Case Study 4: Education Doctoral Student - "An Exploratory Study of Online Social Networking within a Doctorate of Education Program"

Introduction The professional doctorate is specifically designed for professionals investigating real-world problems and relevant issues for a profession, industry, and/or the community. The focus is scholarly research into professional practices. The research programme bridges academia and the professions, and offers doctoral candidates the opportunity to investigate issues relevant to their own practices and to apply these understandings to their professional contexts. The study on which this article is based sought to track the scholarly skill development of a cohort of professional doctoral students who commenced the course in January 2008 at an Australian university. Because they hold positions of responsibility and are time-poor, many doctoral students have difficulty transitioning from professional practitioner to researcher and scholar. The struggle many experience is in the development of a theoretical or conceptual standpoint for argumentation (Lesham, 2007; Weese et al., 1999). It was thought that the use of a scaffolded learning environment that drew upon a blended learning approach incorporating face to face intensive blocks and collaborative knowledge-building tools such as wikis would provide a data source for understanding the development of scholarly skills. Wikis, weblogs and similar social networking software have the potential to support communities to share, learn, create and collaborate. The development of a wiki page by each candidate in the 2008 cohort was encouraged to provide the participants and the teaching team members with textual indicators of progress. Learning tasks were scaffolded with the expectation that the candidates would complete these tasks via the wikis. The expectation was that cohort members would comment on each other’s work, together with the supervisor and/or teaching team member who was allocated to each candidate. The supervisor is responsible for supervising the candidate’s work through to submission of the thesis for examination and the teaching team member provides support to both the supervisor and the candidate through to confirmation. This paper reports on the learning journey of a cohort of doctoral students during the first seven months of their professional doctoral programme to determine if there had been any qualitative shifts in understandings, expectations and perceptions regarding their developing knowledge and skills. The paper is grounded in the literature pertaining to doctoral studies and examines the structure of the professional doctoral programme. Following this is a discussion of the qualitative study that helped to unearth key themes regarding the participants’ learning journey.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

Why have a governing body?

In this chapter we aim to provide a 'pracademic' view on the reasons why we have boards and why they undertake certain activities. Our approach is based primarily on academic research, hopefully tempered with a real-world understanding of governance issues. We also rely on insights we have gleaned from our own research that primarily relies on observing boards in action.

Biologically inspired SLAM using Wi-Fi

Wi-Fi is a commonly available source of localization information in urban environments but is challenging to integrate into conventional mapping architectures. Current state of the art probabilistic Wi-Fi SLAM algorithms are limited by spatial resolution and an inability to remove the accumulation of rotational error, inherent limitations of the Wi-Fi architecture. In this paper we leverage the low quality sensory requirements and coarse metric properties of RatSLAM to localize using Wi-Fi fingerprints. To further improve performance, we present a novel sensor fusion technique that integrates camera and Wi-Fi to improve localization specificity, and use compass sensor data to remove orientation drift. We evaluate the algorithms in diverse real world indoor and outdoor environments, including an office floor, university campus and a visually aliased circular building loop. The algorithms produce topologically correct maps that are superior to those produced using only a single sensor modality.

SHM through flexible vibration sensing technologies and robust safety evaluation paradigm

This research has successfully developed a novel synthetic structural health monitoring system model that is cost-effective and flexible in sensing and data acquisition; and robust in the structural safety evaluation aspect for the purpose of long-term and frequent monitoring of large-scale civil infrastructure during their service lives. Not only did it establish a real-world structural monitoring test-bed right at the heart of QUT Gardens Point Campus but it can also facilitate reliable and prompt protection for any built infrastructure system as well as the user community involved.

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

Making decisions about antibiotic use in the Australian primary healthcare sector

Introduction Australia is contributing to the global problem of antimicrobial resistance with one of the highest rates of antibiotic use amongst OECD countries. Data from the Australian primary healthcare sector suggests unnecessary antibiotics were prescribed for conditions that will resolve without it. If left unchecked, this will result in more resistant micro-organisms, against which antibiotics will be useless. There is a lack of understanding about what is influencing decisions to use antibiotics – what factors influences general practitioners (GPs) to prescribe antibiotics, consumers to seek antibiotics, and pharmacists to fill old antibiotic prescriptions? It is also not clear how these individuals trade-off between the possible benefits that antibiotics may provide in the immediate/short term, against the longer term societal risk of antimicrobial resistance. Method This project will investigate (a) what factors drive decisions to use antibiotics for GPs, pharmacists and consumers, and (b) how these individuals discount the future. Factors will be gleaned from published literature and from a qualitative phase using semi-structured interviews, to inform the development of Discrete Choice Experiments (DCEs). Three DCEs will be constructed – one for each group of interest – to allow investigation of which factors are more important in influencing (a) GPs to prescribe antibiotics, (b) consumers to seek antibiotics, and (c) pharmacists to fill legally valid but old or repeat prescriptions of antibiotics. Regression analysis will be conducted to understand the relative importance of these factors. A Time Trade Off exercise will be developed to investigate how these individuals discount the future, and whether GPs and pharmacists display the same extent of discounting the future, as consumers. Expected Results Findings from the DCEs will provide an insight into which factors are more important in driving decision making in antibiotic use for GPs, pharmacists and consumers. Findings from the Time Trade Off exercise will show what individuals are willing to trade for preserving the miracle of antibiotics. Conclusion The emergence of antibiotic resistance is inevitable. This research will expand on what is currently known about influencing desired behaviour change in antibiotic use, in the fight against antibiotic resistance. Real World Implications Research findings will contribute to existing national programs to bring about a reduction in inappropriate use of antibiotic in Australia. Specifically, influencing (1) how key messages and public health campaigns are crafted to increase health literacy, and (2) clinical education and empowerment of GPs and pharmacists to play a more responsive role as stewards of antibiotic use in the community.

Differential effects of refractive blur on day and night-time driving performance

Purpose To investigate the effect of different levels of refractive blur on real-world driving performance measured under day and nighttime conditions. Methods Participants included 12 visually normal, young adults (mean age = 25.8 ± 5.2 years) who drove an instrumented research vehicle around a 4 km closed road circuit with three different levels of binocular spherical refractive blur (+0.50 diopter sphere [DS], +1.00 DS, +2.00 DS) compared with a baseline condition. The subjects wore optimal spherocylinder correction and the additional blur lenses were mounted in modified full-field goggles; the order of testing of the blur conditions was randomized. Driving performance was assessed in two different sessions under day and nighttime conditions and included measures of road signs recognized, hazard detection and avoidance, gap detection, lane-keeping, sign recognition distance, speed, and time to complete the course. Results Refractive blur and time of day had significant effects on driving performance (P < 0.05), where increasing blur and nighttime driving reduced performance on all driving tasks except gap judgment and lane keeping. There was also a significant interaction between blur and time of day (P < 0.05), such that the effects of blur were exacerbated under nighttime driving conditions; performance differences were evident even for +0.50 DS blur relative to baseline for some measures. Conclusions The effects of blur were greatest under nighttime conditions, even for levels of binocular refractive blur as low as +0.50 DS. These results emphasize the importance of accurate and up-to-date refractive correction of even low levels of refractive error when driving at night.

'Women on a Wiki’ : the role of interactive websites in reflective learning practices for undergraduate women’s health students

Aim Our pedagogical research addressed the following research questions: 1) Can shared ‘cyber spaces’, such as a ‘wiki’, be occupied by undergraduate women’s health students to improve their critical thinking skills? 2) What are the learning processes via which this occurs? 3) What are the implications of this assessment trial for achieving learning objectives and outcomes in future public health undergraduate courses? Methods The students contributed written, critical reflections (approximately 250 words) to the Wiki each week following the lecture. Students reflected on a range of topics including the portrayal of women in the media, femininity, gender inequality, child bearing and rearing, domestic violence, mental health, Indigenous women, older women, and LGBTIQ communities. Their entries were anonymous, but visible to their peers. Each wiki entry contained a ‘discussion tab’ wherein online conversations were initiated. We used a social constructivist approach to grounded theory to analyse the 480 entries posted over the semester. (http://pub336womenshealth.wikispaces.com/) Results The social constructivist approach initiated by Vygotsky (1978) and further developed by Jonasson (1994) was used to analyse the students’ contributions in relation to four key thematic outcomes including: 1) Complexities in representations across contexts; 2) Critical evaluation in real world scenarios; 3) Reflective practice based on experience, and; 4) Collaborative co-construction of knowledge. Both text and image/visual contributions are provided as examples within each of these learning processes. A theoretical model depicting the interactive learning processes that occurred via discussion of the textual and visual stimulus is presented.

A case study of utilising concept knowledge in a topic specific document collection

The use of ‘topic’ concepts has shown improved search performance, given a query, by bringing together relevant documents which use different terms to describe a higher level concept. In this paper, we propose a method for discovering and utilizing concepts in indexing and search for a domain specific document collection being utilized in industry. This approach differs from others in that we only collect focused concepts to build the concept space and that instead of turning a user’s query into a concept based query, we experiment with different techniques of combining the original query with a concept query. We apply the proposed approach to a real-world document collection and the results show that in this scenario the use of concept knowledge at index and search can improve the relevancy of results.

Experiential brand deployment : improving tourism brand evaluations

This article examines the use of an experiential branding process to help leisure resort businesses evaluate their brand. We integrate experiential marketing and the quality function development approach in combination to help understand the brand from the perspectives of both the consumer and firm, to help resort service businesses build their experience-oriented competitive brands. The value of this study is that it provides a real-world brand framework, especially those resorts with limited resources. Much is spoken about the influence of the brand and why it is important, but little is known about decisions related to developing a brand, especially for firms that have limited resources such as resort tourism operators. Tourism operators tend to be small-to-medium enterprises that do not necessarily have the capacity to do everything suggested. Therefore, we explore how firms assess the critical elements of their brand by using an integrated approach. For example, the study finds that, first, by using the quality function development method resorts can identify the most critical brand elements, and second, we identify the associated strengths of each brand element and confirm the identified resort’s critical brand elements for investment. Results show the potential strategies to create a more holistic set of experiences.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Using upper bounds on attainable discrimination to select discrete valued features

Selection of features that will permit accurate pattern classification is a difficult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. This paper extends the discrimination bound method so that both the maximum and average discrimination expected on unseen test data can be estimated. These estimation techniques are the basis of a backwards elimination algorithm that can be use to rank features in order of their discriminative power. Two problems are used to demonstrate this feature selection process: classification of the Mushroom Database, and a real-world, pregnancy related medical risk prediction task - assessment of risk of perinatal death.