Writing together, learning together : the value and effectiveness of a research writing group for doctoral students

The high level of scholarly writing required for a doctoral thesis is a challenge for many research students. However, formal academic writing training is not a core component of many doctoral programs. Informal writing groups for doctoral students may be one method of contributing to the improvement of scholarly writing. In this paper, we report on a writing group that was initiated by an experienced writer and higher degree research supervisor to support and improve her doctoral students’ writing capabilities. Over time, this group developed a workable model to suit their varying needs and circumstances. The model comprised group sessions, an email group, and individual writing. Here, we use a narrative approach to explore the effectiveness and value of our research writing group model in improving scholarly writing. The data consisted of doctoral students’ reflections to stimulus questions about their writing progress and experiences. The stimulus questions sought to probe individual concerns about their own writing, what they had learned in the research writing group, the benefits of the group, and the disadvantages and challenges to participation. These reflections were analysed using thematic analysis. Following this analysis, the supervisor provided her perspective on the key themes that emerged. Results revealed that, through the writing group, members learned technical elements (e.g., paragraph structure), non-technical elements (e.g., working within limited timeframes), conceptual elements (e.g., constructing a cohesive arguments), collaborative writing processes, and how to edit and respond to feedback. In addition to improved writing quality, other benefits were opportunities for shared writing experiences, peer support, and increased confidence and motivation. The writing group provides a unique social learning environment with opportunities for: professional dialogue about writing, peer learning and review, and developing a supportive peer network. Thus our research writing group has proved an effective avenue for building doctoral students’ capability in scholarly writing. The proposed model for a research writing group could be applicable to any context, regardless of the type and location of the university, university faculty, doctoral program structure, or number of postgraduate students. It could also be used within a group of students with diverse research abilities, needs, topics and methodologies. However, it requires a group facilitator with sufficient expertise in scholarly writing and experience in doctoral supervision who can both engage the group in planned writing activities and also capitalise on fruitful lines of discussion related to students’ concerns as they arise. The research writing group is not intended to replace traditional supervision processes nor existing training. However it has clear benefits for improving scholarly writing in doctoral research programs particularly in an era of rapidly increasing student load.

Urban network travel time estimation from stop-line loop detector data and signal controller data

This paper presents a model to estimate travel time using cumulative plots. Three different cases considered are i) case-Det, for only detector data; ii) case-DetSig, for detector data and signal controller data and iii) case-DetSigSFR: for detector data, signal controller data and saturation flow rate. The performance of the model for different detection intervals is evaluated. It is observed that detection interval is not critical if signal timings are available. Comparable accuracy can be obtained from larger detection interval with signal timings or from shorter detection interval without signal timings. The performance for case-DetSig and for case-DetSigSFR is consistent with accuracy generally more than 95% whereas, case-Det is highly sensitive to the signal phases in the detection interval and its performance is uncertain if detection interval is integral multiple of signal cycles.

Self-efficacy and social support as predictors of smoking after a quit attempt

Examined findings (e.g., A. J. Yates and J. Thain [see PA, Vol 73:28269]) that suggest that perceived social support for attempts to quit smoking is a determinant of self-efficacy (SE). 102 adults (aged 18–71 yrs) who participated in a trial of 4 smoking interventions were studied over a 10-mo follow-up period. The study attested to the validity of SE as a predictor of sustained success from an attempt to stop smoking. The tendency for SE theory to be more strongly supported in the longer term was highly consistent with the proposed mechanism for SE effects. The absence of a relationship with perceived social support might be an advantage for SE, since support was a poor predictor of outcomes during follow-up. Results suggest that perceived social influences had less utility than personal skills and SE in predicting sustained non-smoking outcomes.

Augmenting competitiveness of cities through tourism planning : the role of collaborative decision support systems

Many cities around the globe are now considering tourism facilities and their remarkable revenues in order to become competitive in the global economy. In many of these cities a great emphasis is given to the cultural tourism as it plays an important role in the establishment of creative and knowledge-base of cities. The literature points out the importance of local community support in cultural tourism. In such context, the use of new approach and technologies in tourism planning in order to increase the community participation and competitiveness of cities’ cultural assets gains a great significance. This paper advocates a new planning approach for tourism planning, particularly for cultural tourism, to increase the competitiveness of cities. As part of this new approach, the paper introduces the joined up planning approach integrated with a collaborative decision support system: ‘the community-oriented decision support system’. This collaborative planning support system is an effective and efficient tool for cultural tourism planning, which provides a platform for local communities’ participation in the development decision process.

Intelligent urban management systems : online decision support systems for ubiquitous cities

A successful urban management support system requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism. The chapter emphasizes the importance of integrated urban management to better tackle the climate change, and to achieve sustainable urban development and sound urban growth management. This chapter introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for ubiquitous cities. The chapter discusses the essential role of online collaborative decision making in urban and infrastructure planning, development and management, and advocates transparent, fully democratic and participatory mechanisms for an effective urban management system that is particularly suitable for ubiquitous cities. This chapter also sheds light on some of the unclear processes of urban management of ubiquitous cities and online collaborative decision making, and reveals the key benefits of integrated and participatory mechanisms in successfully constructing sustainable ubiquitous cities.

A decision support system for sustainable urban development : the integrated land use and transportation indexing model

The broad definition of sustainable development at the early stage of its introduction has caused confusion and hesitation among local authorities and planning professionals. The main difficulties are experience in employing loosely-defined principles of sustainable development in setting policies and goals. The question of how this theory/rhetoric-practice gap could be filled will be the theme of this study. One of the widely employed sustainability accounting approaches by governmental organisations, triple bottom line, and applicability of this approach to sustainable urban development policies will be examined. When incorporating triple bottom line considerations with the environmental impact assessment techniques, the framework of GIS-based decision support system that helps decision-makers in selecting policy option according to the economic, environmental and social impacts will be introduced. In order to embrace sustainable urban development policy considerations, the relationship between urban form, travel pattern and socio-economic attributes should be clarified. This clarification associated with other input decision support systems will picture the holistic state of the urban settings in terms of sustainability. In this study, grid-based indexing methodology will be employed to visualise the degree of compatibility of selected scenarios with the designated sustainable urban future. In addition, this tool will provide valuable knowledge about the spatial dimension of the sustainable development. It will also give fine details about the possible impacts of urban development proposals by employing disaggregated spatial data analysis (e.g. land-use, transportation, urban services, population density, pollution, etc.). The visualisation capacity of this tool will help decision makers and other stakeholders compare and select alternative of future urban developments.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Reflective learning through social network sites in design education

Reflective learning is vital for successful practice-led education such as animation, multimedia design and graphic design, and social network sites can accommodate various learning styles for effective reflective learning. In this paper, the researcher studies reflective learning through social network sites with two animation units. These units aim to provide students with an understanding of the tasks and workflows involved in the production of style sheets, character sheets and motion graphics for use in 3D productions for film and television and game design. In particular, an assessment in these units requires students to complete their online reflective journals throughout the semester. The reflective learning has been integrated within the unit design and students are encouraged to reflect weekly learning processes and outcomes. A survey evaluating for students’ learning experience was conducted, and its outcomes indicate that social network site based reflective learning will not be effective without considering students’ learning circumstances and designing peer-to-peer interactions.

The Evolution of the creative industries : creative clusters, creative citizens and social network markets 創意產業的演化 : 創意集群 ,創意公民, 社會網絡市場 [published in Chinese]

The creative industries are important because they are clustered at the point of attraction for a billion or more young people around the world. They're the drivers of demographic, economic and political change. They start from the individual talent of the creative artist and the individual desire and aspiration of the audience. These are the raw materials for innovation, change and emergent culture, scaled up to form new industries and coordinated into global markets based on social networks.

Intellectual disability and sexuality : attitudes of disability support staff and leisure industry employees

Background: The attitudes of support staff and others in the community towards the sexuality of individuals with an intellectual disability (ID) have the potential to influence opportunities for normalised life experiences in the area of sexuality. ----- Method: A sample of 169 disability support staff and 50 employees from leisure and services industries completed the Attitudes to Sexuality Questionnaires (Individuals with an Intellectual Disability [ASQ–ID], and Individuals from the General Population [ASQ–GP]). ----- Results: Support staff and leisure workers reported generally positive attitudes towards the sexuality of individuals with an ID, but men were seen as having less self-control than women. Support staff were more cautious in their views about parenting, and both groups considered a lower level of sexual freedom to be desirable for women with an ID compared to women who are developing typically. Conclusions Attitudes of both groups are generally quite positive in relation to ID and sexuality.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

The impact of institutional, instrumental and interpersonal underpinnings on network dynamics and outcomes

Networks form a key part of the infrastructure of contemporary governance arrangements and, as such, are likely to continue for some time. Networks can take many forms and be formed for many reasons. Some networks have been explicitly designed to generate a collective response to an issue; some arise from a top down perspective through mandate or coercion; while others rely more heavily on interpersonal relations and doing the right thing. In this paper, these three different perspectives are referred to as the “3I”s: Instrumental, Institutional or Interpersonal. It is proposed that these underlying motivations will affect the process dynamics within the different types of networks in different ways and therefore influence the type of outcomes achieved. This proposition is tested through a number of case studies. An understanding of these differences will lead to more effective design, management and clearer expectations of what can be achieved through networks.

Evolving interactions : agile design for networked media performance

Network Jamming systems provide real-time collaborative performance experiences for novice or inexperienced users. In this paper we will outline the interaction design considerations that have emerged during through evolutionary development cycles of the jam2jam Network Jamming software that employs generative techniques that require particular attention to the human computer relationship. In particular we describe the co-evolution of features and uses, explore the role of agile development methods in supporting this evolution, and show how the provision of a clear core capability can be matched with options for enhanced features support multi-levelled user experience and skill develop.