Adapting an argumentation framework for online discourse analysis : a knowledge building approach

The use of online tools to support teaching and learning is now commonplace within educational institutions, with many of these institutions mandating or strongly encouraging the use of a blended learning approach to teaching and learning. Consequently, these institutions generally adopt a learning management system (LMS), with a fixed set of collaborative tools, in the belief that effective teaching and learning approaches will be used, to allow students to build knowledge. While some studies into the use of an LMS’s still identify continued didactic approaches to teaching and learning, the focus of this paper is on the ability of collaborative tools such as discussion forums, to build knowledge. In the context of science education, argumentation is touted as playing an important role in this process of knowledge building. However, there is limited research into argumentation in other domains using online discussion and a blended learning approach. This paper describes a study, using design research, which adapts a framework for argumentation that can be applied to other domains. In particular it will focus on an adapted social argumentation schema to identify argument in a discussion forum of N=16 participants in a secondary High School.

A simplified approach for calculating the moments of action for linear reaction-diffusion equations

The mean action time is the mean of a probability density function that can be interpreted as a critical time, which is a finite estimate of the time taken for the transient solution of a reaction-diffusion equation to effectively reach steady state. For high-variance distributions, the mean action time under-approximates the critical time since it neglects to account for the spread about the mean. We can improve our estimate of the critical time by calculating the higher moments of the probability density function, called the moments of action, which provide additional information regarding the spread about the mean. Existing methods for calculating the nth moment of action require the solution of n nonhomogeneous boundary value problems which can be difficult and tedious to solve exactly. Here we present a simplified approach using Laplace transforms which allows us to calculate the nth moment of action without solving this family of boundary value problems and also without solving for the transient solution of the underlying reaction-diffusion problem. We demonstrate the generality of our method by calculating exact expressions for the moments of action for three problems from the biophysics literature. While the first problem we consider can be solved using existing methods, the second problem, which is readily solved using our approach, is intractable using previous techniques. The third problem illustrates how the Laplace transform approach can be used to study coupled linear reaction-diffusion equations.

A business engineering approach to client relationship management in a knowledge based firm

This thesis examines Customer Relationship Management and how the capabilities of an organisation to innovate can be enhanced via its implementation in a Knowledge Based Firm. The research identifies current customer knowledge components within an organisation and identifies for future use, CRM components for implementation within a Knowledge Based Firm. Opinions from a panel of experts' are identified for best practice customer relationship strategy, the most important CRM processes and identification of customer knowledge components that will form the basis of implementing a successful CRM to gain a competitive advantage through enhancing the innovative capability for a Knowledge Based Firm.

A cross-discipline and collaborative approach to identifying the predictors of environmentally friendly and health protective behaviour

Background. As a society, our interaction with the environment is having a negative impact on human health. For example, an increase in car use for short trips, over walking or cycling, has contributed to an increase in obesity, diabetes and poor heart health and also contributes to pollution, which is associated with asthma and other respiratory diseases. In order to change the nature of that interaction, to be more positive and healthy, it is recommended that individuals adopt a range of environmentally friendly behaviours (such as walking for transport and reducing the use of plastics). Effective interventions aimed at increasing such behaviours will need to be evidence based and there is a need for the rapid communication of information from the point of research, into policy and practice. Further, a number of health disciplines, including psychology and public health, share a common mission to promote health and well-being. Therefore, the objective of this project is to take a cross-discipline and collaborative approach to reveal psychological mechanisms driving environmentally friendly behaviour. This objective is further divided into three broad aims, the first of which is to take a cross-discipline and collaborative approach to research. The second aim is to explore and identify the salient beliefs which most strongly predict environmentally friendly behaviour. The third aim is to build an augmented model to explain environmentally friendly behaviour. The thesis builds on the understanding that an interdisciplinary collaborative approach will facilitate the rapid transfer of knowledge to inform behaviour change interventions. Methods. The application of this approach involved two surveys which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in collaboration with an expert panel comprising academics, industry professionals and government representatives, a self-administered, Theory of Planned Behaviour (TPB) based, mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay Region (Queensland, Australia). This survey explored specific beliefs including attitudes, norms, perceived control, intention and behaviour, as well as environmental altruism and green identity, in relation to walking for transport and switching off lights when not in use. Following analysis of the mail survey data and based on feedback from participants and key stakeholders, an internet survey was employed (N=451) to explore two additional behaviours, switching off appliances at the wall when not in use, and shopping with reusable bags. This work is presented as a series of interrelated publications which address each of the research aims. Presentation of Findings. Chapter five of this thesis consists of a published paper which addresses the first aim of the research and outlines the collaborative and multidisciplinary approach employed in the mail survey. The paper argued that forging alliances with those who are in a position to immediately utilise the findings of research has the potential to improve the quality and timely communication of research. Illustrating this timely communication, Chapter six comprises a report presented to Moreton Bay Regional Council (MBRC). This report addresses aim's one and two. The report contains a summary of participation in a range of environmentally friendly behaviours and identifies the beliefs which most strongly predicted walking for transport and switching off lights (from the mail survey). These salient beliefs were then recommended as targets for interventions and included: participants believing that they might save money; that their neighbours also switch off lights; that it would be inconvenient to walk for transport and that their closest friend also walks for transport. Chapter seven also addresses the second aim and presents a published conference paper in which the salient beliefs predicting the four specified behaviours (from both surveys) are identified and potential applications for intervention are discussed. Again, a range of TPB based beliefs, including descriptive normative beliefs, were predictive of environmentally friendly behaviour. This paper was also provided to MBRC, along with recommendations for applying the findings. For example, as descriptive normative beliefs were consistently correlated with environmentally friendly behaviour, local councils could engage in marketing and interventions (workshops, letter box drops, internet promotions) which encourage parents and friends to model, rather than simply encourage, environmentally friendly behaviour. The final two papers, presented in Chapters eight and nine, addresses the third aim of the project. These papers each present two behaviours together to inform a TPB based theoretical model with which to predict environmentally friendly behaviour. A generalised model is presented, which is found to predict the four specific behaviours under investigation. The role of demographics was explored across each of the behaviour specific models. It was found that some behaviour's differ by age, gender, income or education. In particular, adjusted models predicted more of the variance in walking for transport amongst younger participants and females. Adjusted models predicted more variance in switching off lights amongst those with a bachelor degree or higher and predicted more variance in switching off appliances amongst those on a higher income. Adjusted models predicted more variance in shopping with reusable bags for males, people 40 years or older, those on a higher income and those with a bachelor degree or higher. However, model structure and general predictability was relatively consistent overall. The models provide a general theoretical framework from which to better understand the motives and predictors of environmentally friendly behaviour. Conclusion. This research has provided an example of the benefits of a collaborative interdisciplinary approach. It has identified a number of salient beliefs which can be targeted for social marketing campaigns and educational initiatives; and these findings, along with recommendations, have been passed on to a local council to be used as part of their ongoing community engagement programs. Finally, the research has informed a practical model, as well as behaviour specific models, for predicting sustainable living behaviours. Such models can highlight important core constructs from which targeted interventions can be designed. Therefore, this research represents an important step in undertaking collaborative approaches to improving population health through human-environment interactions.

Rating for sustainable subdivision neighbourhood design (RSSND) : case of Bangkok metropolitan region (BMR), Thailand an eco-efficiency modelling approach

Bangkok Metropolitan Region (BMR) is the centre for various major activities in Thailand including political, industry, agriculture, and commerce. Consequently, the BMR is the highest and most densely populated area in Thailand. Thus, the demand for houses in the BMR is also the largest, especially in subdivision developments. For these reasons, the subdivision development in the BMR has increased substantially in the past 20 years and generated large numbers of subdivision developments (AREA, 2009; Kridakorn Na Ayutthaya & Tochaiwat, 2010). However, this dramatic growth of subdivision development has caused several problems including unsustainable development, especially for subdivision neighbourhoods, in the BMR. There have been rating tools that encourage the sustainability of neighbourhood design in subdivision development, but they still have practical problems. Such rating tools do not cover the scale of the development entirely; and they concentrate more on the social and environmental conservation aspects, which have not been totally accepted by the developers (Boonprakub, 2011; Tongcumpou & Harvey, 1994). These factors strongly confirm the need for an appropriate rating tool for sustainable subdivision neighbourhood design in the BMR. To improve level of acceptance from all stakeholders in subdivision developments industry, the new rating tool should be developed based on an approach that unites the social, environmental, and economic approaches, such as eco-efficiency principle. Eco-efficiency is the sustainability indicator introduced by the World Business Council for Sustainable Development (WBCSD) since 1992. The eco-efficiency is defined as the ratio of the product or service value according to its environmental impact (Lehni & Pepper, 2000; Sorvari et al., 2009). Eco-efficiency indicator is concerned to the business, while simultaneously, is concerned with to social and the environment impact. This study aims to develop a new rating tool named "Rating for sustainable subdivision neighbourhood design (RSSND)". The RSSND methodology is developed by a combination of literature reviews, field surveys, the eco-efficiency model development, trial-and-error technique, and the tool validation process. All required data has been collected by the field surveys from July to November 2010. The ecoefficiency model is a combination of three different mathematical models; the neighbourhood property price (NPP) model, the neighbourhood development cost (NDC) model, and the neighbourhood occupancy cost (NOC) model which are attributable to the neighbourhood subdivision design. The NPP model is formulated by hedonic price model approach, while the NDC model and NOC model are formulated by the multiple regression analysis approach. The trial-and-error technique is adopted for simplifying the complex mathematic eco-efficiency model to a user-friendly rating tool format. Credibility of the RSSND has been validated by using both rated and non-rated of eight subdivisions. It is expected to meet the requirements of all stakeholders which support the social activities of the residents, maintain the environmental condition of the development and surrounding areas, and meet the economic requirements of the developers.

Validity and reliability of 3-minute nutrition screening (3-MinNS)performed by nurses on oncology and surgical patients

Background Nutrition screening is usually administered by nurses. However, most studies on nutrition screening tools have not used nurses to validate the tools. The 3-Minute Nutrition Screening (3-MinNS) assesses weight loss, dietary intake and muscle wastage, with the composite score of each used to determine risk of malnutrition. The aim of the study was to determine the validity and reliability of 3-MinNS administered by nurses, who are the intended assessors. Methods In this cross sectional study, three ward-based nurses screened 121 patients aged 21 years and over using 3-MinNS in three wards within 24 hours of admission. A dietitian then assessed the patients’ nutritional status using Subjective Global Assessment within 48 hours of admission, whilst blinded to the results of the screening. To assess the reliability of 3-MinNS, 37 patients screened by the first nurse were re-screened by a second nurse within 24 hours, who was blinded to the results of the first nurse. The sensitivity, specificity and best cutoff score for 3-MinNS were determined using the Receiver Operator Characteristics Curve. Results The best cutoff score to identify all patients at risk of malnutrition using 3-MinNS was three, with sensitivity of 89% and specificity of 88%. This cutoff point also identified all (100%) severely malnourished patients. There was strong correlation between 3-MinNS and SGA (r=0.78, p<0.001). The agreement between two nurses conducting the 3-MinNS tool was 78.3%. Conclusion 3-Minute Nutrition Screening is a valid and reliable tool for nurses to identify patients at risk of malnutrition.

The structured and practical approach in development of decision support system for consultant selection in public sector infrastructure project

Nowadays, most of the infrastructure development projects undertaken are complex in nature. Practically, public clients who do not have a good understanding of the design and management may suffer severe losses, especially for infrastructure projects. There is a need for luring the right consultant to secure client's investment in infrastructure developments. Throughout the project life cycle, consultants play vital role from the inception to completion stage of a project. A few studies in Malaysia show that infrastructure projects involving irrigation and drainage have experience problems such as poor workmanship, delay and cost overrun due to the consultant's inability or the client incompetence of recruiting consultants in time. This highlights the need of aided decision making and an efficient system to select the best consultant by using Decision Support System (DSS). On the other hand, recent trends reveal that most DSS in construction only concentrate on decision model development. These models are impractical and unused as they are complicated or difficult for laymen such as project managers to utilize. Thus, this research attempts to develop an efficient DSS for consultant selection namely consultDeSS. Driven by the motivation and research aims, this study deployed Design Science Research Methodology (DSRM) dominant with a combination of case studies at the Malaysian Department of Irrigation and Drainage (DID). Two real projects involving irrigation and drainage infrastructure were used to design, implement and evaluate the artefact. The 3-tier consultDeSS was revised after the evaluation and the design was significantly improved based on user feedback. By developing desirable tools that fit client's needs will enhance the productivity and minimize conflict within groups and organisations. The tool is more usable and efficient compared to previous studies in construction. Thus, this research has demonstrated a purposeful artefact with a practical and valid structured development approach that is applicable in a variety of problems in construction discipline.

Efficiency and productivity of Singapore's manufacturing sector 2001-2010 : an analysis using bootstrapped truncated approach

This paper seeks to explain the lagging productivity in Singapore’s manufacturing noted in the statements of the Economic Strategies Committee Report 2010. Two methods are employed: the Malmquist productivity to measure total factor productivity (TFP) change and Simar and Wilson’s (2007) bootstrapped truncated regression approach which first derives bias-corrected efficiency estimates before being regressed against explanatory variables to help quantify sources of inefficiencies. The findings reveal that growth in total factor productivity was attributed to efficiency change with no technical progress. Sources of efficiency were attributed to quality of worker and flexible work arrangements while the use of foreign workers lowered efficiency.

Integrated Lean Six Sigma approach for patient flow improvement in hospital emergency department

Because of increased competition between healthcare providers, higher customer expectations, stringent checks on insurance payments and new government regulations, it has become vital for healthcare organisations to enhance the quality of the care they provide, to increase efficiency, and to improve the cost effectiveness of their services. Consequently, a number of quality management concepts and tools are employed in the healthcare domain to achieve the most efficient ways of using time, manpower, space and other resources. Emergency departments are designed to provide a high-quality medical service with immediate availability of resources to those in need of emergency care. The challenge of maintaining a smooth flow of patients in emergency departments is a global problem. This study attempts to improve the patient flow in emergency departments by considering Lean techniques and Six Sigma methodology in a comprehensive conceptual framework. The proposed research will develop a systematic approach through integration of Lean techniques with Six Sigma methodology to improve patient flow in emergency departments. The results reported in this paper are based on a standard questionnaire survey of 350 patients in the Emergency Department of Aseer Central Hospital in Saudi Arabia. The results of the study led us to determine the most significant variables affecting patient satisfaction with patient flow, including waiting time during patient treatment in the emergency department; effectiveness of the system when dealing with the patient’s complaints; and the layout of the emergency department. The proposed model will be developed within a performance evaluation metric based on these critical variables, to be evaluated in future work within fuzzy logic for continuous quality improvement.

Modeling the viability of the free-ranging cheetah population in Namibia : an object-oriented Bayesian network approach

Conservation of free-ranging cheetah (Acinonyx jubatus) populations is multi faceted and needs to be addressed from an ecological, biological and management perspective. There is a wealth of published research, each focusing on a particular aspect of cheetah conservation. Identifying the most important factors, making sense of various (and sometimes contrasting) findings, and taking decisions when little or no empirical data is available, are everyday challenges facing conservationists. Bayesian networks (BN) provide a statistical modeling framework that enables analysis and integration of information addressing different aspects of conservation. There has been an increased interest in the use of BNs to model conservation issues, however the development of more sophisticated BNs, utilizing object-oriented (OO) features, is still at the frontier of ecological research. We describe an integrated, parallel modeling process followed during a BN modeling workshop held in Namibia to combine expert knowledge and data about free-ranging cheetahs. The aim of the workshop was to obtain a more comprehensive view of the current viability of the free-ranging cheetah population in Namibia, and to predict the effect different scenarios may have on the future viability of this free-ranging cheetah population. Furthermore, a complementary aim was to identify influential parameters of the model to more effectively target those parameters having the greatest impact on population viability. The BN was developed by aggregating diverse perspectives from local and independent scientists, agents from the national ministry, conservation agency members and local fieldworkers. This integrated BN approach facilitates OO modeling in a multi-expert context which lends itself to a series of integrated, yet independent, subnetworks describing different scientific and management components. We created three subnetworks in parallel: a biological, ecological and human factors network, which were then combined to create a complete representation of free-ranging cheetah population viability. Such OOBNs have widespread relevance to the effective and targeted conservation management of vulnerable and endangered species.

Beyond compliance : project on an integrated systems approach for pest risk management in South East Asia

The Beyond Compliance project, which began in July 2011 with funding from the Standards and Trade Development Facility for 2 years, aims to enhance competency and confidence in the South East Asian sub-region by applying a Systems Approach for pest risk management. The Systems Approach involves the use of integrated measures, at least two of which are independent, that cumulatively reduce the risk of introducing exotic pests through trade. Although useful in circumstances where single measures are inappropriate or unavailable, the Systems Approach is inherently more complicated than single-measure approaches, which may inhibit its uptake. The project methodology is to take prototype decision-support tools, such as Control Point-Bayesian Networks (CP-BN), developed in recent plant health initiatives in other regions, including the European PRATIQUE project, and to refine them within this sub-regional context. Case studies of high-priority potential agricultural trade will be conducted by National Plant Protection Organizations of participating South East Asian countries in trials of the tools, before further modifications. Longer term outcomes may include: more robust pest risk management in the region (for exports and imports); greater inclusion of stakeholders in development of pest risk management plans; increased confidence in trade negotiations; and new opportunities for trade.

A Bayesian network approach to modelling temporal behaviour of Lyngbya majuscula bloom initiation

Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.