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.

Artful inquiry : an arts-based facilitation approach for individual and organisational learning and development

The research undertaken in these two major doctoral studies investigates the field of artsbased learning, a pedagogical approach to individual and organisational learning and development, my professional creative facilitation practice and development as a researcher. While the studies are stand-alone projects they are intended to build on each other in order to tell the evolving story of my research and professional practice. The first study combines The Role of Arts-based Learning in a Creative Economy; The Need for Artistry in Professional Education the art of knowing what to do when you don’t know what to do and Lines of Inquiry: Making Sense of Research and Professional Practice. The Role of Arts-based Learning in a Creative Economy provides an overview of the field of arts-based learning in business. The study focuses on the relevant literature and interviews with people working in the field. The paper argues that arts-based learning is a valuable addition to organisations for building a culture of creativity and innovation. The Need for Artistry in Professional Education continues that investigation. It explores the way artists approach their work and considers what skills and capabilities from artistic practice can be applied to other professions’ practices. From this research the Sphere of Professional Artistry model is developed and depicts the process of moving toward professional artistry. Lines of Inquiry: making sense of research and professional practice through artful inquiry is a self-reflective study. It explores my method of inquiry as a researcher and as a creative facilitation practitioner using arts-based learning processes to facilitate groups of people for learning, development and change. It discusses how my research and professional practice influence and inspire the other and draws on cased studies. The second major research study Artful Inquiry: Arts-based Learning for Inquiry, Reflection and Action in Professional Practice is a one year practice-led inquiry. It continues the research into arts-based and aesthetic learning experiences and my arts-based facilitation practice. The research is conducted with members of a Women’s Network in a large government service agency. It develops the concept of ‘Artful Inquiry’’ a creative, holistic, and embodied approach for facilitation, inquiry, learning, reflection, and action. Storytelling as Inquiry is used as a methodology for understanding participants’ experiences of being involved in arts-based learning experiences. The study reveals the complex and emergent nature of practice and research. It demonstrates what it can mean to do practice-led research with others, within an organisational context, and to what effect.

Learning and teaching from the edge to centre stage : Critical factors in embedding sustainable university-wide engagement in external awards and grants funding initiatives.

Teaching awards, grants and fellowships are strategies used to recognise outstanding contributions to learning and teaching, encourage innovation, and to shift learning and teaching from the edge to centre stage. Examples range from school, faculty and institutional award and grant schemes to national schemes such as those offered by the Australian Learning and Teaching Council (ALTC), the Carnegie Foundation for the Advancement of Teaching in the United States, and the Fund for the Development of Teaching and Learning in higher education in the United Kingdom. The Queensland University of Technology (QUT) has experienced outstanding success in all areas of the ALTC funding since the inception of the Carrick Institute for Learning and Teaching in 2004. This paper reports on a study of the critical factors that have enabled sustainable and resilient institutional engagement with ALTC programs. As a lens for examining the QUT environment and practices, the study draws upon the five conditions of the framework for effective dissemination of innovation developed by Southwell, Gannaway, Orrell, Chalmers and Abraham (2005, 2010): 1. Effective, multi-level leadership and management 2. Climate of readiness for change 3. Availability of resources 4. Comprehensive systems in institutions and funding bodies 5. Funding design The discussion on the critical factors and practical and strategic lessons learnt for successful university-wide engagement offer insights for university leaders and staff who are responsible for learning and teaching award, grant and associated internal and external funding schemes.

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.

Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.