Loneliness and classroom participation in adolescents with learning disabilities : a social cognitive view

Students with learning disabilities (LD) often experience significant feelings of loneliness. There is some evidence to suggest that these feelings of loneliness may be related to social difficulties that are linked to their learning disability. Adolescents experience more loneliness than any other age group, primarily because this is a time of identity formation and self-evaluation. Therefore, adolescents with learning disabilities are highly likely to experience the negative feelings of loneliness. Many areas of educational research have highlighted the impact of negative feelings on learning. This begs the question, =are adolescents with learning disabilities doubly disadvantaged in regard to their learning?‘ That is, if their learning experience is already problematic, does loneliness exacerbate these learning difficulties? This thesis reveals the findings of a doctoral project which examined this complicated relationship between loneliness and classroom participation using a social cognitive framework. In this multiple case-study design, narratives were constructed using classroom observations and interviews which were conducted with 4 adolescent students (2 girls and 2 boys, from years 9-12) who were identified as likely to be experiencing learning disabilities. Discussion is provided on the method used to identify students with learning disabilities and the related controversy of using disability labels. A key aspect of the design was that it allowed the students to relate their school experiences and have their stories told. The design included an ethnographic element in its focus on the interactions of the students within the school as a culture and elements of narrative inquiry were used, particularly in reporting the results. The narratives revealed all participants experienced problematic social networks. Further, an alarmingly high level of bullying was discovered. Participants reported that when they were feeling rejected or were missing a valued other they had little cognitive energy for learning and did not want to be in school. Absenteeism amongst the group was high, but this was also true for the rest of the school population. A number of relationships emerged from the narratives using social cognitive theory. These relationships highlighted the impact of cognitive, behavioural and environmental factors in the school experience of lonely students with learning disabilities. This approach reflects the social model of disability that frames the research.

What learners "know" through digital media production : learning by design

The power to influence others in ever-expanding social networks in the new knowledge economy is tied to capabilities with digital media production that require increased technological knowledge. This article draws on research in elementary classrooms to examine the repertoires of cross-disciplinary knowledge that literacy learners need to produce innovative digital media via the “social web”. The article builds on Learning by Design and the Knowledge Processes to describe “how” learning occurs, while presenting a model to theorise “what” students know – the Knowledge Assets – when learners produce digital and multimodal texts.

Personalising teacher professional development : strategies enabling effective learning for educators of 21st century students

In the Knowledge Society, new demands are placed on teachers as they strive to empower young people to be global citizens, ready for the 21st century. Systemic shifts need to be made, however, to build capacity across the workforce to practise new ways of teaching and learning, including the personalisation of teacher professional development. This article argues new strategies and approaches for effective adult learning, including an individualised focus, context-based learning and an empowerment of teachers to develop their own personal learning networks. This article concludes with an analysis of the challenges facing professional development leaders in moving towards personalised teacher learning.

A comparative study of marketing student engagement in online social networks and virtual worlds : Facebook versus second life

The study evaluated two student online contemporary learning environments; Second Life and Facebook, student learning experiences and student knowledge outcomes. A case study methodology was used to gain rich exploratory knowledge of student learning when integrating online social networks (OSN) and virtual worlds (VW) platforms. Findings indicated students must perceive relevance in the activities when using such platforms, even though online environments create an interesting learning space for students and educators, the novelty can diminish quickly and these online environments dilute traditional authority boundaries.

Paving for diverse pathways : using industry-linked learning to support Performing Arts students in their transition to protean careers

Statistics presented in Australia Council reports such as Don’t Give Up Your Day Job (2003), and Artswork: A Report On Australians Working in the Arts 1 and 2 (1997, 2005), and in other studies on destinations for Performing Arts graduates, demonstrate the diversity of post-graduation pathways for our students, the prevalence of protean careers, and the challenges in developing a sense of professional identity in a context where a portfolio of work across performance making, producing, administration and teaching can make it difficult for young artists to establish career status and capital in conventional terms (cf. Dawn Bennett, “Academy and the Real World: Developing Realistic Notions of Career in the Performing Arts”, Arts & Humanities in Higher Education, 8.3, 2009). In this panel, academics from around Australia will consider the ways in which Drama, Theatre and Performance Studies as a discipline is deploying a variety of practical, professional and work-integrated teaching and learning activities – including performance-making projects, industry projects, industry placements and student-initiated projects – to connect students with the networks, industries and professional pathways that will support their progression into their career. The panellists include Bree Hadley (Queensland University of Technology), Meredith Rogers (La Trobe University), Janys Hayes (Woolongong University) and Teresa Izzard (Curtin University). The panelists will present insights into the activities they have found successful, and address a range of questions, including: How do we introduce students to performance-making and / or producing models they will be able to employ in their future practice, particularly in light of the increasingly limited funds, time and resources available to support students’ participation in full-scale productions under the stewardship of professional artists?; How and when do we introduce students to industry networks?; How do we cater for graduates who will work as performers, writers, directors or administrators in the non-subsidised sector, the subsidised sector, community arts and education?; How do we category cater for graduates who will go on to pursue their work in a practice-as-research context in a Higher Degree?; How do we assist graduates in developing a professional identity? How do we assist graduates in developing physical, professional and personal resilience?; How do we retain our connections with graduates as part of their life-long learning?; Do practices and processes need to differ for city or regionally based / theoretically or practically based degree programs?; How do our teaching and learning activities align with emergent policy and industrial frameworks such as the shift to the “Producer Model” in Performing Arts funding, or the new mentorship, project, production and enterprise development opportunities under the Australia Council for the Arts’ new Opportunities for Young and Emerging Artists policy framework?

Creating global networks through an online engineering graduate program

Internationally the railway industry is facing a severe shortage of engineers with high level, relevant, profession and technical knowledge and abilities, in particular amongst engineers involved in the design, construction and maintenance of railway infrastructure. A unique graduate level program has been created to meet that global need via a fully online, distance education format. The development and operation of this Master of Engineering degree is proposed as a model of the process needed for the industry-relevance, flexible delivery, international networking, and professional development required for a successful graduate engineering program in the 21st century. In particular, the paper demonstrates how a mix of new and more familiar technologies are utilised through a variety of tasks to overcome the huge distances and multiple time zones that separate the participants across a growing number of countries, successfully achieving close and sustained interaction amongst the participants and railway experts.

Education for the creative economy : innovation, transdisciplinarity, and networks

The question posed in this chapter is: To what extent does current education theory and practice prepare graduates for the creative economy? We first define what we mean by the term creative economy, explain why we think it is a significant point of focus, derive its key features, describe the human capital requirements of these features, and then discuss whether current education theory and practice are producing these human capital requirements. The term creative economy can be critiqued as a shibboleth, but as a high level metaphor, it nevertheless has value in directing us away from certain sorts of economic activity and toward other kinds. Much economic activity is in no way creative. If I have a monopoly on some valued resource, I do not need to be creative. Other forms of economic activity are intensely creative. If I have no valued resources, I must create something that is valued. At its simplest and yet most profound, the idea of a creative economy suggests a capacity to compete based on engaging in a gainful activity that is different from everyone else’s, rather than pursuing the same endeavor more competitively than everyone else. The ability to differentiate on novelty is key to the concept of creative economy and key to our analysis of education for this economy. Therefore, we follow Potts and Cunningham (2008, p. 18) and Potts, Cunningham, Hartley, and Ormerod (2008) in their discussion of the economic significance of the creative industries and see the creative economy not as a sector but as a set of economic processes that act on the economy as a whole to invigorate innovation based growth. We see the creative economy as suffused with all industry rather than as a sector in its own right. These economic processes are essentially concerned with the production of new ideas that ultimately become new products, service, industry sectors, or, in some cases, process or product innovations in older sectors. Therefore, our starting point is that modern economies depend on innovation, and we see the core of innovation as new knowledge of some kind. We commence with some observations about innovation.

Bayesian filtering over compressed appearance states

This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots. The framework proposed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classification is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task.

Parents resourcing children’s early development and learning

Parents deal with a complex web of choices when seeking and using knowledge and resources related to their young children’s literacy development. Information about children’s learning and development comes in many forms and is produced by an increasingly diverse range of players including governments, nongovernment organisations and commercial businesses. This study used a survey, interview and artefact collection to investigate mothers’ and fathers’ reported activities in seeking, accessing, producing and circulating information and resources related to children’s learning and development. Differences were found relating to parent gender and level of education. Parents’ resourcing activities are also shaped by their particular goals for their children.

The role of digressive learning and uniqueness on performance : longitudinal evidence from young firms

The ability to differentiate from competitors through the selection of unique offerings is an important cornerstone of competitive performance. Developing unique products and services to offer in the marketplace is not only important for established firms, but also an important strategic choice for young firms (Baum and Haveman, 1997). Unlike large and established firms, young firms tend to have less access to adequate resources, well-developed sources of information, contact networks, and considerable experience and management know-how. That is, these firms differ significantly in their attributes and performance from larger and well-established firms (c.f. Miller and Chen, 1994). Although young firms are disadvantaged by the paucity of resources in putting together its unique product offering(s), they develop different pathways in advancing their assortment of capabilities that enables them to stay ahead of competitors.

Bayesian methodology for genetics of complex diseases

Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

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