Probabilistic world modeling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that the action selection mechanism of a member in a robot team can select an effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probabilistic view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried out to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.

Personal epistemology in pre-service teachers : belief changes throughout a teacher education course

Classrooms of the 21st century are complex systems. They support diverse learners from varied contexts and function in a “messy” bricolage of policy contexts. This complexity is also evident in the nature of teaching and learning deployed in these classrooms. There is also, in current contexts, a general expectation that teachers will support students to construct, rather than simply receive knowledge. This process of constructing knowledge requires a focus on critical thinking in complex social and real world contexts (see also Elen & Clarebout, 2001; Yang, Chang & Hsu 2008). Critical thinking, which involves the identification and evaluation of multiple perspectives when making decisions, is a process of knowing – a tool of wisdom (Kuhn & Udell, 2001). Schommer-Aikens, Bird and Bakken (2010) refer to classrooms that encourage critical thinking as “epistemologically based” in which “the teacher encourages his/her students to look for connections among concepts within the text, with their prior knowledge, and with concepts found in the world beyond themselves” (p. 48).

An exploratory study of online social networking within a Doctorate of Education program

The professional doctorate is a degree that is specifically designed for professionals investigating real-world problems and relevant issues for a profession, industry and/or the community. The exploratory study on which this paper is based sought to track the scholarly skill development of a cohort of professional doctoral students who commenced their course in January 2008 at an Australian university. Via an initial survey and two focus groups held six months apart, the study aimed to determine if there had been any qualitative shifts in students’ understandings, expectations and perceptions regarding their developing knowledge and skills. Three key findings that emerged from this study were: (i) the appropriateness of using a blended learning approach in this professional doctoral program; (ii) the challenges of using wikis as an online technology for creating communities of practice; and (iii) the transition from professional to scholar is a process that requires the guided support inherent in the design of this particular doctorate of education program.

Community engagement and artful academic staff development : a return on expectations through collaborative practice

The changing ownership of roles in organisational work-life leads this paper to examine what universities are doing in their academic development practice through research at an Australian university where ‘artful’ collaboration with the real world aims to build capability for innovative academic community engagement. The paper also presents findings on the ‘return on expectations’ (Hodges, 2004) of community engagement for both academics and their organisational supervisors.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.

Innovative use of Blackboard® to assess laboratory skills

A novel application of the popular web instruction architecture Blackboard Academic Suite® is described. The method was applied to a large number of students to assess quantitatively the accuracy of each student’s laboratory skills. The method provided immediate feedback to students on their personal skill level, replaced labour-intensive scrutiny of laboratory skills by teaching staff and identified immediately those students requiring further individual assistance in mastering the skill under evaluation. The method can be used for both formative and summative assessment. When used formatively, the assessment can be repeated by the student without penalty until the skill is mastered. When used for summative assessment, the method can save the teacher much time and effort in assessing laboratory skills of vital importance to students in the real world.

Hybrid robot control and SLAM for persistent navigation and mapping

For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe the work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context.

Enabling 21st century learning spaces : practical interpretations of the MCEETYA Learning Spaces Framework at Bounty Boulevard State School, Queensland, Australia

While the Queensland and Australian Governments have recognised the importance of new spaces for teaching and learning, particularly with the Rudd Government's Building the Education Revolution, the practical implementation of new spaces is largely left to schools and even individual teachers. This article proposes a theory for the consideration of 21st century learning spaces in relation to the learner, desired knowledge and understanding, digital technology and digital pedagogy. New and emerging learning spaces at Bounty Boulevard State School are analysed and critiqued through an analysis of the guiding principles offered by the 'Learning in an Online World: Learning Spaces Framework' (MCEETYA, 2008) publication, including flexibility, inclusivity, collaboration, creativity and efficiency. The argument put forward in this article is that 21st century learning spaces can be enabled while acknowledging barriers of resourcing and current ICT infrastructure.

Researching professionals and online social networking : toward a professional doctorate

The professional doctorate is a degree that is specifically designed for professionals investigating real world problems and relevant issues for a profession, industry and/ or the community. The study on which this paper is based sought to track the scholarly skill development of a cohort of professional doctoral students who commenced their course in January 2008 at an Australian University. Via an initial survey and two focus groups held six months apart, the study aimed to determine if there had been any qualitative shifts in students’ understandings, expectations and perceptions regarding this developing knowledge and skills. Three key findings has emerged from this study were: (i) the appropriateness of using a blended learning approach for this doctoral cohort; (ii) the challenges of using wikis as an online technology of creating communities of practice: and (iii) that the transition from student to scholar is a process that is unlikely to be achieved in a short time frame.

Measurement of affect during simulation-based training

Training designed to support and strengthen higher-order mental abilities now often involves immersion in Virtual Reality where dangerous real world scenarios can be safely replicated. However despite the growing popularity of advanced training simulations, methods for evaluating their use rely heavily on subjective measures or analysis of final outcomes. Without dynamic, objective performance measures the outcome of training in terms of impact on cognitive skills and ability to transfer newly acquired skills to the real world is unknown. The relationship between affective intensity and cognitive learning provides a potential new approach to ensure the processing of cognitions which occur prior to final outcomes, such as problem-solving and decision-making, are adequately evaluated. This paper describes the technical aspects of pilot work recently undertaken to develop a new measurement tool designed to objectively track individual affect levels during simulation-based training.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Experiences of the FIRST LEGO League in China and Australia

FIRST (For Inspiration and Recognition of Science and Technology) was initiated in the U.S. by accomplished inventor Dean Kamen in 1989. FIRST LEGO League (FLL) is one of the five competitions conducted by this organization. Dean’s vision was “to create a world where science and technology are celebrated……where young people dream of becoming science and technology heroes”. Each year FLL creates opportunities for young people aged 9-16 to engage in problem solving, teamwork and collaborative learning around a real-world theme. In the 2009/2010 season, more than 145,000 young people in over 50 countries participated in this competition. As they tackle the challenges; they construct and de-construct their own knowledge through hands-on engagement in a constructivist learning environment. The challenges are presented at least eight weeks before the competition. In most events the participants are judged in four categories - robot game, robot design, team project and team challenge. “Gracious professionalism” is an essential element of the competition. This paper compares and contrasts the FLL in China and Australia and presents some of the achievements of the event. It also highlights some of the models which have been adopted in the two countries to facilitate participation. The educational benefits of embedding the FLL will also be discussed.

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.

Mooting and technology : to what extent does using technology improve the mooting experience for students?

It is generally acknowledged that mooting is an effective way to enhance the teaching of practical skills in legal education as well as to provide an authentic learning experience with links to the real world. However, there are a number of impediments to students participating in mooting; in particular being located off-campus, inexperience and lack of time. It has been suggested that technology may be a means of overcoming these impediments. However the use of technology in mooting has not been tested. This paper will report on a trial of the use of Second Life and Elluminate and videoconferencing as platforms for the conduct of moots. The trials identified limitations in the use of technology for mooting in particularly in relation to the development of advocacy skills. The paper will conclude that these limitations can be overcome by careful consideration of the appropriate technology to be used depending on the context and the objectives to be achieved by the moot. It will also suggest that in order to provide an authentic use of online communication technology in a court setting, the best available technology should be used for the conduct of moot competitions.

Students' perceptions of the relevance of mathematics in engineering

In this paper, we report on the findings of an exploratory study into the experience of students as they learn first year engineering mathematics. Here we define engineering as the application of mathematics and sciences to the building and design of projects for the use of society (Kirschenman and Brenner 2010)d. Qualitative and quantitative data on students' views of the relevance of their mathematics study to their engineering studies and future careers in engineering was collected. The students described using a range of mathematics techniques (mathematics skills developed, mathematics concepts applied to engineering and skills developed relevant for engineering) for various usages (as a subject of study, a tool for other subjects or a tool for real world problems). We found a number of themes relating to the design of mathematics engineering curriculum emerged from the data. These included the relevance of mathematics within different engineering majors, the relevance of mathematics to future studies, the relevance of learning mathematical rigour, and the effectiveness of problem solving tasks in conveying the relevance of mathematics more effectively than other forms of assessment. We make recommendations for the design of engineering mathematics curriculum based on our findings.