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