Educational accountability tests, social and legal inclusion approaches to discrimination for students with disability : a national case study from Australia

The UN Convention on the Rights of Persons with Disability (CRPD) promotes equal and full participation by children in education. Equity of educational access for all students, including students with disability, free from discrimination, is the first stated national goal of Australian education (MCEETYA 2008). Australian federal disability discrimination law, the Disability Discrimination Act 1992 (DDA), follows the Convention, with the federal Disability Standards for Education 2005 (DSE) enacting specific requirements for education. This article discusses equity of processes for inclusion of students with disability in Australian educational accountability testing, including international tests in which many countries participate. The conclusion drawn is that equitable inclusion of students with disability in current Australian educational accountability testing in not occurring from a social perspective and is not in principle compliant with law. However, given the reluctance of courts to intervene in education matters and the uncertainty of an outcome in any court consideration, the discussion shows that equitable inclusion in accountability systems is available through policy change rather than expensive, and possibly unsuccessful, legal challenges.

Does the social inclusion discourse add value?

The election of a national Labor Government in 2007 saw ‘social inclusion’ emerge as Australia’s overarching social policy agenda. Being ‘included’ has since been defined as being able to ‘have the resources, opportunities and capabilities needed to learn, work, engage and have a voice’. Various researchers have adopted the social inclusion concept to construct a multi-dimensional framework for measuring disadvantage, beyond poverty alleviation. This research program has enabled various forms of statistical modelling based on some agreement about what it means to be ‘included’ in society. At the same time it is acknowledged that social inclusion remains open and contestable and can be used in the name of both progressive and more punitive programs and policies. This ambiguity raises questions about whether the social inclusion framework, as it is presently defined, has the potential to be a progressive and transformative discourse. In this paper we examine whether the Australian social inclusion agenda has the capacity to address social inequality in a meaningful way, concluding with a discussion about the need to understand social inequality and social disadvantage in relational terms.

Justice and social inclusion policies

Since 2008 the social policy of Australia’s Labor government (in office since 2007) has been framed by a commitment to ‘social inclusion’. In this respect Australia belatedly aligned itself with policy imaginaries already widely, if variably, adopted in Europe (Atkinson & Davoudi 2000; Levitas et al 2007; Buckmaster & Thomas 2009). This framework has been self-consciously identified as what Labor governments are equipped to do. Framed by the post-2007 global financial crisis and agreeing with claims that ‘excessive greed’ and irresponsibility on the part of financial markets sponsored that calamity, the Labor government vigorously promoted its ‘social democratic’ credentials. Former Prime Minister Rudd has explained this meant that Australia would no longer adopt a neo-liberal orientation promoting unrestrained capitalism (Rudd 2009).

Wherefore art thou, inclusion? Analysing the development of inclusive education in New South Wales, Finland and Alberta

Over the last two decades, moves toward “inclusion” have prompted change in the formation of education policies, schooling structures and pedagogical practice. Yet, exclusion through the categorisation and segregation of students with diverse abilities has grown; particularly for students with challenging behaviour. This paper considers what has happened to inclusive education by focusing on three educational jurisdictions known to be experiencing different rates of growth in the identification of special educational needs: New South Wales (Australia), Alberta (Canada) and Finland (Europe). In our analysis, we consider the effects of competing policy forces that appear to thwart the development of inclusive schools in two of our case-study regions.

Developing capabilities for social inclusion : engaging diversity through inclusive school communities

The effort to make schools more inclusive, together with the pressure to retain students until the end of secondary school, has greatly increased both the number and educational requirements of students enrolling in their local school. Of critical concern, despite years of research and improvements in policy, pedagogy and educational knowledge, is the enduring categorisation and marginalization of students with diverse abilities. Research has shown that it can be difficult for schools to negotiate away from the pressure to categorise or diagnose such students, particularly those with challenging behaviour. In this paper, we highlight instances where some schools have responded to increasing diversity by developing new cultural practices to engage both staff and students; in some cases, decreasing suspension while improving retention, behaviour and performance.

The inclusion lottery : who's in and who's out? Tracking inclusion and exclusion in New South Wales government schools

The last few decades have witnessed a broad international movement towards the development of inclusive schools through targeted special education funding and resourcing policies. Student placement statistics are often used as a barometer of policy success but they may also be an indication of system change. In this paper, trends in student enrolments from the Australian state of New South Wales are considered in an effort to understand what effect inclusive education has had in this particular region of the world.

An illusory interiority : interrogating the discourse/s of inclusion

It is generally accepted that the notion of inclusion derived or evolved from the practices of mainstreaming or integrating students with disabilities into regular schools. Halting the practice of segregating children with disabilities was a progressive social movement. The value of this achievement is not in dispute. However, our charter as scholars and cultural vigilantes (Slee & Allan, 2001) is to always look for how we can improve things; to avoid stasis and complacency we must continue to ask, how can we do it better? Thus, we must ask ourselves uncomfortable questions and develop a critical perspective that Foucault characterised as an ‘ethic of discomfort’ (Rabinow & Rose, 2003, p. xxvi) by following the Nietzscheian principle where one acts “counter to our time and thereby on our time… for the benefit of a time to come” (Nietzsche, 1874, p. 60 in Rabinow & Rose, 2003, p. xxvi). This paper begins with a fundamental question for those participating in inclusive education research and scholarship – when we talk of including, into what do we seek to include?

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations

The pioneering work of Runge and Kutta a hundred years ago has ultimately led to suites of sophisticated numerical methods suitable for solving complex systems of deterministic ordinary differential equations. However, in many modelling situations, the appropriate representation is a stochastic differential equation and here numerical methods are much less sophisticated. In this paper a very general class of stochastic Runge-Kutta methods is presented and much more efficient classes of explicit methods than previous extant methods are constructed. In particular, a method of strong order 2 with a deterministic component based on the classical Runge-Kutta method is constructed and some numerical results are presented to demonstrate the efficacy of this approach.

Numerical solutions of stochastic differential equations – implementation and stability issues

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively.

A variable stepsize implementation for stochastic differential equations

Stochastic differential equations (SDEs) arise from physical systems where the parameters describing the system can only be estimated or are subject to noise. Much work has been done recently on developing higher order Runge-Kutta methods for solving SDEs numerically. Fixed stepsize implementations of numerical methods have limitations when, for example, the SDE being solved is stiff as this forces the stepsize to be very small. This paper presents a completely general variable stepsize implementation of an embedded Runge Kutta pair for solving SDEs numerically; in this implementation, there is no restriction on the value used for the stepsize, and it is demonstrated that the integration remains on the correct Brownian path.

Adams-Type Methods for the Numerical Solution of Stochastic Ordinary Differential Equations

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively. (C) 2000 Elsevier Science B.V. All rights reserved.