Keeping all students on the learning path

A powerful notion to guide thinking about whole-class mathematics teaching is Vygotsky’s zone of proximal development (ZPD). Our research with primary and secondary teachers over the last six years has identified roles of teachers in relation to the ZPD, and ways of overcoming some typical barriers to students’ movement through their zones. Methods have included focus groups of experts, video analysis of classroom interactions, classroom observation, and analysis of lesson plans and teachers’ reflections teaching processes their outcomes. The research has involved the gradual development, trailing, evaluation, and adjustment of a six-component model for planning and teaching mathematics. The focus of this paper is on the use of one of its components, “differentiated learning trajectories”.

Medical error and decision making : learning from the past and present in intensive care

Background : Human error occurs in every occupation. Medical errors may result in a near miss or an actual injury to a patient that has nothing to do with the underlying medical condition. Intensive care has one of the highest incidences of medical error and patient injury in any specialty medical area; thought to be related to the rapidly changing patient status and complex diagnoses and treatments.

Purpose : The aims of this paper are to: (1) outline the definition, classifications and aetiology of medical error; (2) summarise key findings from the literature with a specific focus on errors arising from intensive care areas; and (3) conclude with an outline of approaches for analysing clinical information to determine adverse events and inform practice change in intensive care.

Data source : Database searches of articles and textbooks using keywords: medical error, patient safety, decision making and intensive care. Sociology and psychology literature cited therein.

Findings : Critically ill patients require numerous medications, multiple infusions and procedures. Although medical errors are often detected by clinicians at the bedside, organisational processes and systems may contribute to the problem. A systems approach is thought to provide greater insight into the contributory factors and potential solutions to avoid preventable adverse events.

Conclusion : It is recommended that a variety of clinical information and research techniques are used as a priority to prevent hospital acquired injuries and address patient safety concerns in intensive care.

Class size and its effect on learning in business education

This thesis explores the multi-dimensional nature of class size and how educational processes are shaped by different class size environments in four undergraduate business courses. Class size significantly influenced how curricula were structured and designed for learning and consequently students' course expectations and learning experiences in large and small classes.

The outcomes, processes, and challenges of inclusive education in Victoria

A collaborative school culture is important to improve the learning of students with special education needs. This could be met through a consolidation of a school's understanding of students' learning difficulties as environmental causes and an increase of teachers' confidence and knowledge on addressing students' diverse needs.

Enriching student experience and inter-professional learning of inclusive design with Second Life

This position paper reflects rapid advances in immersive 2D and 3D eLearning technologies and the expanding pool of ideas and applications in higher education across two professions. Inspiration has been drawn from examples in design learning, and various multidisciplinary collaborative projects through developmental research in Multi-User Virtual Environments (MUVEs). Linden Lab's Second Life (SL) is the most mature and popular of the ‘persistent’ virtual worlds. The study described in this paper aims to increase the authenticity of student learning through a range of SL simulated ‘life experiences’ relating to accessibility and mobility in the built environment. Significantly, the successes of such initiatives lie in several elements: teaching champions with vision and courage; detailed scripting of precise role-play encounters for first-time users to provide supportive ‘blended learning’ contexts; careful and vigilant strategic management of facilities and resources, and a robust design program. This paper focuses on the crucial alignment of these elements to the specific challenges of designing and navigating conception and development processes, to enable the execution and delivery of a tightly defined script for meaningful and memorable learning outcomes. This innovative pedagogical approach lacks time-tested outcomes, but is recognised equally as opportunity and challenge; risk and reward.

Continuous product innovation : learning behaviours and knowledge management

The research reported in this paper considers Product Innovation from a broader perspective than that of the isolated NPD (New Product Development) project commonly discussed in the literature. In this perspective, Product Innovation is a continuous and cross-functional process involving the sharing and transfer of knowledge within the many steps of the innovation process, and the integration of a growing number of different competencies inside and outside the organisational boundaries. This paper examines two in-depth case studies that were carried out to establish if and how learning occurred within companies developing new products. Based on a model developed as part of a joint Euro-Australian research project, the way in which the selected companies share and transfer knowledge and learning experiences during their product innovation processes have been examined and analysed. This model uses a number of interrelated variables including performance, behaviours and levers to stimulate improvement, contingencies, and learning/innovation capabilities to describe the learning and knowledge transfer in product innovation processes within the case studies. This paper discusses some of the skills the research has identified that managers need to enable their companies to gain a competitive advantage through improved product innovation. The ongoing research has developed, tested and disseminated a computer-based methodology to assess organisational knowledge capture and transfer in the new product development process. The research is part of the Euro-Australian co-operation project known as CIMA (Continuous Improvement and Product Innovation Management).

Complexity and learning behaviours in product innovation

Successful product innovation and the ability of companies to continuously improve their innovation processes are rapidly becoming essential requirements for competitive advantage and long-term growth in both manufacturing and service industries. It is now recognized that companies must develop innovation capabilities across all stages of the product development, manufacture, and distribution cycle. These Continuous Product Innovation (CPI) capabilities are closely associated with a company’s knowledge management systems and processes. Companies must develop mechanisms to continuously improve these capabilities over time.  Using results of an international survey on CPI practices, sets of companies are identified by similarities in specific contingencies related to their complexity of product, process, technological, and customer interface. Differences between the learning behaviors found present in the company groups and in the levers used to develop and support these behaviors are identified and discussed. This paper also discusses appropriate mechanisms for firms with similar complexities, and some approaches they can use to improve their organizational learning and product innovation.

Under what conditions? Including students with learning disabilities within Australian classrooms

In Australian schools, "inclusion" is a term that is used to challenge a previously narrow focus on students with disabilities and their integration within and distribution amongst "mainstream" schools and classrooms. Nevertheless, this article argues that, as a concept, "inclusion" requires further broadening and deepening, particularly in arenas of practice, if it is to serve the interests of all students. Informed by notions of recognitive justice, the paper advocates rethinking inclusion to accommodate student differences in more socially just ways - emphasising students' contributions rather than their disabilities - and what this means for the organisation of classrooms and schools. Within the article, research data are focused primarily on students with learning disabilities and draw on twenty semi-structured interviews conducted with parents and teachers across six Australian state primary and secondary schools. Three sets of conditions are proposed as necessary for inclusive classroom and school processes: specifically, those that promote self-identity and respect, self expression and development and selfdetermination and decision-making.

Environmental sustainability change management in SMEs : learning from sustainability champions

This study identifies the change management processes involved in undertaking environmental sustainability (ES) initiatives within Small and Medium Size Enterprises (SMEs) and relate these to the main attributes of learning organisations. Using case study techniques, the study draws from the change management experiences of a sample of 12 ES champions in different industries. The findings suggest that the ES champions experience four distinct change management stages in undertaking ES initiatives; namely the design, internalise, implement and evaluate stages. Each stage is also found to relate strongly with a number of key characteristics of learning organisations. Overall, the findings suggest that SMEs with strong learning organisation attributes are more likely to be successful in implementing and managing ES change initiatives. The implications of the findings are also discussed.

Learning through constructing representations in science : a framework of representational construction affordances

Compared with research on the role of student engagement with expert representations in learning science, investigation of the use and theoretical justification of student-generated representations to learn science is less common. In this paper, we present a framework that aims to integrate three perspectives to explain how and why representational construction supports learning in science. The first or semiotic perspective focuses on student use of particular features of symbolic and material tools to make meanings in science. The second or epistemic perspective focuses on how this representational construction relates to the broader picture of knowledge-building practices of inquiry in this disciplinary field, and the third or epistemological perspective focuses on how and what students can know through engaging in the challenge of representing causal accounts through these semiotic tools. We argue that each perspective entails productive constraints on students’ meaning-making as they construct and interpret their own representations. Our framework seeks to take into account the interplay of diverse cultural and cognitive resources students use in these meaning-making processes. We outline the basis for this framework before illustrating its explanatory value through a sequence of lessons on the topic of evaporation.

Understanding and developing structure - its importance for mathematics learning

This issue comes at a time when mathematics education research is becoming more intently focused on the development of "structure" as salient to generalised mathematics learning. Not surprisingly the attention on structure creates particular synergies with the increasingly rich field of research on algebraic thinking and arithmetic processes, particularly in the early years. In many ways, this special issue is concerned with describing the process of "structuring" that enables abstraction and generalisation. A recent MERJ special issue, Abstraction in Mathematics Education (Mitchelmore & White, 2007), illustrated theories of abstraction aligning these to notions of underlying structure. The importance of structure in the transition from school to university was also highlighted by Godfrey and Thomas (2008), and Novotna and Hoch (2008) in the previous special issue of MERJ (Thomas, 2008). In this special issue we present six papers that provide evidence of developing structure as critical for all learners of mathematics throughout schooling.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.