Creating Active Futures : Edited Proceedings of the 26th ACHPER International Conference

Physical inactivity has become a major cause of the global increase in non-communicable disease (World Health Organisation, 2009}. In 2008, the World Economic Forum called for employers to be proactive in the prevention of non-communicable diseases in the workforce. A significant contributor to the development of a healthy workforce is a reliable pool of employees who are receptive to and aware of healthy lifestyle practices even before becoming employed. Health and Physical Education (HPE) is often stereotyped as 'doing sport'. However, if HPE is to play a part in the development of a healthy workforce, then the HPE learning environment must be about creating meaningful learning for all, which is clearly more than the creation of elite athletes. The ultimate aim of health and physical educators must be about 1) developing lifelong and habitual physical activity; 2) developing generic physical skills; 3) inspiring holistic and positive emotional attitudes and 4) instilling a focus on evidence based knowledge as a framework for inspiring active citizenship. As a response to the worldwide move to the development of healthier people, Australia currently has a strong momentum for an expanded and more unified role for HPE within a potential National curriculum. Other countries have engaged in such a process and much can be learned from their experiences of the process. The 2009 Australian Council for Health, Physical Education and Recreation (ACHPER) conference was a landmark conference that included an International group of experts from all continents and twenty three countries. Creating Active Futures: Edited Proceedings of the 26th ACHPER International Conference is an amalgamation of research and professional perspectives presented at the conference. The papers in this volume emerged from those presented for peer review, rather than through seeking specific articles. This volume is divided into sections based on the five conference themes: 1) Issues in Health and Physical Education (HPE) Pedagogy; 2) Practical Application of Science in HPE; 3) Lifestyle Enhancement; 4) Developing Sporting Excellence; 5) Contemporary Games Teaching. The 'Issues in HPE Pedagogy' section provides a diverse set of perspectives on teaching HPE with papers from a range of topics that include first aid, philosophy, access, cultural characteristics, methods and teaching styles, curriculum, qualifications and emotional development. The second section links science to teaching HPE and provides a range of valuable information on injury prevention, information technology, personality and skill development. Section 3 is a collection of writings and research about Lifestyle Enhancement. Topics include the important role of adventure, the natural world, curriculum, migrant viewpoints, beliefs and globally focused programs in the development of active citizens. The section on sporting excellence contains papers that undertake to explain an aspect of excellence in sport. The last section of this volume highlights some contemporary views on teaching games.

Early childhood teachers' mathematical content knowledge

The process of becoming numerate begins in the early years. According to Vygotskian theory (1978), teachers are More Knowledgeable Others who provide and support learning experiences that influence children’s mathematical learning. This paper reports on research that investigates three early childhood teachers mathematics content knowledge. An exploratory, single case study utilised data collected from interviews, and email correspondence to investigate the teachers’ mathematics content knowledge. The data was reviewed according to three analytical strategies: content analysis, pattern matching, and comparative analysis. Findings indicated there was variation in teachers’ content knowledge across the five mathematical strands and that teachers might not demonstrate the depth of content knowledge that is expected of four year specially trained early years’ teachers. A significant factor that appeared to influence these teachers’ content knowledge was their teaching experience. Therefore, an avenue for future research is the investigation of factors that influence teachers’ content numeracy knowledge.

Adult learning in a workplace context

In light of the changing nature of contemporary workplaces, this chapter attempts to identify employer expectations and the associated skills required to workers to function effectively in such workplaces. Workers are required to participate in informed discussion about their specific jobs and to contribute to the overall development of organisations. This requires deep understanding of domain-specific knowledge, which at times can be very complex. Workers are also required to take responsibility for their actions and are expected to be flexible so that they can be deployed to other related jobs depending on demand. Finally, workers are expected to be pro-active, be able to anticipate situations and continuously update their knowledge to address new situations. This chapter discusses the nature of knowledge and skills that will facilitate the above qualities.

Towards a model for the analysis and description of mathematical knowledge and understanding

Mathematics education literature has called for an abandonment of ontological and epistemological ideologies that have often divided theory-based practice. Instead, a consilience of theories has been sought which would leverage the strengths of each learning theory and so positively impact upon contemporary educational practice. This research activity is based upon Popper’s notion of three knowledge worlds which differentiates the knowledge shared in a community from the personal knowledge of the individual, and Bereiter’s characterisation of understanding as the individual’s relationship to tool-like knowledge. Using these notions, a re-conceptualisation of knowledge and understanding and a subsequent re-consideration of learning theories are proposed as a way to address the challenge set by literature. Referred to as the alternative theoretical framework, the proposed theory accounts for the scaffolded transformation of each individual’s unique understanding, whilst acknowledging the existence of a body of domain knowledge shared amongst participants in a scientific community of practice. The alternative theoretical framework is embodied within an operational model that is accompanied by a visual nomenclature with which to describe consensually developed shared knowledge and personal understanding. This research activity has sought to iteratively evaluate this proposed theory through the practical application of the operational model and visual nomenclature to the domain of early-number counting, addition and subtraction. This domain of mathematical knowledge has been comprehensively analysed and described. Through this process, the viability of the proposed theory as a tool with which to discuss and thus improve the knowledge and understanding with the domain of mathematics has been validated. Putting of the proposed theory into practice has lead to the theory’s refinement and the subsequent achievement of a solid theoretical base for the future development of educational tools to support teaching and learning practice, including computer-mediated learning environments. Such future activity, using the proposed theory, will advance contemporary mathematics educational practice by bringing together the strengths of cognitivist, constructivist and post-constructivist learning theories.

Indigenous mathematics : creating an equitable learning environment

This chapter examines how a change in school leadership can successfully address competencies in complex situations and thus create a positive learning environment in which Indigenous students can excel in their learning rather than accept a culture that inhibits school improvement. Mathematics has long been an area that has failed to assist Indigenous students in improving their learning outcomes, as it is a Eurocentric subject (Rothbaum, Weisz, Pott, Miyake & Morelli, 2000, De Plevitz, 2007) and does not contextualize pedagogy with Indigenous culture and perspectives (Matthews, Cooper & Baturo, 2007). The chapter explores the work of a team of Indigenous and non-Indigenous academics from the YuMi Deadly Centre who are turning the tide on improving Indigenous mathematical outcomes in schools and in communities with high numbers of Aboriginal and Torres Strait Islander students.

Accommodating serendipity in the active studio

This paper presents a series of ongoing experiments to facilitate serendipity in the design studio through a diversity of delivery modes. These experiments are conducted in a second year architectural design studio, and include physical, dramatic and musical performance. The act of designing is always exploratory, always seeking an unknown resolution, and the ability to see and capture the value in the unexpected is a critical aspect of such creative design practice. Engaging with the unexpected is however a difficult ability to develop in students. Just how can a student be schooled in such abilities when the challenge and the context are unforeseeable? How can students be offered meaningful feedback about an issue that cannot be predicted, when feedback comes in the form of extrinsic assessment from a tutor? This project establishes a number of student activities that seek to provide intrinsic feedback from the activity itself. Further to this, the project seeks to heighten student engagement with the project through physical expression and performance: utilising more of the students’ senses than just vision and hearing. Diana Laurillard’s theories of conversational frameworks (2002) are used to interrogate the act of dramatic performance as an act of learning, with particular reference to the serendipitous activities of design. Such interrogation highlights the feedback mechanisms that facilitate intrinsic feedback and fast, if not instantaneous, cycles of learning. The physical act of performance itself provides a learning experience that is not replicable in other modes of delivery. Student feedback data and independent assessment of project outcomes are used to assess the success of this studio model.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the “ideal” algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

A learning-based approach to reactive security

Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980]

The paper "the importance of convexity in learning with squared loss" gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.