Explaining the success of the world's leading education systems: the case of Singapore

International comparative data on student performance has led McKinsey & Company, among others, to suggest that education systems will inexorably converge in their developmental trajectories with principals and schools enjoying more autonomy. This article challenges these assumptions through referencing Singapore where schools and professionals are still tightly controlled in key resources, curricula and assessment, and where other key factors contribute to its success – thereby evidencing multiple pathways to success

Re-conceptualising learning-centred (instructional) leadership: an obsolete concept in need of renovation

For more than thirty years, ‘instructional leadership’ has been at the forefront of research and practice in school effectiveness and improvement. Governments, employers, universities and professional developers, all see it as a mainstay of raising school and student performance. Wave-after-wave of educational policy reforms during this period have changed school environments, widening and deepening the (instructional) leadership roles and functions of principals and other school leaders. Terminology has changed – while Americans still use ‘instructional leadership’, others prefer ‘learning-centred’ and ‘leadership-for -learning’, disputing whether they encompass the same or different meanings. Yet curiously, the concept itself – as defined and measured by academic researchers and scholars - has changed relatively little since Hallinger and Murphy’s first seminal contribution in 1985. This paper argues the case for wholesale renovation of the concept if it is to maintain relevance going forward. The case is supported by important and powerful trends in policy and practice

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

Widening of Reinforced Concrete Bridges – Extension of Pier Cap Beams with CFRP Composites

Increase in traffic load dictates widening highway bridges rather than construction of new bridges, as it offers an economical solution. Highway bridge widening is usually accomplished by the construction of additional new bridge piers, however, large amount of construction work and extensive use of heavy plant and machinery could result in very high cost. For limited widening of bridges, e.g. addition of one driving/emergency lane, extension of the pier cap beam offers an attractive solution. Due to additional load resulting from the widened bridge, strengthening of extended cap beams may be needed. In addition, depending on the strength of the existing pier column, limited strengthening of the column will probably be needed. This research project presents a bridge cap beam extension and reinforcing system, which is considered an alternative of constructing new piers for bridge widening projects. Experimental results and numerical modeling of quarter-scaled reinforced concrete hammerhead non-prismatic pier cap beams, extended on verges and reinforced with different CFRP systems are presented, followed by development of a practical analytical model. In addition, a column strengthening system, which provides a continuous load path to transfer additional moment to the foundation, was presented in this study. The experimental work was to investigate the structural performance of this particular type of beams with different reinforcing systems, such as concrete jacket, CFRP sheets and pre-saturated CFRP laminates with various anchor systems. Thirteen pier cap beam specimens were tested to evaluate the effect of the reinforcing systems on ultimate strength, stiffness and ductility. In addition, five full-scaled rectangular RC columns strengthened with either concrete jacket or vertical CFRP plates were tested under cyclic loading, flexural and cyclic performance of strengthened columns, and strain distribution on CFRP composites were investigated. A numerical study based on finite element modeling was performed to investigate the failure mechanism of tested beams. A 3-D finite element model was developed and verified against experimental results. A good agreement between finite element results and experimental results was achieved in terms of failure mode, ultimate capacity and load-deflection response. The experimental work and finite element results demonstrated the feasibility of proposed cap beam extension system. All of the tested reinforcing systems are effective in improving flexural strength of the extended cap beams. Among the investigated reinforcing systems, the fully wrapped CFRP sheet offers the most efficient solution for proposed system. Based on these observations, an iterative based simple practical analytical model was developed to predict the ultimate capacity of extended cap beams with CFRP reinforcing systems. A few design and construction recommendations are presented at the end of the dissertation