Stars are made of glass : children as capable and creative communicators

This booklet is the third in the Research in Practice Series, designed to complement Belonging, being & becoming: The Early Years Learning Framework for Australia (DEEWR, 2009). It focuses on Learning Outcome 5 of the Early Years Learning Framework (EYLF): Children are effective communicators (DEEWR, 2009).

Australian ePortfolio Project–Stage 2 ePortfolio use by university students in Australia : Developing a sustainable community of practice Case studies extracted from the final project report December 2009

Fourteen sase studies extracted from the final project report - December 2009 Australian Flexible Learning Framework: E-portfolios Community of Practice (Aus) Personal learning plans and ePortfolio (Aus) RMIT University: Introducing ePortfolios (Aus) ePortfolio Practice: ALTC Exchange (Aus) Australian PebblePad User Group (APpUG) (Aus) ePortfolios in the library and information services sector (Aus) PDP and ePortfolios UK (UK) SURF NL Portfolio (Netherlands) University of Canterbury ePortfolio (NZ) AAEEBL: Association for Authentic, Experiential and Evidence-Based Learning (USA) Midlands Eportfolio Group, West Midlands(UK) EPAC: Electronic Portfolio Action and Communication (USA) Scottish Higher Education PDP Forum (UK) Centre for Recording Achievement (CRA)(UK)

Evidence based practice

An integral part of teaching and a principle underpinning professional practice in the early years is the importance of reflecting on and researching our own practice. For example, in Australia, the Early Years Learning Framework: Belonging, Being and Becoming identifies “ongoing learning and reflective practice” (DEEWR, 2009, p. 13) as one of the five principles distilled from theories and research evidence that underpin professional practice in the early years. Recognising teaching as encompassing the role of researching pedagogical practice highlights that teaching is not simply practical or procedural but requires intellectual work. This chapter details evidence based practice (EBP) in early years education and highlights four questions: 1. What is evidence based practice?; 2. What evidence do I draw on?; 3. How might I discern relevant evidence?; and 4. What is my part in generating research evidence?

Still valuing play

Play has had a prominent position in early childhood education and care (ECEC) for over 200 years. As educators, we tend to talk about young children learning through play as a matter of fact. In our first national Early Years Learning Framework (Department of Education, Employment and Workplace Relations, 2009), play is promoted as the right of all children, an integral part of being a child and as the prime context for learning in the early years. While the Early Years Learning Framework (EYLF) defines its use of the term ‘play’, there are differing perspectives on what constitutes play, the relationship between play and learning,and the educator’s role in play. In this context, it might be interesting to go a little deeper, and to look at some different perspectives on play and learning.

Get real : the development, implementation and evaluation of an intervention for primary aged children with High Functioning Autism

Children with Autism Spectrum Disorder experience difficulty in communication and in understanding the social world which can have negative consequences for their relationships, in managing emotions, and generally dealing with the challenges of everyday life. This thesis examines the effectiveness of the Active and Reflective components of the Get REAL program through the assessment of the detailed coding of video-recorded observations and longitudinal quantitative analysis. The aim of Get REAL is to increase the social, emotional, and cognitive learning of children with High Functioning Autism (HFA). Get REAL is a group program designed specifically for use in inclusive primary school settings. The Get REAL program was designed in response to the mixed success of generalisation of learning to new contexts of existing social skills programs. The theoretical foundation of Get REAL is based upon pedagogical theory and learning theory to facilitate transfer of learning, combined with experiential, individualised, evaluative and organisational approaches. This thesis is by publication and consists of four refereed journal papers; 1 accepted for publication and 3 that are under review. Paper 1 describes the development and theoretical basis of the Get REAL program and provides detail of the program structure and learning cycle. The focus of Paper 1 reflects the first question of interest in the thesis which is about the extent to which learning derived from participation in the program can be generalised to other contexts. Participants are 16 children with HFA ranging in age from 8-13 years. Results provided support for the generalisability of learning from Get REAL to home and school evidenced by parent and teacher data collected pre and post participation in Get REAL. Following establishment of the generalisation of learning from Get REAL, Papers 2 and 3 focus on the Active and Reflective components of the program in order to examine how individual and group learning takes place. Participants (N = 12) in the program are video-taped during the Active and Reflective Sessions. Using identical coding protocols of video data, improvements in prosocial behaviour and diminishing of inappropriate behaviours were apparent with the exception of perspective taking. Data also revealed that 2 of the participants had atypical trajectories. An in-depth case study analysis was then conducted with these 2 participants in Paper 4. Data included reports from health care and education professionals within the school and externally (e.g., paediatrician) and identified the multi-faceted nature of care needed for children with comorbid diagnoses and extremely challenging family circumstances as a complex task to effect change. Results of this research support the effectiveness of the Get REAL program in promoting pro social behaviours such as improvements in engaging with others and emotional regulation, and in diminishing unwanted behaviours such as conduct problems. Further, the gains made by the participating children were found to be generalisable beyond Get REAL to home and other school settings. The research contained in the thesis adds to current knowledge about how learning can take place for children with HFA. Results show that an experiential learning framework with a focus on social cognition, together with explicit teaching, scaffolded with video feedback, are key ingredients for the generalisation of social learning to broader contexts.

Transformative approaches to sustainability

How to live sustainably is a topic of local, national and international importance. The Australian National Curriculum (ACARA, 2011) identifies sustainability as a cross-disciplinary strand, obligating teachers to build sustainability into their pedagogical practices. In early childhood education, the Early Years Learning Framework (2009) and more recently, the National Quality Framework (2011) provide impetus for early childhood education for sustainably (ECEfS). This article discusses ECEfS, but first, it addresses climate change putting this into a sustainability perspective.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Critical reflection

Critical reflection is a necessary component of professionalism in early childhood education. Evidence of critical reflection within a service draws attention to the intellectual work of early childhood educators and highlights professional capacities beyond the care of young children.

Early years national curriculum documents : revisiting inclusive education

Early years education encompasses early childhood education and care (ECEC) and the early years of school across the age range birth to eight years. The introduction of two national curriculum documents for early years education – the Early Years Learning Framework (Department of Education, Employment and Workplace Relations DEEWR, 2009) for ECEC programs and the Australian Curriculum (Australian Curriculum, Assessment and Reporting Authority ACARA, 2011a) – indicates a trend towards national coherence, yet highlights a gap between notions of inclusion in the ECEC and school sectors of early years education. These gaps have the potential to impact negatively on school transition experiences through reductions in continuity of pedagogy and partnerships with families. Australian definitions of inclusion have moved beyond integration (i.e., mainstream classroom placement with support services and accommodations to address disability or lack of English), to encompass curricular and pedagogic differentiation catering for the participation rights and sense of belonging of children with a diverse range of abilities and backgrounds. This paper considers improved curriculum alignment and pedagogic continuity through enactment of elements relevant to inclusion.

Contemplating Whitehead's freedom and discipline

This paper extends the previous application of Alfred Whitehead's educational ideas to the domain of enterprise education. In doing so, a unique approach to enterprise education is illustrated that links students to their reality whilst also connecting the curriculum to contemporary entrepreneurship theory. The paper reports upon past cycles of reflective practice related to the developing hic et nunc teaching and learning framework. Two specific findings of note have emerged. First, that student' learning outcomes are enhanced through the oscillating influence of freedom and discipline. However, in the absence of either factor, sub-optimal outcomes are seen to occur. That is, an imbalance between freedom and discipline has resulted in sub-optimal outcomes from either a lack of student interest or an inability to adequately apply acquired knowledge. Where gains have been made, the most obvious process has been through consultation with students. Second, that the students also play an important role in shaping the nature of the learning environments within which they interact. Both findings are of significant importance to all academics charged with the responsibility of developing enterprise education curriculum. The main implication of the paper is that in the absence of sound pedagogical practises, it is possible that enterprise programs may develop a tendency to reinforce past practises. The processes of constructive alignment and criterion-based assessment are argued to offer avenues through which students can influence the educational process. They also provide the educator with a reflective pathway through which continual improvements are constantly possible. This paper provides other academics with a window through which to view the ongoing development of a process that has been recognised nationally for teaching excellence and influenced many fine young entrepreneurs. The paper also draws attention to a set of core educational philosophies that have transferable value to any academic setting. It is noted that the task of developing a learner-centred curriculum for enterprise education has been an entrepreneurial endeavour in itself. Many mistakes have been made and many memorable achievements have been celebrated.

Staged development of Robot Motor Coordination

M.H. Lee and Q. Meng, 'Staged development of Robot Motor Coordination', IEEE International Conference on Systems, Man and Cybernetics, (IEEE SMC 05), Hawaii, USA, v3, 2917-2922, 2005.

3D Hand Pose Reconstruction Using Specialized Mappings

A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA's fundamental components are a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which in our case are examples of hand joint configurations and their corresponding visual features. The joint angle data in the training set is obtained via a CyberGlove, a glove with 22 sensors that monitor the angular motions of the palm and fingers. In training, the visual features are generated using a computer graphics module that renders the hand from arbitrary viewpoints given the 22 joint angles. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both hands of the user, calculates the appropriate features, and estimates the 3D hand joint angles from those features. Results are encouraging given the complexity of the task.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Modèle informatique du coapprentissage des ganglions de la base et du cortex : l'apprentissage par renforcement et le développement de représentations

Tout au long de la vie, le cerveau développe des représentations de son environnement permettant à l’individu d’en tirer meilleur profit. Comment ces représentations se développent-elles pendant la quête de récompenses demeure un mystère. Il est raisonnable de penser que le cortex est le siège de ces représentations et que les ganglions de la base jouent un rôle important dans la maximisation des récompenses. En particulier, les neurones dopaminergiques semblent coder un signal d’erreur de prédiction de récompense. Cette thèse étudie le problème en construisant, à l’aide de l’apprentissage machine, un modèle informatique intégrant de nombreuses évidences neurologiques. Après une introduction au cadre mathématique et à quelques algorithmes de l’apprentissage machine, un survol de l’apprentissage en psychologie et en neuroscience et une revue des modèles de l’apprentissage dans les ganglions de la base, la thèse comporte trois articles. Le premier montre qu’il est possible d’apprendre à maximiser ses récompenses tout en développant de meilleures représentations des entrées. Le second article porte sur l'important problème toujours non résolu de la représentation du temps. Il démontre qu’une représentation du temps peut être acquise automatiquement dans un réseau de neurones artificiels faisant office de mémoire de travail. La représentation développée par le modèle ressemble beaucoup à l’activité de neurones corticaux dans des tâches similaires. De plus, le modèle montre que l’utilisation du signal d’erreur de récompense peut accélérer la construction de ces représentations temporelles. Finalement, il montre qu’une telle représentation acquise automatiquement dans le cortex peut fournir l’information nécessaire aux ganglions de la base pour expliquer le signal dopaminergique. Enfin, le troisième article évalue le pouvoir explicatif et prédictif du modèle sur différentes situations comme la présence ou l’absence d’un stimulus (conditionnement classique ou de trace) pendant l’attente de la récompense. En plus de faire des prédictions très intéressantes en lien avec la littérature sur les intervalles de temps, l’article révèle certaines lacunes du modèle qui devront être améliorées. Bref, cette thèse étend les modèles actuels de l’apprentissage des ganglions de la base et du système dopaminergique au développement concurrent de représentations temporelles dans le cortex et aux interactions de ces deux structures.

An Electronic Market-Maker

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from real-time market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building market-making algorithms.