Children with special health care needs : social and learning competence in the early years

The number of children with special health care needs surviving infancy and attending school has been increasing. Due to their health status, these children may be at risk of low social-emotional and learning competencies (e.g., Lightfoot, Mukherjee, & Sloper, 2000; Zehnder, Landolt, Prchal, & Vollrath, 2006). Early social problems have been linked to low levels of academic achievement (Ladd, 2005), inappropriate behaviours at school (Shiu, 2001) and strained teacher-child relationships (Blumberg, Carle, O‘Connor, Moore, & Lippmann, 2008). Early learning difficulties have been associated with mental health problems (Maughan, Rowe, Loeber, & Stouthamer-Loeber, 2003), increased behaviour issues (Arnold, 1997), delinquency (Loeber & Dishion, 1983) and later academic failure (Epstein, 2008). Considering the importance of these areas, the limited research on special health care needs in social-emotional and learning domains is a factor driving this research. The purpose of the current research is to investigate social-emotional and learning competence in the early years for Australian children who have special health care needs. The data which informed this thesis was from Growing up in Australia: The Longitudinal Study of Australian Children. This is a national, longitudinal study being conducted by the Commonwealth Department of Families, Housing, Community Services and Indigenous Affairs. The study has a national representative sample, with data collection occurring biennially, in 2004 (Wave 1), 2006 (Wave 2) and 2008 (Wave 3). Growing up in Australia uses a cross-sequential research design involving two cohorts, an Infant Cohort (0-1 at recruitment) and a Kindergarten Cohort (4-5 at recruitment). This study uses the Kindergarten Cohort, for which there were 4,983 children at recruitment. Three studies were conducted to address the objectives of this thesis. Study 1 used Wave 1 data to identify and describe Australian children with special health care needs. Children who identified as having special health care needs through the special health care needs screener were selected. From this, descriptive analyses were run. The results indicate that boys, children with low birth weight and children from families with low levels of maternal education are likely to be in the population of children with special health care needs. Further, these children are likely to be using prescription medications, have poor general health and are likely to have specific condition diagnoses. Study 2 used Wave 1 data to examine differences between children with special health care needs and their peers in social-emotional competence and learning competence prior to school. Children identified by the special health care needs screener were chosen for the case group (n = 650). A matched case control group of peers (n = 650), matched on sex, cultural and linguistic diversity, family socioeconomic position and age, were the comparison group. Social-emotional competence was measured through Social/Emotional Domain scores taken from the Growing up in Australia Outcome Index, with learning competence measured through Learning Domain scores. Results suggest statistically significant differences in scores between the two groups. Children with special health care needs have lower levels of social-emotional and learning competence prior to school compared to their peers. Study 3 used Wave 1 and Wave 2 data to examine the relationship between special health care needs at Wave 1 and social-emotional competence and learning competence at Wave 2, as children started school. The sample for this study consisted of children in the Kindergarten Cohort who had teacher data at Wave 2. Results from multiple regression models indicate that special health care needs prior to school (Wave 1) significantly predicts social-emotional competence and learning competence in the early years of school (Wave 2). These results indicate that having special health care needs prior to school is a risk factor for the social-emotional and learning domains in the early years of school. The results from these studies give valuable insight into Australian children with special health care needs and their social-emotional and learning competence in the early years. The Australia population of children with special health care needs were primarily male children, from families with low maternal education, were likely to be of poor health and taking prescription medications. It was found that children with special health care needs were likely to have lower social-emotional competence and learning competence prior to school compared to their peers. Results indicate that special health care needs prior to school were predictive of lower social-emotional and learning competencies in the early years of school. More research is required into this unique population and their competencies over time. However, the current research provides valuable insight into an under researched 'at risk' population.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Elicitator : a user-friendly, interactive tool to support scenario-based elicitation of expert knowledge

Expert elicitation is the process of determining what expert knowledge is relevant to support a quantitative analysis and then eliciting this information in a form that supports analysis or decision-making. The credibility of the overall analysis, therefore, relies on the credibility of the elicited knowledge. This, in turn, is determined by the rigor of the design and execution of the elicitation methodology, as well as by its clear communication to ensure transparency and repeatability. It is difficult to establish rigor when the elicitation methods are not documented, as often occurs in ecological research. In this chapter, we describe software that can be combined with a well-structured elicitation process to improve the rigor of expert elicitation and documentation of the results

The link between age, career goals, and adaptive development for work-related learning among local government employees

More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for learning at work in relation to demographics and career goals. It was found that mature age was associated with perceptions of preferential treatment of younger workers with respect to learning and development. Age was also correlated with several career goals. Findings suggest that younger workers’ learning and development options are better catered for in the workplace. Mature aged workers may compensate for unequal learning opportunities at work by studying for an educational qualification or seeking alternate job opportunities. The desire for a higher level job within the organization or educational qualification was linked to engagement in learning and development goals at work. It is suggested that an understanding of employee perceptions in the workplace in relation to goals and activities may be important in designing strategies to retain workers.

Building support for learning within a Doctor of Education programme

Professional doctorates were introduced in the 1990s for practitioners to research ‘real-world’ problems relevant to their respective workplace communities and contexts. An array of difficulties faces professional doctoral students as they transition from professionals to practitioner researchers. This study sought to understand the learning journey of a cohort of students at an Australian university and to assess whether the cohort approach provided the necessary support for students to reach their scholarly destinations. Throughout the first 18 months of the programme, focus group interviews and surveys were conducted to gauge students’ experiences and to evaluate developments for support within the programme. Utilising a socio-cultural perspective helped identify and explain the importance of shared practice in fostering learning, the development of academic and researcher identities, and the role of communities of practice. Challenges of managing time and overcoming the professional and academe divide were facilitated by the evolving developments of the programme.

The constraints-based approach to motor learning : Implications for a non-linear pedagogy in sport and physical education

Recently, a constraints- led approach has been promoted as a framework for understanding how children and adults acquire movement skills for sport and exercise (see Davids, Button & Bennett, 2008; Araújo et al., 2004). The aim of a constraints- led approach is to identify the nature of interacting constraints that influence skill acquisition in learners. In this chapter the main theoretical ideas behind a constraints- led approach are outlined to assist practical applications by sports practitioners and physical educators in a non- linear pedagogy (see Chow et al., 2006, 2007). To achieve this goal, this chapter examines implications for some of the typical challenges facing sport pedagogists and physical educators in the design of learning programmes.

The role of academic community in higher learning : Alternatives to a drive-thru education

The purpose of our paper is to illustrate the fundamental importance of developing academic community among first year students. We argue that a sense of academic community is of fundamental importance in combating the effects of the neo-liberal economic discourse on higher education, and that the values of higher education are incongruent with those of economic rationalism. The discursive commodification of the student, and of education itself, works against the formation of community, both within the university environment and in the wider society. We argue that, at present, the dominant discourse shaping the social practice of higher education is that of neo-liberal economics. Community values stand in opposition to the dominant discourse, and are integral to the long-term survival of a socially critical and socially responsive society. We conclude that the importance of establishing a sense of academic community during the first year of university is justified by its ultimate value to society.

Book Review: "Rethinking Learning in Early Childhood Education" written by Nicola Yelland, Libby Lee, Maureen O’Rourke & Cathie Harrison

There is a growing body of work that responds to the impact of the rapid uptake of information and communication technology (ICT) on education (Buckingham, 2003; Cheung, 2003; Cuban, 2003; Leung, 2003; Prensky, 2005; Green & Hannon, 2007; Brooks-Gunn & Donahue, 2008; Lyman et al, 2008). Mostly, this work has been positioned in the context of upper-primary or secondary classrooms. More recently, there has been a growing call for research about the impact of ICT on the early years or in early childhood contexts. This text initiates a response to that call. The authors concur that today’s children are a generation who create, learn, work, play and communicate very differently from their parents and teachers (Buckingham, 2003), and that classroom activity needs to reflect this difference.

Ezine and iRadio as knowledge creation metaphors for scaffolding learning in physical and virtual learning spaces

eZine and iRadio represent metaphors for multimedia communication on the Internet. Participating students experience a simulated Internet publishing environment in both their classroom and virtual learning environment. This chapter presents an autoethnographic account highlighting the voices of the learning designer and the teacher and provides evidence of the planning and implementation of two tertiary music elective courses over three iterations of each course. A blended learning environment was incorporated within each elective music course and a collaborative approach to development between lecturers, tutors, learning and technological designers using an iterative research design. The research suggests that learning design which provides real world examples and resources integrating authentic task design into their unit can provide meaningful and engaging experiences for students. The dialogue between learning designers and teachers and iterative review of the learning process and student outcomes, we believe, has engaged students meaningfully to achieve transferable learning outcomes.

Reframing the development of corporate learning

Due to increasing recognition by industry that partnerships with universities can lead to more effective knowledge and skills acquisition and deployment, corporate learning programmes are currently experiencing a resurgence of interest. Rethinking of corporations’ approaches to what has traditionally been classed as ‘training’ has resulted in a new focus on learning and the adoption of philosophies that underlie the academic paradigm. This paper reports on two studies of collaboration between major international engineering corporations and an Australian university, the aim of which was to up-skill the workforce in response to changing markets. The paper highlights the differences between the models of learning adopted in such collaboration and those in more conventional, university-based environments. The learning programmes combine the ADDIE (analysis, design, develop, implement and evaluate) development and workplace learning models. Adaptations that have added value for industry partners and recommendations as to how these can be evolved to cope with change are discussed. The learning is contextualised by industry- based subject matter experts working in close collaboration with university experts and learning designers to develop programmes that are reflective of current and future needs in the organisation. Results derived from user feedback indicate that the learning programmes are effectively aligned with the needs of the industry partners whilst simultaneously upholding academic ideals. In other words, it is possible to combine academic and more traditional approaches to develop corporate learning programmes that satisfy requirements in the workplace. Emerging from the study, a new conceptual framework for the development of corporate learning is presented.

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.

Building on the past for the future : assessment for learning as a field of exchange

Standardised testing does not recognise the creativity and skills of marginalised youth. This paper presents the development of an innovative approach to assessment designed for the re-engagement of at risk youth who have left formal schooling and are now in an alternative education institution. An electronic portfolio system (EPS) has been developed to capture, record and build on the broad range of students’ cultural and social capital. The assessment as a field of exchange model draws on categories from sociological fields of capital and reconceptualises an eportfolio and social networking hybrid system as a sociocultural zone of learning and development. The EPS, and assessment for learning more generally, are conceptualised as social fields for the exchange of capital (Bourdieu 1977, 1990). The research is underpinned by a sociocultural theoretical perspective that focuses on how students and teachers at the Flexible Learning Centre (FLC) develop and learn, within the zone of proximal development (Vygotsky, 1978). The EPS is seen to be highly effective in the engagement and social interaction between students, teachers and institutions. It is argued throughout this paper that the EPS provides a structurally identifiable space, an arena of social activity, or a field of exchange. The students, teachers and the FLC within this field are producing cultural capital exchanges. The term efield (exchange field) has been coined to refer to this constructed abstract space. Initial results from the trial show a general tendency towards engagement with the EPS and potential for the attainment of socially valued cultural capital in the form of school credentials.

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.