'I never imagined I'd have to teach these children': Providing authentic learning experiences for secondary pre-service teachers in teaching students with special needs

This article describes a collaborative and cross-curricula initiative undertaken in the School of Education at the University of Queensland, Brisbane, Australia. The project involved developing an integrated approach to providing professional year pre-service secondary teacher education students with experiences that would assist them to develop their knowledge and skills to teach students with special needs in their classrooms. These experiences were undertaken in the authentic teaching and learning context of a post-school literacy program for young adults with intellectual disabilities. In preliminary interviews pre-service teachers revealed that they lacked experience, knowledge and understanding related to teaching students with special needs, and felt that their teacher education program lacked focus in this field. This project was developed in response to these expressed needs. Through participating in the project, pre-service teachers' knowledge and understanding about working with students with diverse learning needs were developed as they undertook real and purposeful tasks in an authentic context.

A responsible gambling strategy for older Queenslanders: Final Report

This study investigates the needs, experiences, behaviours and attitudes of older Queenslanders who participate in gambling. It aims to understand the special needs and circumstances of older Queensland gamblers which might make them particularly vulnerable to problem gambling behaviour, or other negative effects of gambling. The findings of the research will provide an evidence base for the development of initiatives and policies that can address the specific prevention, protection and rehabilitation needs of older gamblers. This is with a particular view to informing the ongoing development and implementation of the Queensland Government’s Responsible Gambling Strategy and its voluntary industry code – the Queensland Responsible Gambling Code of Practice.

Passive air sampling for persistent organic pollutants: Introductory remarks to the special issue

There have been a number of developments in the need, design and use of passive air samplers (PAS) for persistent organic pollutants (POPs). This article is the first in a Special Issue of the journal to review these developments and some of the data arising from them. We explain the need and benefit of developing PAS for POPs, the different approaches that can be used, and highlight future developments and needs. (c) 2006 Elsevier Ltd. All rights reserved.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).