cognitive loyalty while the ego-defensive function of loyalty is significantly positively related to emotional loyalty. The results for the value-expressive function of loyalty were nonsignificant.