Teaching in play-based contexts

For some lay observers, play is mistakenly viewed as a leisure and uncomplicated activity done by young children. Lay observers may also see early childhood play-based settings as lacking academic opportunities for young children and may regrettably view the role of teachers in early childhood play-based contexts as simply custodial managers of chaos. However, a play-based context sets a stage for meaningful exchanges of thought that can beneficially challenge children's understandings and be particularly suitable for transforming children's everyday understandings to scientific knowledge.

While there have been shifts in theoretical approaches and differing texts on how one may view young children engaging in the human venture of science, early childhood play-based environments are settings for scientific inquiry, which can engender even the youngest of children to be critical thinkers, problem solvers, and reflectors of reason.

Adult Learning in the Transformational Environments of the Digital Revolution: Connecting Theory and Practice

The purpose of this study was to investigate the learning preferences and the post-secondary educational experiences of a group of Net-Gen adult learners, aged between 18 and 35, currently working in the knowledge economy workplace, and their assessment of how adequately they were prepared to meet the requirements of the knowledge economy workplace. This study utilized an explanatory mixed-method research design. Participants completed a questionnaire providing information on their self-reported learning style preferences, their use of digital tools for formal and informal learning, their use of digital technologies in postsecondary educational experiences, and their use of digital technologies in their workplace. Four volunteers from the questionnaire respondents were selected to participate in interviews based on the diversity of their experiences in higher education, including digital environments, and the diversity of their knowledge economy workplaces. Data collected from the questionnaire were analyzed for descriptive and demographic statistics, and categorized so that common patterns could be identified from information gathered from the online questionnaire and interviews. Findings based on this study indicated that these Net-Gen adult learners were fluent with all types of digital technologies in collaborative environments, expecting their educational experiences to provide a similar experience. Participants clearly expressed an understanding that digital/collaborative aptitudes are essential to successful employment in the knowledge economy workplace. The findings of this study indicated that the majority of participants felt that their post-secondary educational experiences did not adequately prepare them to meet the expectations of this type of working environment.

Employers' perspectives on work-integrated learning in project-based workplaces

The property and construction industry is uniquely impacted by project-based work environments; this creates special challenges for collaborative education. This research is based on investigating the attitudes of employer’s towards the use of formally assessed internships. The study comprised two stages; firstly a series of pilot interviews were undertaken with employers to test a number known issues. Secondly, the results from the interviews were used to refine a set of questions that were put to a large focus group of employers who were invited from across the property and construction sector. The results showed that many organisations expressed considerable goodwill towards collaborative education with universities. However, the challenges caused by project-based work environments restricted their ability to provide comprehensive learning opportunities. This research focuses on the distinctive issues associated with work-integrated learning in the property and construction industry

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

Generalizing on Multiple Grounds: Performance Learning in Model-Based Technology

This thesis explores ways to augment a model-based diagnostic program with a learning component, so that it speeds up as it solves problems. Several learning components are proposed, each exploiting a different kind of similarity between diagnostic examples. Through analysis and experiments, we explore the effect each learning component has on the performance of a model-based diagnostic program. We also analyze more abstractly the performance effects of Explanation-Based Generalization, a technology that is used in several of the proposed learning components.

What is the learning in games-based learning?

This chapter explores the nature of “learning” in games-based learning and the cognitive and motivational processes that might underpin that learning by drawing on psychological theories and perspectives. Firstly, changing conceptions of learning over the last few decades are reviewed. This is described in relation to the changes in formal learning theories and connections made between learning theory and GBL. Secondly, the chapter reviews empirical research on the learning outcomes that have been identified for GBL, with specific focus on cognitive benefits, school attainment, collaborative working, and the motivational and engaging appeal of games. Finally, an overview of the dominant theoretical perspectives/findings mostly associated with GBL is presented in an attempt to broaden understanding of the potential for GBL in the classroom.

Designing and Programming Organizational Infrastructures for Agents situated in Artifact-based Environments

Actual trends in software development are pushing the need to face a multiplicity of diverse activities and interaction styles characterizing complex and distributed application domains, in such a way that the resulting dynamics exhibits some grade of order, i.e. in terms of evolution of the system and desired equilibrium. Autonomous agents and Multiagent Systems are argued in literature as one of the most immediate approaches for describing such a kind of challenges. Actually, agent research seems to converge towards the definition of renewed abstraction tools aimed at better capturing the new demands of open systems. Besides agents, which are assumed as autonomous entities purposing a series of design objectives, Multiagent Systems account new notions as first-class entities, aimed, above all, at modeling institutional/organizational entities, placed for normative regulation, interaction and teamwork management, as well as environmental entities, placed as resources to further support and regulate agent work. The starting point of this thesis is recognizing that both organizations and environments can be rooted in a unifying perspective. Whereas recent research in agent systems seems to account a set of diverse approaches to specifically face with at least one aspect within the above mentioned, this work aims at proposing a unifying approach where both agents and their organizations can be straightforwardly situated in properly designed working environments. In this line, this work pursues reconciliation of environments with sociality, social interaction with environment based interaction, environmental resources with organizational functionalities with the aim to smoothly integrate the various aspects of complex and situated organizations in a coherent programming approach. Rooted in Agents and Artifacts (A&A) meta-model, which has been recently introduced both in the context of agent oriented software engineering and programming, the thesis promotes the notion of Embodied Organizations, characterized by computational infrastructures attaining a seamless integration between agents, organizations and environmental entities.

Usability and learning in online environments : a case of interactive encounters

This paper proposes a set of strategies to maximise learner-content and learner-learner interactions in on-line learning environments. Extrapolating the outcomes of a research study that investigated the ways in which users responded to the interactive constructs embedded within interactive multimedia applications, the concept of encounter theory is introduced. Using observation and interview techniques, participants in the study identified a range of options by which learnercomputer interactions might be enhanced. The implications of these findings for online and desktop environments are considered, specifically in terms of the independent learner's encounter with content material and other learners. Developing a comprehensive understanding of the interactive phenomenon will not only lead to more effective useability and learning in on-line environments, but also to their working better for the learner.

‘They get fed up with playing.’ Parents’ views on play-based learning in the Preparatory Year

Within early childhood education two ideas are firmly held: play is the best way for children to learn and parents are partners in the child’s learning. While these ideas have been explored, limited research to date has investigated the confluence of the two - how parents of young children view the concept of play. This paper investigates parents’ views on play by analysing the views of small group of parents of Prep Year children in Queensland, Australia. The parents in this study held varying definitions of what constitutes play, and complex and contradictory notions of its value. Positive views of play were linked to learning without knowing it, engaging in hands-on activities, and preparation for Year One through a strong focus on academic progress. Some parents held that Prep was play-based, while others did not. The complexities and diversity of parental opinion in this study echo the ongoing commentary about how play ought to be defined. Moreover, the notion that adults may interpret play in different ways is also reflected here. The authors suggest that for early childhood educators these complexities require an ongoing engagement, debate and reconceptualisation of the place of play in light of broader curricular and socio-political agendas.

The ‘state of play’ in Australia: Early childhood educators and play-based learning

This article provides an overview of the Education Meets Play study that will investigate early childhood educators’ use of play-based learning, now mandatory under the National Quality Standard. By building on what can be gleaned about educators’ approaches to play-based learning prior to the implementation of the Early Years Learning Framework, the study will contribute to the evidence base concerning the implementation and effects of Australia’s early childhood education and care policy reform initiatives.

Performance Modeling based on Multidimensional Surface Learning for Performance Predictions of Parallel Applications in Non-Dedicated Environments

Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active area of research for several years. The existing curve fitting strategies for performance modeling utilize data from experiments that are conducted under uniform loading conditions. Hence the accuracy of these models degrade when the load conditions on the machines and network change. In this paper, we analyze a curve fitting model that attempts to predict execution times for any load conditions that may exist on the systems during application execution. Based on the experiments conducted with the model for a parallel eigenvalue problem, we propose a multi-dimensional curve-fitting model based on rational polynomials for performance predictions of parallel applications in non-dedicated environments. We used the rational polynomial based model to predict execution times for 2 other parallel applications on systems with large load dynamics. In all the cases, the model gave good predictions of execution times with average percentage prediction errors of less than 20%